Wef global informationtechnology_report_2014

The Global Information
Technology Report 2014
Rewards and Risks of Big Data
Beñat Bilbao-Osorio, Soumitra Dutta, and Bruno Lanvin, Editors
Insight Report

Insight Report
The Global Information
Technology Report 2014
Rewards and Risks of Big Data
Beñat Bilbao-Osorio, World Economic Forum
Soumitra Dutta, Cornell University
Bruno Lanvin, INSEAD
Editors
© 2014 World Economic Forum

The Global Information Technology Report 2014 is
a special project within the framework of the World
Economic Forum’s Global Competitiveness and
Benchmarking Network and the Industry Partnership
Programme for Information and Communication
Technologies. It is the result of collaboration between
the World Economic Forum and INSEAD.
Professor Klaus Schwab
Executive Chairman
Espen Barth Eide
Managing Director, Centre for Global Strategies
EDITORS
Beñat Bilbao-Osorio, Associate Director and Senior
Economist, Global Competitiveness and Benchmarking
Network, World Economic Forum
Soumitra Dutta, Dean, Samuel Curtis Johnson Graduate
School of Management, Cornell University
Bruno Lanvin, Executive Director, European
Competitiveness Initiative, INSEAD
GLOBAL COMPETITIVENESS AND BENCHMARKING
NETWORK
Jennifer Blanke, Chief Economist, Head of the Global
Competitiveness and Benchmarking Network
Ciara Browne, Associate Director
Roberto Crotti, Quantitative Economist
Gemma Corrigan, Project Associate
Attilio di Batista, Junior Quantitative Economist
Gaëlle Dreyer, Project Associate
Margareta Drzeniek-Hanouz, Director, Senior Economist,
Head of Competitiveness Research
Thierry Geiger, Associate Director, Economist
Tania Gutknecht, Community Manager
Caroline Ko, Economist
Cecilia Serin, Senior Associate
INFORMATION AND COMMUNICATION TECHNOLOGY
INDUSTRIES TEAM
Alan Marcus, Senior Director, Head of Information and
Communication Technology Industries
Aurélie Corre, Team Coordinator, Telecommunication
Industry
Aurélien Goutorbe, Senior Content Manager,
Telecommunication Industry
Qin He, Associate Director, Telecommunication Industry
William Hoffman, Associate Director, Head of Personal
Data Initiative
Dimitri Kaskoutas, Senior Community Associate,
Telecommunication Industry
Danil Kerimi, Director, Government Affairs, Information
and Communication Technology Industries
Elena Kvochko, Manager, Information Technology
Industry
Derek O’Halloran, Associate Director, Head of
Information Technology Industry
Michele Petruzziello, Senior Partnership Development,
Global Leadership Fellow
Alexandra Shaw, Senior Community Associate,
Information Technology Industry
Bruce Weinelt, Director, Head of Telecommunication
Industry
World Economic Forum
Geneva
Copyright © 2014
by the World Economic Forum and INSEAD
All rights reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted,
in any form or by any means, electronic, mechanical,
photocopying, or otherwise without the prior permission of
the World Economic Forum.
ISBN-13: 978-92-95044-63-0
ISBN-10: 92-95044-63-0
This report is printed on paper suitable for recycling and
made from fully managed and sustainable forest sources.
Printed and bound in Switzerland by SRO-Kundig.
Visit The Global Information Technology Report page at
www.weforum.org/gitr
We thank Hope Steele for her excellent editing work and
Neil Weinberg for his superb graphic design and layout.
The terms country and nation as used in this report do
not in all cases refer to a territorial entity that is a state
as understood by international law and practice. The
terms cover well-defined, geographically self-contained
economic areas that may not be states but for which
statistical data are maintained on a separate and
independent basis.

The Global Information Technology Report 2014 | iii
Contents
Preface v
Jennifer Blanke and Alan Marcus (World Economic Forum)
Foreword vii
John Chambers (Cisco Systems)
Foreword ix
Cesare Mainardi (Booz & Company)
Executive Summary xi
Beñat Bilbao-Osorio (World Economic Forum), Soumitra Dutta
(Cornell University), and Bruno Lanvin (INSEAD)
The Networked Readiness Index Rankings xix
Part 1: The Current Networked 1
Readiness Landscape and Rewards
and Risks of Big Data
1.1 The Networked Readiness Index 2014: 3
Benchmarking ICT Uptake in a World
of Big Data
Beñat Bilbao-Osorio and Roberto Crotti (World Economic Forum),
Soumitra Dutta (Cornell University), and Bruno Lanvin (INSEAD)
1.2 The Internet of Everything: How 35
the Network Unleashes the Benefits
of Big Data
Robert Pepper and John Garrity (Cisco Systems)
1.3 Big Data Maturity: An Action Plan 43
for Policymakers and Executives
Bahjat El-Darwiche, Volkmar Koch, David Meer, Ramez T. Shehadi,
and Walid Tohme (Booz & Company)
1.4 Big Data: Balancing the Risks and 53
Rewards of Data-Driven Public Policy
Alex Pentland (MIT)
1.5 Managing the Risks and Rewards 61
of Big Data
Matt Quinn and Chris Taylor (TIBCO)
1.6 Rebalancing Socioeconomic 67
Asymmetry in a Data-Driven Economy
Peter Haynes (Atlantic Council) and M-H. Carolyn Nguyen (Microsoft)
1.7 Building Trust: The Role of 73
Regulation in Unlocking the Value
of Big Data
Scott Beardsley, Luís Enríquez, Ferry Grijpink, Sergio Sandoval,
Steven Spittaels, and Malin Strandell-Jansson (McKinsey &
Company)
1.8 From Big Data to Big Social 81
and Economic Opportunities: Which
Policies Will Lead to Leveraging Data-
Driven Innovation’s Potential?
Pedro Less Andrade, Jess Hemerly, Gabriel Recalde, and
Patrick Ryan (Public Policy Division, Google, Inc.)
1.9 Making Big Data Something 87
More than the “Next Big Thing”
Anant Gupta (HCL Technologies)
Part 2: Country/Economy Profiles 95
How to Read the Country/Economy Profiles ..................................97
Index of Countries/Economies........................................................99
Country/Economy Profiles ............................................................100
Part 3: Data Tables 249
How to Read the Data Tables.......................................................251
Index of Data Tables.....................................................................253
Data Tables ..................................................................................255
Technical Notes and Sources 323
About the Authors 329
Partner Institutes 335
Acknowledgments 343

The Global Information Technology Report 2014 | v
The 13th edition of The Global Information Technology
Report is released at a time when economies need
to solidify the recovery of the past year and leave the
worst financial and economic crisis of the past 80
years behind. Developed economies need to sustain
their incipient economic recovery and find new areas
of growth and employment creation; emerging and
developing economies need to build their resilience
against turbulence in the markets and foster their
innovation potential in order to sustain the rapid
economic growth they experienced in the past decade.
Against this backdrop, information and
communication technologies (ICTs)—in their role as
key enablers of innovation and new employment
opportunities—are drawing more attention than ever
before. As the benefits of ICTs increasingly materialize
into tangible assets, building and strengthening digital
ecosystems becomes increasingly important.
The GITR series has been published by the World
Economic Forum in partnership with INSEAD since
2002. The Report has accompanied and monitored ICT
progress for more than a decade and raised awareness
of the importance of ICTs for long-term competitiveness
and well-being. Through the lens of the Networked
Readiness Index (NRI), the driving factors and impacts
of networked readiness and ICT leveraging have been
identified, highlighting the joint responsibility of all social
actors—individuals, businesses, and governments.
The Global Information Technology Report 2014
features the latest results of the NRI, offering an overview
of the current state of ICT readiness in the world. This
year’s coverage includes a record number of 148
economies, accounting for over 98 percent of global
GDP. In addition, it features a number of essays that
inquire into the rewards and risks accruing from big
data, an unprecedented phenomenon in terms of the
volume, velocity, and variety of sources of the creation
of new data. These essays also advise on the changes
that organizations, both public and private, will need to
adopt in order to manage, make sense of, and obtain
economic and social value from this vast quantity of
newly generated data. In addition, the Report presents
a wealth of data, including detailed profiles for each
economy covered and data tables with global rankings
for the NRI’s 54 indicators.
We would like to convey our sincere gratitude to
the industry and academic organizations’ experts who
contributed outstanding chapters. We also wish to thank
the editors of the Report—Soumitra Dutta at the Samuel
Curtis Johnson Graduate School of Management at
Cornell University, Bruno Lanvin at INSEAD, and Beñat
Bilbao-Osorio at the World Economic Forum—for
their leadership in this project, together with the other
members of the GITR team: Roberto Crotti, Danil Kerimi,
and Elena Kvochko. Appreciation also goes to members
of the Global Competitiveness and Benchmarking
Network team: Ciara Browne, Gemma Corrigan, Attilio
di Batista, Gaëlle Dreyer, Margareta Drzeniek-Hanouz,
Thierry Geiger, Tania Gutknecht, Caroline Ko, and Cecilia
Serin. Last but not least, we would like to express our
gratitude to our network of over 160 Partner Institutes
around the world and to all the business executives who
participated in our Executive Opinion Survey. Without
their valuable input, the production of this Report would
not be possible.
Preface
JENNIFER BLANKE and ALAN MARCUS
World Economic Forum

The Global Information Technology Report 2014 | vii
Foreword
JOHN CHAMBERS
Chairman and Chief Executive Officer, Cisco Systems
I never cease to be amazed by the speed of innovation.
Change is the only true constant, and each year the
pace of change only accelerates. Transitions that once
took place over three or five years now happen in 12 to
18 months.
I believe we are currently experiencing the biggest
fundamental change the world has seen since the initial
development of the Internet as people, processes,
data, and things become increasingly connected.
We call this the Internet of Everything (IoE), and it is
having a profound impact on individuals, businesses,
communities, and countries. According to analysis
conducted by Cisco, the Internet of Everything
represents a US$19 trillion global opportunity to create
value over the next decade through greater profits for
businesses as well as improved citizen services, cost
efficiencies, and increased revenues for governments
and other public-sector organizations.
Several major transitions in technology—each
important in its own right—are combining to make
the Internet of Everything possible. These include the
emergence of cloud and mobile computing, the growth
of big data and analytics, and the explosive development
of the Internet of Things (IoT). These transitions are
changing the role of information technology (IT), with
Internet protocol (IP) networks playing an increasingly
central part by seamlessly connecting disparate IT
environments. Cisco’s contribution to this edition of The
Global Information Technology Report focuses on the
how IP networks facilitate new information flows through
the interaction between two of these transitions: IoT and
data analytics.
The explosive expansion of IoT, or connections
between context-aware machines and other physical
objects, is changing how we utilize devices to improve
our daily lives. And the shift in data and analytics—
from being centralized, structured, and static to being
distributed, mixed structured and unstructured, and real-
time—is leading to a new era of real-time processing and
decision-making.
More industries are moving their systems and
processes to IP networks, and the rapid growth of IP-
connected devices is driving exponential increases in
data traffic. The migration to IP networks and the ability
to turn “big data” into valuable, actionable information
have demonstrable benefits—both economic and
social—as well as positive financial impacts for firms.
In our 30-year history, our success has been based
on our ability to see around corners, identify market
transitions, and make big bets on what is next—such as
the emergence of the Internet of Everything. We have
seen this before, in the transitions from bridged networks
to routed networks, shared networks to switched ones,
circuit switching to packet switching, fixed connectivity to
mobile connectivity, dedicated resources to virtual ones,
data traffic to voice and video traffic, PC connections to
any-device connections, and physical data centers to the
cloud.
We see the network as the critical accelerator
and enabler in all of these transitions, transforming
processes to increase efficiency and decrease costs. In
data centers, for example, the network is the common
element for intelligence, scale, and flexibility. Data
centers have evolved as more intelligence has been built
into the network—from networking virtual machines and
developing a platform optimizing computing to scaling
applications and decoupling them from the server or
data center in which they live.
The network also facilitates the growth of
applications, a key driver of the Internet of Everything.
Applications already provide an integral way that
consumers experience the Internet of Everything, with
the number of applications growing from 10 billion
downloads in 2010 to 77 billion by 2014.
As this trend continues, we expect the Internet of
Everything to drive massive gains in efficiency, business
growth, and quality of life, helped along by thousands
of new IoT applications. These applications will require
building new end-to-end IoT infrastructures, which will
enable the deployment of even more IoT applications.
We are pleased to collaborate again with the World
Economic Forum and INSEAD to produce The Global
Information Technology Report and the Networked
Readiness Index (NRI). The NRI provides policymakers,
business leaders, and concerned citizens with valuable
insights into current market conditions and the state
of connectivity across the world, and helps to identify
where more can be done to accelerate the Internet of
Everything’s positive impact on the world in which we
live.

The Global Information Technology Report 2014 | ix
Foreword
CESARE MAINARDI
Chief Executive Officer, Booz & Company
The report in your hands is a compilation of wisdom
about the relationship between digitization and corporate
strategy. This is a relationship of enormous promise,
because digitization—the mass adoption of connected
digital services by consumers, enterprises, and
governments—provides dramatic power and reach to the
companies that understand it.
But there is also enormous tension in the
relationship between digitization and strategy. Digital
media and technologies are inherently subject to change.
A company’s strategy, its way of winning in the market,
is most effective when it is tied to its identity. And
corporate identities—and the capabilities and cultures
that go with them—are by nature slow to change. It
takes years to develop the kind of proficiency that no
other company can easily master.
We have found in our research on capable
companies that the most consistently successful are
those that master this tension. They base their strategies
on their distinctive capabilities: the things they do
better than any other company. These are consistent
throughout their lifetimes. Apple competes on its unique
approach to design, which no other company can
match; IKEA on its unparalleled prowess in making and
selling low-priced but appealing home furnishings; and
Haier on its remarkable ability to translate its customers’
needs into innovative new appliances. These successful
companies then learn to adapt to new challenges and
opportunities within the context of their constant identity.
They become rapid innovators, able to shift to new
products, markets, and geographies, but they adapt only
when their existing capabilities can give them the right to
win.
For most companies, digitization is a great enabler.
But it is also a great disruptor. It enables competitors as
well, including competitors from other sectors who might
enter your business arena. This can present a threat that
can often turn a company’s advantage into a weakness.
Digitization is also distracting: it can present a
bewildering array of potential opportunities, all of which
look compelling. But pursuing them all is both financially
unfeasible and strategically distracting. It leads to
incoherence, which can lead an enterprise into decline.
How then do capable companies handle the
opportunities and challenges of digitization? They are
rigorously and prudently selective. They invest in those
areas that are in line with their chosen way to play in the
market—the distinctive way they create value for their
customers.
Consider, for example, the threats, distractions,
and benefits of one current element of digitization: big
data. This is comprised of large datasets often gathered
in unstructured forms from the behavior of people and
groups. For example, as individuals search online, shop,
express their opinions, communicate with each other,
and move from one place to another, their aggregate
behavior can be tracked and inferences drawn from it.
New technologies, such as analytic engines and cloud-
based storage, have made it possible to gather these
data in unprecedented amounts and interpret them in
novel ways. Insights from old forms of market research,
such as asking customers what they liked or did not
like in a commercial transaction, now become part of
a larger pattern of awareness, with both the scale and
granularity to give business people a much clearer view
of their market.
In capable companies, big data is aligned with their
strategies. They invest only in the data gathering that
gives them privileged access to the customers they care
about, or that contributes to the capabilities that make
them distinctive. Without that discipline, companies
can be overwhelmed by big data. They can collect a
huge volume of information without any predetermined
purpose, and then struggle to make sense of it. In short,
the prevalence of massive amounts of new information
has only highlighted the importance of the old strategic
verity: to change successfully, you must stay true to your
identity and seek the customers who you can serve well.
The next few years will see many companies
struggling to resolve the tension between change and
identity. Those who master the new digital technologies
in a disciplined way, in the service of their focused
strategies, will become more successful in the market—
and the economies around them will thrive accordingly.

Executive Summary
BEÑAT BILBAO-OSORIO, World Economic Forum
SOUMITRA DUTTA, Cornell University
BRUNO LANVIN, INSEAD
When The Global Information Technology Report (GITR)
and the Networked Readiness Index (NRI) were created
more than 13 years ago, the attention of decision
makers was focused on how to develop strategies that
would allow them to benefit from what Time Magazine
had described as “the new economy”: a new way of
organizing and managing economic activity based on
the new opportunities that the Internet provided for
businesses.1
At present, the world is slowly emerging
from one of the worst financial and economic crises
in decades, and policymakers, business leaders, and
civil society are looking into new opportunities that
can consolidate growth, generate new employment,
and create business opportunities. Information and
communication technologies (ICTs) continue to rank high
on the list as one of the key sources of new opportunities
to foster innovation and boost economic and social
prosperity, for both advanced and emerging economies.
For more than 13 years, the NRI has provided
decision makers with a useful conceptual framework
to evaluate the impact of ICTs at a global level and
to benchmark the ICT readiness and usage of their
economies.
EXTRACTING VALUE FROM BIG DATA
Data have always had strategic value, but with the
magnitude of data available today—and our capability to
process them—they have become a new form of asset
class. In a very real sense, data are now the equivalent
of oil or gold. And today we are seeing a data boom
rivaling the Texas oil boom of the 20th century and the
San Francisco gold rush of the 1800s. It has spawned
an entire support industry and has attracted a great deal
of business press in recent years.
This new asset class of big data is commonly
described by what we call the “three Vs.” Big data is
high volume, high velocity, and includes a high variety of
sources of information. Next to those traditional three Vs
we could add a fourth: value. This is what everyone is
looking for, and this is why big data today gets so much
attention. In the quest for value, the challenge facing us
is how to reduce the complexity and unwieldiness of big
data so that it becomes truly valuable.
Big data can take the form of structured data such
as financial transactions or unstructured data such as
photographs or blog posts. It can be crowd-sourced or
obtained from proprietary data sources. Big data has
been fueled by both technological advances (such as the
spread of radio-frequency identification, or RFID, chips)
and social trends (such as the widespread adoption of
social media). Our collective discussions, comments,
likes, dislikes, and networks of social connections are
now all data, and their scale is massive. What did we
search for? What did we read? Where did we go? With
whom do we associate? What do we eat? What do
we purchase? In short, almost any imaginable human
interaction can be captured and studied within the realm
of big data.
Big data has arrived. It is changing our lives and
changing the way we do business. But succeeding
with big data requires more than just data. Data-based
value creation requires the identification of patterns from
which predictions can be inferred and decisions made.
Businesses need to decide which data to use. The
data each business owns might be as different as the
businesses themselves; these data range from log files
and GPS data to customer- or machine-to-machine
data. Each business will need to select the data source
it will use to create value. Moreover, creating this
value will require the right way of dissecting and then
analyzing those data with the right analytics. It will
require knowing how to separate valuable information
from hype.
This world of big data has also become a source
of concern. The consequences of big data for issues
of privacy and other areas of society are not yet fully
understood. Some prominent critics, such as Jaron
Lanier,2
call on us to be cautious about readily believing
any result created by the “wisdom of the crowd.”
Moreover, applications of big data in military intelligence
have created a growing concern for privacy around the
world.
Indeed, we are now living in a world where anything
and everything can be measured. “Data” could become
a new ideology. We are just at the beginning of a long
journey where, with the proper principles and guidelines,
we should be able to collect, measure, and analyze more
and more information about everyone and everything
in order to make better decisions, individually and
collectively.
The Global Information Technology Report 2014 | xi

PART 1: THE CURRENT NETWORKED
READINESS LANDSCAPE
Part 1 of this Report presents the latest findings of
the NRI, offering a comprehensive assessment of the
present state of networked readiness in the world.
Furthermore, a number of expert contributions inquiring
into the role of big data and how to extract value from it
are also included. These contributions relate to (1) how
the network unleashes the benefits of big data; (2) how
and why policymakers and business executives need
to develop action plans to extract value from big data;
(3) balancing the risks and rewards of big data from a
public policy perspective; (4) managing these risks and
rewards; (5) rebalancing socioeconomic asymmetry in a
data-driven economy; (6) the role of regulation and trust
building in unlocking the value of big data; (7) turning the
potential of big data into socioeconomic results; and (8)
defining organizational change to take full advantage of
big data.
Insights from the NRI 2014 on the world’s
networked readiness
Chapter 1.1 provides an overview of the networked
readiness landscape of the world as assessed by the
NRI 2014. It presents the results of the top 10 performers
and selected countries by region, in the following order:
Europe and the Commonwealth of Independent States,
Asia and the Pacific, Latin America and the Caribbean,
sub-Saharan Africa, and the Middle East and North
Africa.
Tables 1 through 5 report the 2014 rankings for
the overall NRI, its four subindexes, and its 10 pillars. In
addition, the Country/Economy Profile and Data Tables
sections at the end of the Report present the detailed
results for the 148 economies covered by the study and
the 54 indicators composing the NRI. To complement
the analysis of the results, Box 1 presents a classification
of countries based on their NRI 2014 scores and the
change rate of this Index over a two-year period; Box
2 assesses the nature of the digital divide in Europe;
and Box 3 discusses the challenges large emerging
economies must overcome if they are to keep moving
forward in integrating ICTs into more robust innovation
ecosystems that could help them transition from what
appears to be a mid-life crisis toward a knowledge-
based society. Finally, Appendix A of Chapter 1.1 details
the structure of the NRI and describes the method of
calculation.
Top 10
The top 10 spots continue to be dominated by Northern
European economies, the Asian Tigers, and some of
the most advanced Western economies. Three Nordic
economies—Finland, Sweden, and Norway—lead the
rankings and are positioned among the top 5. Denmark
and Iceland, the remaining two Nordic economies,
also perform strongly, and despite small slips this
year they feature among the top 20. Overall, their
performance in terms of ICT readiness, with excellent
digital infrastructures and robust innovation systems,
allows them to score very highly both in ICT use—with
almost universal Internet use, for example—and in
innovation performances. The Asian Tigers—composed
of Singapore, Hong Kong SAR, the Republic of Korea,
and Taiwan (China)—also perform very strongly, all of
them positioned at the forefront of the NRI and with
Singapore, Hong Kong SAR, and Korea featuring among
the top 10. All these economies continue to boast
outstanding business and innovation environments that
are consistently ranked among the most conducive to
entrepreneurship in the world. Finally, the top 10 includes
some of the most advanced Western economies—the
Netherlands, Switzerland, the United States, and the
United Kingdom—that have recognized the potential of
ICTs to embark in a new economic and social revolution,
and thus have substantially invested in developing their
digital potential.
In evolutionary terms, this year the rankings remain
very stable, with no movement in the top 6 and negligible
changes in the rest, with the exception of the significant
improvement by six positions of Hong Kong SAR, which
climbs to 8th place.
For a second consecutive year, Finland tops
the rankings with a strong performance across the
board. It ranks 1st in the readiness subindex thanks
to an outstanding digital ICT infrastructure—the best
in the world—and 2nd in both the usage and impact
subindexes, with more than 90 percent of its population
using the Internet and high levels of technological and
non-technological innovation. The country also comes
in 3rd in the environment subindex, with a very robust
innovation system. Singapore continues to follow
closely in the rankings, remaining in 2nd place. With
the best pro-business and pro-innovation environment
worldwide, the city-state continues to obtain the top rank
in terms of ICT impacts, notably on the social dimension.
Supported by a government with a clear digital strategy
that offers the best online services in the world, an ICT
infrastructure that is relentlessly being improved over
time (16th), and one of the highest quality educational
systems in the world (3rd), notably in terms of math and
science (1st), Singapore has become one of the most
knowledge-intensive economies globally (2nd) and is an
ICT-generation powerhouse. Sweden (3rd) maintains
its position this year despite a slight improvement in its
overall score, unable to regain the top position it held
two editions ago. Overall, the very strong performance
of Sweden reflects its world-class, affordable (11th) ICT
infrastructure (3rd) and a stable and pro-business and
innovation environment (15th), despite its high tax rate
(123rd). These strengths result in outstanding uptake
and use of ICTs by individuals (1st), businesses (3rd), and
government (7th) and one of the highest technological
and non-technological innovation performances in the
xii | The Global Information Technology Report 2014
Executive Summary

world (2nd), making Sweden a truly knowledge-based
society.
The United States moves up two positions to
7th place, thanks to slight improvements in many
areas of the Index. These include the country’s already
good business and innovation environment (7th) and
improvements in its ICT infrastructure (4th), notably in
terms of wider access to international Internet bandwidth
per user. Overall, the country exhibits a robust uptake
of ICTs by all major stakeholders—businesses (9th),
government (11th), and individuals (18th)—who manage
to leverage well one of the best and more affordable
(20th) ICT infrastructures (4th). Coupled with a pro-
business and pro-innovation environment (7th), these
result in a strong innovation capacity (5th) and significant
ICT-related economic impacts (9th). The ranking of the
United States, the largest economy in the world, in the
top 10 shows that fully leveraging ICTs is not dependent
on small or medium-sized economies, but instead
depends on undertaking the right investments and
creating the right condition for it. Despite a drop of two
places, the United Kingdom continues to exhibit a very
strong performance in 9th position. As a service-based
economy, the country early recognized the importance
of ICTs to support its innovation and competitiveness
performance. As a result, it has managed to build a well-
developed ICT infrastructure (15th), exhibiting one of the
highest population uptakes (8th) and a well-developed
e-commerce (1st), which, coupled with a strong pro-
business environment, has resulted in solid economic
(14th) and social (9th) impacts.
Regional results
Europe has been at the forefront of developing a digital
ecosystem as a key ingredient that fosters innovation
and competitiveness. As a result, several European
countries lead the NRI rankings, with six European
economies—Finland, Sweden, the Netherlands,
Norway, Switzerland, and the United Kingdom—in the
top 10. In addition, in order to maximize the positive
impacts of ICTs throughout the European Union and
create synergies and positive spillover effects, the
European Commission has developed its Digital Agenda
as one of seven flagship initiatives under its growth
strategy Europe 2020. Despite these efforts, important
differences remain across European economies,
with Southern and Central and Eastern European
economies continuing to lag behind. A deeper analysis
of the root causes of these differences shows that, in
general, ICT infrastructure and individual uptake is more
homogeneous across EU Member States. However, less
favorable conditions for innovation and entrepreneurship
across European countries result in starker disparities in
terms of the economic impacts—for example, innovation
performance—accruing from their use, which illustrates
the changing nature of the digital divide in Europe and
in the rest of the world. The digital divide should not be
regarded only in terms of access to ICT infrastructure,
but also in terms of the impacts that using ICTs can
provide for the economy and society in general. Within
the Commonwealth of Independent States, several
countries improve their performances, reflecting the
key importance and hopes they have placed on ICTs to
diversify their economies and lead them toward more
knowledge-intensive activities.
With three economies from the region in the top
10 of the NRI rankings and several countries showing
improvement, Asia and the Pacific is very dynamic
and active in developing its ICT agenda. Yet a significant
digital divide persists between the most advanced
economies—such as the Asian Tigers and Japan—
and emerging economies and other trailing countries.
Regardless of their position on the development ladder,
however, all Asian economies have much to gain from
increased networked readiness. It will allow populations
of the least advanced among them to gain access to
much-needed basic services, to improve government
transparency and efficiency, and—for the most
advanced—it will contribute to boosting their innovation
capacity and allow them to attain higher levels of
competitiveness.
Improving the connectivity of Latin America and
the Caribbean continues to represent one of the
region’s main challenges despite the recent efforts
of many countries to develop and update their ICT
infrastructures. Countries such as Chile, Panama,
Uruguay, and Colombia have made significant progress
in developing and ensuring more and better access to
ICT infrastructure, ensuring higher ICT usage across
stakeholders. However, persistent weaknesses in
the broader innovation system hinder the overall
capacity of the region to fully leverage ICTs to foster its
competitiveness potential, highlighting the rise of the
new digital divide—that is, the divide between countries
that are achieving positive economic and social impacts
related to the use of ICTs and those that are not.
Sub-Saharan Africa slowly continues to develop
its ICT infrastructure, especially by expanding the share
of the population covered by, and having access to,
mobile telephony and by expanding the number of
Internet users, which in some countries—such as South
Africa—has almost doubled. These improvements have
led to many important innovations that provide more
and better services that were previously unavailable,
such as financial services. Notwithstanding this
progress, the region overall continues to suffer from a
relatively poor ICT infrastructure, which remains costly to
access, although some notable exceptions exist. More
importantly, severe weaknesses persist in the region’s
business and innovation ecosystems, which result in very
low positive economic and social impacts. Addressing
these weaknesses, not only by developing a more solid
ICT infrastructure but also by improving the framework
conditions for innovation and entrepreneurship, will be
The Global Information Technology Report 2014 | xiii
Executive Summary

crucial to avoid the emergence of a new digital divide
that will be evident in a disparity of the economic and
social impacts associated with what has been called the
digital revolution.
As in previous years, the Middle East and North
Africa depicts a highly diversified outlook in terms of
the capacity of countries to leverage ICTs to boost
competitiveness and well-being. On the one hand,
Israel and several Gulf Cooperation Council states
have continued their efforts to improve ICT uptake
and integrate ICTs better in more robust innovation
ecosystems in order to obtain higher returns. On the
other hand, many countries in North Africa continue
to lag behind and suffer from important weaknesses
in their framework conditions and overall innovation
capacity that prevent them from fully leveraging ICTs and
obtaining higher returns.
The Internet of Everything: How the Network
Unleashes the Benefits of Big Data
Chapter 1.2, contributed by Robert Pepper and John
Garrity from Cisco Systems, details how Internet protocol
(IP) networks underpin the concept of the Internet
of Everything (IoE) and explores how IP networks
accelerate big data’s transformational impact on
individuals, businesses, and governments around the
world.
As exabytes of new data are created daily, a rising
share of this data growth is flowing over IP networks
as more people, places, and things connect to the IoE.
Proprietary networks are increasingly migrating to IP,
facilitating the growth of big data, and networks are fast
becoming the key link among data generation, analysis,
processing, and utilization.
The authors highlight four major trends driving
data growth over IP networks and detail how networks
are central to maximizing analytical value from the
data deluge. The chapter identifies critical technology
and public policy challenges that could accelerate,
or encumber, the full impact of big data and the IoE
including standards and interoperability, privacy and
security, spectrum and bandwidth constraints, cross-
border data traffic, legacy regulatory models, reliability,
scaling, and electrical power.
Big Data Maturity: An Action Plan for Policymakers
and Executives
In Chapter 1.3, Bahjat El-Darwiche, Volkmar Koch,
David Meer, Ramez T. Shehadi, and Walid Tohme of
Booz & Company argue that big data has the potential
to improve or transform existing business operations
and reshape entire economic sectors. Big data can
pave the way for disruptive, entrepreneurial companies
and allow new industries to emerge. The technological
aspect is important, but technology alone is insufficient
to allow big data to show its full potential and to prevent
companies from feeling swamped by this information.
What matters is to reshape internal decision-making
culture so that executives base their judgments on data
rather than hunches. Research already indicates that
companies that have managed this are more likely to be
productive and profitable than their competition.
Organizations need to understand where they are in
terms of big data maturity, an approach that allows them
to assess progress and identify necessary initiatives.
Judging maturity requires looking at environment
readiness, determining how far governments have
provided the necessary legal and regulatory frameworks
and ICT infrastructure; considering an organization’s
internal capabilities and how ready it is to implement big
data initiatives; and looking also at the many and more
complicated methods for using big data, which can
mean simple efficiency gains or revamping a business
model. The ultimate maturity level involves transforming
the business model to become data-driven, which
requires significant investment over many years.
Policymakers should pay particular attention to
environment readiness. They should present citizens
with a compelling case for the benefits of big data.
This means addressing privacy concerns and seeking
to harmonize regulations around data privacy globally.
Policymakers should establish an environment that
facilitates the business viability of the big data sector
(such as data, service, or IT system providers), and
they should take educational measures to address the
shortage of big data specialists. As big data becomes
ubiquitous in public and private organizations, its use will
become a source of national and corporate competitive
advantage.
Balancing the Risks and Rewards of Data-Driven
Public Policy
Alex “Sandy” Pentland from the Massachusetts Institute
of Technology (MIT) highlights in Chapter 1.4 that we
are entering a big data world, where governance is
far more driven by data than it has been in the past.
Basic to the success of a data-driven society is the
protection of personal privacy and freedom. Discussions
at the World Economic Forum have made substantial
contributions to altering the privacy and data ownership
standards around the world in order to give individuals
unprecedented control over data that are about
them, while at the same time providing for increased
transparency and engagement in both the public and
private spheres.
We still face the challenge that large organizations,
and in particular governments, may be tempted to abuse
the power of the data that they hold. To address this
concern we need to establish best practices that are in
the interests of both large organizations and individuals.
This chapter suggests one path by which potential
abuses of power can be limited, while at the same time
providing greater security for organizations that use
xiv | The Global Information Technology Report 2014
Executive Summary

big data. The key policy recommendations for all large
organizations, commercial or government, are that:
1. Large data systems should store data in a
distributed manner, separated by type (e.g.,
financial vs. health) and real-world categories (e.g.,
individual vs. corporate), managed by a department
whose function is focused on those data, and
with sharing permissions set and monitored by
personnel from that department. Best practice
would have the custodians of data be regional
and use heterogeneous computer systems. With
such safeguards in place, it is difficult to attack
many different types of data at once, and it is more
difficult to combine data types without authentic
authorization.
2. Data sharing should always maintain provenance
and permissions associated with data and support
automatic, tamper-proof auditing. Best practice
would share only answers to questions about the
data (e.g., by use of pre-programmed SQL queries
known as “Database Views”) rather than the
data themselves, whenever possible. This allows
improved internal compliance and auditing, and
helps minimize the risk of unauthorized information
leakage.
3. Systems controlled by partner organizations, and
not just a company’s own systems, should be
secure. External data sharing should occur only
between data systems that have similar local
control, permissions, provenance, and auditing,
and should include the use of standardized legal
agreements such as those employed in trust
networks. Otherwise data can be siphoned off at
either the data source or the end consumer, without
the need for attacking central system directly.
4. The need for a secure data ecosystem extends to
the private data of individuals and the proprietary
data of partner companies. As a consequence, best
practice for data flows to and from individual citizens
and businesses is to require them to have secure
personal data stores and be enrolled in a trust
network data sharing agreement.
5. All entities should employ secure identity credentials
at all times. Best practice is to base these
credentials on biometric signatures.
6. Create an “open” data commons that is available
to partners under a lightweight legal agreement,
such as the trust network agreements. Open data
can generate great value by allowing third parties to
improve services.
Although these recommendations might at first
glance seem cumbersome, they are for the most part
easily implemented with the standard protocols found
within modern computer databases and networks. In
many cases, the use of distributed data stores and
management are already part of current practice, and
so the entire system will be simpler and cheaper to
implement than a centralized solution: all that is really
new is the careful use of provenance, permissions, and
auditing within a legal or regulatory framework such as a
trust network. Most importantly, these recommendations
will result in a data ecosystem that is more secure and
resilient, allowing us to safely reap the advantages of
using big data to help set and monitor public policy.
Managing the Risks and Rewards of Big Data
In Chapter 1.5., Matt Quinn and Chris Taylor from TIBCO
argue that expert handling of big data brings the reward
of being able to react to world-changing events, both
big and small, at an unprecedented rate and scope.
Epidemics can be tracked and miracle drugs developed,
for example, but at the same time, big data brings risks
that require balancing those benefits against privacy
concerns raised by the potentially unsettling correlation
of personal information.
Organizations are awakening to the reality that an
overwhelming amount of high-volume, wide-variety, and
high-velocity data creates three key trends:
• Big data leverages previously untapped data
sources to liberate information from places where it
was previously hidden.
• Big data management requires automation wherever
possible, because volume and complexity eliminate
the ability of humans to intervene and reprogram
processes in real time.
• Big data forces us to create adaptable, less
fragile data systems because the sheer variety of
structured and unstructured data breaks the old
computational and transactional ways of writing
logic.
These trends create two main challenges:
• Big data holds unseen patterns, which need to be
visualized using analytics tools and techniques.
Insights gained must be used at the right time, in
the right context, and with the right approach.
• The challenge of systematically discovering,
capturing, governing, and securing ever-larger
amounts of data is much more complicated than the
relatively simple problem of marshaling storage and
computational resources.
These elements are the driving forces behind
making use of big data in increasingly sophisticated
ways. The chapter cites examples in healthcare,
logistics, and retail where big data is being tackled
with a systems approach that takes into consideration
information streaming constantly as well as what is found
in historical databases that cut through the mystique of
The Global Information Technology Report 2014 | xv
Executive Summary

big data and get to the core of understanding big data’s
risks and rewards.
Rebalancing Socioeconomic Asymmetry in a
Data-Driven Economy
Chapter 1.6, contributed by Peter Haynes of the
Atlantic Council and M-H. Carolyn Nguyen at Microsoft,
explains that an increasing amount of data is being
generated by individuals who are handing potentially
valuable information to commercial enterprises in
exchange for “free” services. Moreover, they are doing
this without realizing—or being recompensed for—their
data’s monetary value, and with little or no control over
its immediate or future use. These socioeconomic
asymmetries in the broad data ecosystem are a potential
threat to the emerging data-driven economy, since
they may reduce overall output as more and more
economic activity is predicated on the use, exchange,
and analytics of data. The authors argue the need for a
data ecosystem based on fair value exchange and the
ability of users to control the use of data related to them.
The chapter also considers potential technology and
policy approaches by which this might be achieved, and
present the need for significant additional research and
new thinking, in both technology and policy, to enable a
sustainable data-driven economy.
Building Trust: The Role of Regulation in Unlocking
the Value of Big Data
In Chapter 1.7, Scott Beardsley, Luís Enríquez, Ferry
Grijpink, Sergio Sandoval, Steven Spittaels, and Malin
Strandell-Jansson from McKinsey & Company highlight
the expectation that big data will create great benefit
for society, companies, and individuals in the coming
years. For this to fully materialize, however, a number of
factors must be in place. There must be robust high-
speed Internet networks, an educated workforce, and
consumer trust in the services, especially regarding the
protection of personal data and privacy.
The increasing importance of protecting personal
data and privacy is being recognized by countries and
organizations across the world. There are, however, a
range of diverging views about how to tackle the issue.
These range from the light-touch approach of the United
States, which leaves the issue mainly to the industry
to solve, to the strict ex ante regulatory framework as
advocated by the European Union.
No matter which approach is taken, a few issues
remain unclear across the frameworks. These issues
might hamper public trust in big data applications and
companies and hinder the development of big data to
its full potential. The issues of concern include how to
define personal data, how to treat anonymous data,
whether to allow the right to be forgotten, and the need
to clarify the relevant jurisdictions and liabilities between
parties.
The chapter outlines a few suggestions for
regulators and companies about how to tackle these
issues, and suggests that regulators should work closely
with industry stakeholders and across regions to achieve
technology-neutral high-level regulatory principles that
last and promote industry self-regulation.
Companies should, after an initial assessment,
implement necessary changes into their organization and
cooperate with the regulators and industry stakeholders.
Key, however, is to empower the customer. With clear
and transparent privacy policies outlining practices,
enabled services, and trade-offs, consumers are
empowered to make their own choices while the trust of
the industry is preserved.
From Big Data to Big Social and Economic
Opportunities: Which Policies Will Lead to
Leveraging Data-Driven Innovation’s Potential?
Chapter 1.8, contributed by Pedro Less Andrade, Jess
Hemerly, Gabriel Recalde, and Patrick Ryan at Google,
focuses on the social and economic value of data,
but from the point of view of use and purpose rather
than volume. As it has become axiomatic that more
data are produced every year, commentators have
been driven to call this revolution the “age of big data.”
However, what is commonly known as “big data” is
not a new concept: the use of data to build successful
products and services, optimize business processes,
and make more efficient data-based decisions already
has an established history. Moreover, the term big data
is ambiguous: the main features of big data (quantity,
speed, variety) are technical properties that depend
not on the data themselves but on the evolution of
computing, storage, and processing technologies. What
is important about big data is not its volume but how it
may contribute to innovation and therefore be used to
create value. This is why this chapter uses data-driven
innovation to frame the discussion.
High-value solutions that may not have quantifiable
economic value are being developed using data, and
many sectors, from businesses to governments, benefit
from data-driven innovation. Apart from producing
and using data for better policymaking processes, the
public sector can also play its part in promoting and
fostering data-driven innovation and growth throughout
economies by (1) making public data accessible through
open data formats, (2) promoting balanced legislation,
and (3) supporting education that focuses on data
science skills.
Making Big Data Something More than the “Next Big
Thing”
In Chapter 1.9., Anant Gupta, Chief Executive Officer at
HCL Technologies Ltd, argues that big data analytics is
not a passing fad. It will be a central means of creating
value for the organization of tomorrow—almost literally,
tomorrow. It represents a major change in the way that
xvi | The Global Information Technology Report 2014
Executive Summary

businesses and other organizations will operate, and
using it successfully will require a new mind-set and
new capabilities. Given that, many organizations are
struggling to even know where to start in becoming big-
data competent. A step-by-step approach can make the
transition seem less daunting and minimize the stumbles
that are bound to occur along the way.
PARTS 2 AND 3: COUNTRY/ECONOMY PROFILES
AND DATA PRESENTATION
Parts 2 and 3 of the Report feature comprehensive
profiles for each of the 148 economies covered this
year as well as data tables for each of the 54 variables
composing the NRI, with global rankings. Each part
begins with a description of how to interpret the data
provided.
Technical notes and sources, included at the end
of Part 3, provide additional insight and information on
the definitions and sources of specific quantitative non-
Survey data variables included in the NRI computation
this year.
NOTES
1 Alexander 1983.
2 See Lanier 2010; see also Kakutani 2010.
REFERENCES
Alexander, C. P. 1983. “The New Economy.” Time Magazine, May
30. Available at http://content.time.com/time/magazine/
article/0,9171,926013,00.html.
Kakutani, M. 2010. “A Rebel in Cyberspace, Fighting Collectivism.”
Books of the Times, January 14. The New York Times. Available at
http://www.nytimes.com/2010/01/15/books/15book.html?_r=0.
Lanier, J. 2010. You Are Not a Gadget. New York: Vintage Books,
Random House.
The Global Information Technology Report 2014 | xvii
Executive Summary

The Networked
Readiness Index
Rankings

The Global Information Technology Report 2014 | xxi
Rank Country/Economy Value
2013 rank
(out of 144)
1 Finland 6.04 1
2 Singapore 5.97 2
3 Sweden 5.93 3
4 Netherlands 5.79 4
5 Norway 5.70 5
6 Switzerland 5.62 6
7 United States 5.61 9
8 Hong Kong SAR 5.60 14
9 United Kingdom 5.54 7
10 Korea, Rep. 5.54 11
11 Luxembourg 5.53 16
12 Germany 5.50 13
13 Denmark 5.50 8
14 Taiwan, China 5.47 10
15 Israel 5.42 15
16 Japan 5.41 21
17 Canada 5.41 12
18 Australia 5.40 18
19 Iceland 5.30 17
20 New Zealand 5.27 20
21 Estonia 5.27 22
22 Austria 5.26 19
23 Qatar 5.22 23
24 United Arab Emirates 5.20 25
25 France 5.09 26
26 Ireland 5.07 27
27 Belgium 5.06 24
28 Malta 4.96 28
29 Bahrain 4.86 29
30 Malaysia 4.83 30
31 Lithuania 4.78 32
32 Saudi Arabia 4.78 31
33 Portugal 4.73 33
34 Spain 4.69 38
35 Chile 4.61 34
36 Slovenia 4.60 37
37 Cyprus 4.60 35
38 Kazakhstan 4.58 43
39 Latvia 4.58 41
40 Oman 4.56 40
41 Puerto Rico 4.54 36
42 Czech Republic 4.49 42
43 Panama 4.36 46
44 Jordan 4.36 47
45 Brunei Darussalam 4.34 57
46 Croatia 4.34 51
47 Hungary 4.32 44
48 Mauritius 4.31 55
49 Azerbaijan 4.31 56
50 Russian Federation 4.30 54
51 Turkey 4.30 45
52 Montenegro 4.27 48
53 Costa Rica 4.25 53
54 Poland 4.24 49
55 Barbados 4.22 39
56 Uruguay 4.22 52
57 Macedonia, FYR 4.19 67
58 Italy 4.18 50
59 Slovak Republic 4.12 61
60 Georgia 4.09 65
61 Mongolia 4.07 59
62 China 4.05 58
63 Colombia 4.05 66
64 Indonesia 4.04 76
65 Armenia 4.03 82
66 Seychelles 4.02 79
67 Thailand 4.01 74
68 Bosnia and Herzegovina 3.99 78
69 Brazil 3.98 60
70 South Africa 3.98 70
71 Trinidad and Tobago 3.97 72
72 Kuwait 3.96 62
73 Bulgaria 3.96 71
74 Greece 3.95 64
Rank Country/Economy Value
2013 rank
(out of 144)
75 Romania 3.95 75
76 Sri Lanka 3.94 69
77 Moldova 3.89 77
78 Philippines 3.89 86
79 Mexico 3.89 63
80 Serbia 3.88 87
81 Ukraine 3.87 73
82 Ecuador 3.85 91
83 India 3.85 68
84 Vietnam 3.84 84
85 Rwanda 3.78 88
86 Jamaica 3.77 85
87 Tunisia 3.77 n/a
88 Guyana 3.77 100
89 Cape Verde 3.73 81
90 Peru 3.73 103
91 Egypt 3.71 80
92 Kenya 3.71 92
93 Dominican Republic 3.69 90
94 Bhutan 3.68 n/a
95 Albania 3.66 83
96 Ghana 3.65 95
97 Lebanon 3.64 94
98 El Salvador 3.63 93
99 Morocco 3.61 89
100 Argentina 3.53 99
101 Guatemala 3.52 102
102 Paraguay 3.47 104
103 Botswana 3.43 96
104 Iran, Islamic Rep. 3.42 101
105 Namibia 3.41 111
106 Venezuela 3.39 108
107 Gambia, The 3.38 98
108 Cambodia 3.36 106
109 Lao PDR 3.34 n/a
110 Zambia 3.34 115
111 Pakistan 3.33 105
112 Nigeria 3.31 113
113 Suriname 3.30 117
114 Senegal 3.30 107
115 Uganda 3.25 110
116 Honduras 3.24 109
117 Zimbabwe 3.24 116
118 Kyrgyz Republic 3.22 118
119 Bangladesh 3.21 114
120 Bolivia 3.21 119
121 Liberia 3.19 97
122 Côte d'Ivoire 3.14 120
123 Nepal 3.09 126
124 Nicaragua 3.08 125
125 Tanzania 3.04 127
126 Swaziland 3.00 136
127 Mali 3.00 122
128 Gabon 2.98 121
129 Algeria 2.98 131
130 Ethiopia 2.95 128
131 Cameroon 2.94 124
132 Malawi 2.90 129
133 Lesotho 2.88 138
134 Sierra Leone 2.85 143
135 Benin 2.82 123
136 Burkina Faso 2.78 130
137 Mozambique 2.77 133
138 Libya 2.75 132
139 Madagascar 2.74 137
140 Yemen 2.73 139
141 Timor-Leste 2.69 134
142 Mauritania 2.61 135
143 Haiti 2.52 141
144 Angola 2.52 n/a
145 Guinea 2.48 140
146 Myanmar 2.35 n/a
147 Burundi 2.31 144
148 Chad 2.22 142
The Networked Readiness Index 2014

Part 1
The Current Networked
Readiness Landscape
and Rewards and Risks
of Big Data

The Global Information Technology Report 2014 | 3
CHAPTER 1.1
The Networked Readiness
Index 2014: Benchmarking
ICT Uptake in a World of
Big Data
BEÑAT BILBAO-OSORIO, World Economic Forum
ROBERTO CROTTI, World Economic Forum
SOUMITRA DUTTA, Cornell University
BRUNO LANVIN, INSEAD
When The Global Information Technology Report (GITR)
and the Networked Readiness Index (NRI) were created
more than 13 years ago, the attention of decision
makers was focused on how to develop strategies that
would allow them to benefit from what Time Magazine
had described as “the new economy”: a new way of
organizing and managing economic activity based on
the new opportunities that the Internet provided for
businesses.1
At present, the world is slowly emerging
from one of the worst financial and economic crises
in decades, and policymakers, business leaders, and
civil society are looking into new opportunities that
can consolidate growth, generate new employment,
and create business opportunities. Information and
communication technologies (ICTs) continue to rank
high on the list as one of the key sources of new
opportunities to foster innovation and boost economic
and social prosperity, for both advanced and emerging
economies.
For more than 13 years, the NRI has provided
decision makers with a useful conceptual framework
to evaluate the impact of ICTs at a global level and
to benchmark the ICT readiness and usage of their
economies.
EXTRACTING VALUE FROM BIG DATA
Data have always had strategic value, but with the
magnitude of data available today—and our capability to
process them—they have become a new form of asset
class. In a very real sense, data are now the equivalent
of oil or gold. And today we are seeing a data boom
rivaling the Texas oil boom of the 20th century and the
San Francisco gold rush of the 1800s. It has spawned
an entire support industry and has attracted a great deal
of business press in recent years.
As explained in more detail in Chapter 1.3, this
new asset class of big data is commonly described by
what we call the “three Vs.” Big data is high volume,
high velocity, and includes a high variety of sources
of information. Next to those three Vs we could add a
fourth: value. This is what everyone is looking for, and
this is why big data today gets so much attention. In the
quest for value, the challenge facing us is how to reduce
the complexity and unwieldiness of big data so that it
becomes truly valuable.
Big data can take the form of structured data such
as financial transactions or unstructured data such as
photographs or blog posts. It can be crowd-sourced or
obtained from proprietary data sources. Big data has
been fueled by both technological advances (such as the
spread of radio-frequency identification, or RFID, chips)
and social trends (such as the widespread adoption of
social media). Our collective discussions, comments,
likes, dislikes, and networks of social connections are
now all data, and their scale is massive. What did we
search for? What did we read? Where did we go? With
whom do we associate? What do we eat? What do

1.1: The Networked Readiness Index 2014
4 | The Global Information Technology Report 2014
we purchase? In short, almost any imaginable human
interaction can be captured and studied within the realm
of big data.
Big data has arrived. It is changing our lives and
changing the way we do business. Some examples
include the following:
• Google uses big data to predict the next wave of
influenza.2
• IBM uses data to optimize traffic flow in the city of
Stockholm,3
and to get the best possible air quality.
• Dr. Jeffrey Brenner, a physician in New Jersey, uses
medical billing data to map out hot spots where
you can find his city’s most complex and costly
healthcare cases as part of a program to lower
healthcare costs.4
• The National Center for Academic Transformation
is using data mining to help understand which
college students are more likely to succeed in which
courses.5
But succeeding with big data requires more than
just data. Data-based value creation requires the
identification of patterns from which predictions can be
inferred and decisions made. Businesses need to decide
which data to use. The data each business owns might
be as different as the businesses themselves; these
data range from log files and GPS data to customer- or
machine-to-machine data. Each business will need
to select the data source it will use to create value.
Moreover, creating this value will require the right way
of dissecting and then analyzing those data with the
right analytics. It will require knowing how to separate
valuable information from hype. Chapter 1.7 provides
guidelines for businesses to make this transition. To a
large extent, mastering big data can also be compared
to irrigation. It is not enough to “bring water” to where it
can create fertility and value. Flooding can destroy crops
and even drive precious nutrients away. Mastering water
resources requires the delicate management of how
much is needed and when, and often requires complex
and interconnected systems of channels, levees, and
regulation. Success with these resources is what made
ancient Egypt a brilliant civilization and turned China into
a unified country. The stakes are not dissimilar when
applied to big data, but this is a resource that could
benefit the entire planet instead of just one country.
For many, “data-driven” has become the new
management philosophy. The Economist Intelligence
Unit released survey data showing that approximately
two-thirds of executives feel that big data will help find
new market opportunities and make better decisions.6
Nearly half of the surveyed respondents feel big data will
increase competitiveness, and more than a third believe
it will boost financial performance.
This world of big data has also become a source
of concern. The consequences of big data for issues
of privacy and other areas of society are not yet fully
understood. Some prominent critics, such as Jaron
Lanier,7
call on us to be cautious about readily believing
any result created by the “wisdom of the crowd.”
Infrastructure
Affordability Skills
Individual
Environment
DRIVERS
Business Government
IMPACTS
Economic
Social
Readiness Usage
Figure 1: The Networked Readiness Index framework

Moreover, applications of big data in military intelligence
have created a growing concern for privacy around the
world.
Indeed, we are now living in a world where anything
and everything can be measured. “Data” could become
a new ideology. We are just at the beginning of a long
journey where, with the proper principles and guidelines,
we should be able to collect, measure, and analyze more
and more information about everyone and everything
in order to make better decisions, individually and
collectively.
THE NETWORKED READINESS FRAMEWORK: A
HOLISTIC APPROACH TO MEASURE ICT ACCESS
AND IMPACTS
Because of the potential high returns that ICTs can
provide in transforming a nation’s economy and
increasing its citizens’ well-being, assessing ICT
developments has been the object of much academic
and policy attention over the past decade. Several
organizations have exerted significant effort toward
measuring and benchmarking ICT deployment and
uptake, but few have tried as hard to assess the returns
that ICTs can actually provide to both the economy and
society. Although data are still scarce in terms of ICT
impacts, policy interest in measuring ICTs has shifted
from measuring ICT access to measuring ICT impacts.
In 2012, after two years of research and
consultations with ICT practitioners, policy and industry
experts, and academia, the Networked Readiness Index
(NRI) introduced a new subindex on ICT impacts that
aimed at holistically assessing the way that countries
go about leveraging ICTs and benefiting from them in
terms of enhanced competitiveness and well-being.
This evolution ensures that the NRI framework remains
at the forefront of ICT measurement. As one of the
most authoritative assessments of its kind, it has been
adopted by several governments as a valuable tool for
informing their competitiveness and policy agendas.
The design of the framework for the calculation of
the NRI (Figure 1) has been guided by five principles:
1. Measuring the economic and social impacts of
ICTs is crucial. The NRI must include aspects of
the way ICTs are transforming both the economy
and society. In several economies, the ICT industry
has become increasingly important and now
accounts for a significant share of value-added and
employment. In addition, ICTs interact closely with
many other sectors, thus enabling innovations to
accrue and affecting productivity. Moreover, the
impacts of ICTs are also evident in the development
of new skills that are important in knowledge-
based, information-rich societies and that are crucial
for employment. In society, ICTs allow citizens to
participate more actively and steadily in social and
political debates and make the government more
accountable. They improve access to better and
faster services, which, in turn, yield significant
benefits.
2. An enabling environment determines the
capacity of an economy and society to benefit
from the use of ICTs. The success of a country
in leveraging ICTs and achieving the desired
economic and social benefits will depend on its
overall environment—including market conditions,
the regulatory framework, and innovation-
prone conditions—to boost innovation and
entrepreneurship.
3. ICT readiness and usage remain key drivers and
preconditions for obtaining any impacts. Despite
the increasing availability of ICTs, the question of
access and usage remains important especially for
developing countries, given their need to narrow
the digital divide. Even within developed nations,
the need to provide high-speed broadband to all
segments of the population has acquired greater
importance in recent years. Some features of the
NRI are related to access and usage; these cover
not only affordable ICT infrastructure but also digital
resources, including software and skills. Moreover,
ICT impacts can arise only if ICTs are widely used
by all key actors—individuals, businesses, and
governments. It is a society-wide effort. Those
actors demonstrating better preparedness and
greater interest are likely to use ICTs more and
more effectively, contributing to a greater impact on
competitiveness and development.
4. All factors interact and co-evolve within an ICT
ecosystem. Those societies that can count on
better-prepared actors and an enabling environment
are more likely to benefit from higher rates of ICT
use and more extensive impacts. At the same time,
those societies that benefit from higher rates of
ICT use and positive impacts will, in turn, be more
likely to benefit from a push on the part of the
different stakeholders to be better prepared and
keep improving the framework conditions that will
allow for more and stronger benefits to accrue. As a
result, a virtuous circle starts, where improvements
in one area affect and drive improvements in other
areas. Conversely, lags in one particular factor also
affect the evolution of the other factors.
5. The framework should provide clear policy
orientations and identify opportunities for
public-private collaboration. The NRI facilitates
the identification of areas where policy intervention—
through investment including public-private
partnerships, smart regulation, or the provision
of incentives—could boost the impacts of ICTs.
This is important because the development and
general uptake of ICTs depend on the capacity
of a country to provide an institutional framework

with reliable and efficient rules and regulations;
favorable business conditions for the founding and
growth of new (social and commercial) enterprises;
an innovation-prone environment, capable of
developing and absorbing new knowledge; and an
ICT-friendly government policy.
ELEMENTS OF THE NETWORKED READINESS
INDEX
The networked readiness framework translates into the
NRI, comprising four subindexes: these measure the
environment for ICTs; the readiness of a society to use
ICTs; the actual usage of all main stakeholders; and,
finally, the impacts that ICTs generate in the economy
and in society. The three first subindexes can be
regarded as the drivers that establish the conditions for
the results of the fourth subindex, ICT impacts. These
four subindexes are divided into 10 pillars composed of
54 individual indicators in total, according to the following
structure (see also Figure 2):
A. Environment subindex
1. Political and regulatory environment
2. Business and innovation environment
B. Readiness subindex
3. Infrastructure and digital content
4. Affordability
5. Skills
C. Usage subindex
6. Individual usage
7. Business usage
8. Government usage
D. Impact subindex
9. Economic impacts
10. Social impacts
The final NRI score is a simple average of the four
composing subindex scores, while each subindex’s
score is a simple average of those of the composing
pillars. In doing this, we assume that all NRI subindexes
make a similar contribution to networked readiness.
Appendix A includes detailed information on the
composition and computation of the NRI 2014, while we
briefly describe the different subindexes below.
Environment subindex
The environment subindex gauges the friendliness
of a country’s market and regulatory framework in
supporting high levels of ICT uptake and the emergence
of entrepreneurship and innovation-prone conditions. A
supportive environment is necessary to maximize the
potential impacts of ICTs in boosting competitiveness
and well-being. It includes a total of 18 variables
distributed into two pillars.
The political and regulatory environment pillar
(composed of nine variables) assesses the extent
to which the national legal framework facilitates ICT
penetration and the safe development of business
activities, taking into account general features of the
regulatory environment (including the protection afforded
to property rights, the independence of the judiciary, and
the efficiency of the law-making process) as well as more
ICT-specific dimensions (the passing of laws related to
ICTs and software piracy rates).
The business and innovation environment pillar
(nine variables) gauges the capacity of the business
framework’s conditions to boost entrepreneurship,
taking into account dimensions related to the ease of
doing business (including the presence of red tape
and excessive fiscal charges). This pillar also measures
the presence of conditions that allow innovation to
flourish by including variables on the overall availability
of technology, the demand conditions for innovative
products (as proxied by the development of government
procurement of advanced technology products), the
availability of venture capital for financing innovation-
related projects, and the presence of a skilled labor
force.
Readiness subindex
The readiness subindex, with a total of 12 variables,
measures the degree to which a society is prepared to
make good use of an affordable ICT infrastructure and
digital content.
The infrastructure and digital content pillar (five
variables) captures the development of ICT infrastructure
(including mobile network coverage, international Internet
bandwidth, secure Internet servers, and electricity
production) as well as the accessibility of digital content.
The affordability pillar (three variables) assesses
the cost of accessing ICTs, either via mobile telephony
or fixed broadband Internet, as well as the level of
competition in the Internet and telephony sectors that
determine this cost.
The skills pillar (four variables) gauges the ability
of a society to make effective use of ICTs thanks to
the existence of basic educational skills captured by
the quality of the educational system, the level of adult
literacy, and the rate of secondary education enrollment.
Usage subindex
The usage subindex assesses the individual efforts of
the main social agents—that is, individuals, business,
and government—to increase their capacity to use ICTs
as well as their actual use in their day-to-day activities
with other agents. It includes 16 variables.
The individual usage pillar (seven variables)
measures ICT penetration and diffusion at the individual
level, using indicators such as the number of mobile
phone subscriptions, individuals using the Internet,
households with a personal computer (PC), households
with Internet access, both fixed and mobile broadband
subscriptions, and the use of social networks.

The business usage pillar (six variables) captures the
extent of business Internet use as well as the efforts of
the firms in an economy to integrate ICTs into an internal,
technology-savvy, innovation-conducive environment that
generates productivity gains. Consequently, this pillar
measures the firm’s technology absorption capacity as
well as its overall capacity to innovate and the production
of technology novelties measured by the number of
Patent Cooperation Treaty (PCT) patent applications.
It also measures the extent of staff training available,
which indicates the extent to which management
and employees are more capable of identifying and
developing business innovations. As we did last year,
we split the e-commerce variable to distinguish the
business-to-business dimension from the business-to-
consumer one, because some noticeable differences
between the two dimensions exist in several countries.
The government usage pillar (three variables)
provides insights into the importance that governments
place on carrying out ICT policies for competitiveness
and to enhance the well-being of their citizens, the
effort they make to implement their visions for ICT
development, and the number of government services
they provide online.
Impact subindex
The impact subindex gauges the broad economic
and social impacts accruing from ICTs to boost
competitiveness and well-being and that reflect the
transformation toward an ICT- and technology-savvy
economy and society. It includes a total of eight
variables.
The economic impacts pillar (four variables)
measures the effect of ICTs on competitiveness thanks
to the generation of technological and non-technological
innovations in the shape of patents, new products
or processes, and novel organizational practices. In
addition, it also measures the overall shift of an economy
toward more knowledge-intensive activities.
The social impacts pillar (four variables) aims to
assess the ICT-driven improvements in well-being that
result from their impacts on the environment, education,
energy consumption, health progress, or more-active
civil participation. At the moment, because of data
limitations, this pillar focuses on measuring the extent
to which governments are becoming more efficient in
the use of ICTs and provide increased online services to
their citizens, and thus improving their e-participation.
It also assesses the extent to which ICTs are present in
education, as a proxy for the potential benefits that are
associated with the use of ICTs in education.
Business and innovation environment
Political and regulatory environment
Networked
Readiness Index
AffordabilityReadiness
Infrastructure and digital content
Skills
Usage Business usage
Individual usage
Government usage
Environment
Component subindexes Pillars
Social impacts
Economic impacts
Impacts
Figure 2: The Networked Readiness Index structure

In general, measuring the impacts of ICTs is
a complex task, and the development of rigorous
quantitative data to do so is still in its infancy. As a result,
many of the dimensions where ICTs are producing
important impacts—especially when these impacts are
not directly translated into commercial activities, as is
the case for the environment and for health—cannot yet
be covered. Therefore this subindex should be regarded
as a work in progress that will evolve to accommodate
new data on many of these dimensions as they become
available.
COMPUTATION METHODOLOGY AND DATA
In order to capture as comprehensively as possible all
relevant dimensions of societies’ networked readiness,
the NRI 2014 is composed of a mixture of quantitative
and survey data, as shown in Figure 3.
Of the 54 variables composing the NRI this year,
27—or 50 percent—are quantitative data, collected
primarily by international organizations such as
International Telecommunication Union (ITU), the World
Bank, and the United Nations. International sources
ensure the validation and comparability of data across
countries.
The remaining 27 variables capture aspects that
are more qualitative in nature or for which internationally
comparable quantitative data are not available for a large
enough number of countries, but that nonetheless are
crucial to fully measure national networked readiness.
These data come from the Executive Opinion Survey (the
Survey), which the Forum administers annually to over
15,000 business leaders in all economies included in
the Report.8
The Survey represents a unique source of
insight into many critical aspects related to the enabling
environment, such as the effectiveness of law-making
bodies and the intensity of local competition; into ICT
readiness, such as the quality of the educational system
and the accessibility of digital content; into ICT usage,
such as capacity to innovate and the importance of
government vision for ICTs; and into impacts, such as
the impact of ICTs on developing new products and
services and improving access to basic services.
The NRI’s coverage every year is determined
by the Survey coverage and data availability for
indicators obtained from other sources, mostly
international organizations. This year the Report
includes 148 economies, four more than the 2013
edition. The newly covered countries are Bhutan, Lao
PDR, and Myanmar. We have also re-instated Angola
and Tunisia into the Index, two countries that were not
included in last year’s edition. Tajikistan is not covered
in the 2014 Report because Survey data could not be
collected this year.
More details on variables included in the Index and
their computation can be found in Appendix A and in the
Technical Notes and Sources section at the end of the
Report.
THE CURRENT NETWORKED READINESS
LANDSCAPE: INSIGHTS FROM THE NRI 2014
This section provides an overview of the networked
readiness landscape of the world as assessed by the
Figure 3: Breakdown of indicators used in the Networked Readiness Index 2014 by data source
TOTAL: 54 INDICATORS
INDICATORS FROM
OTHER SOURCES
27 INDICATORS
(50%)
EXECUTIVE OPINION
SURVEY
27 INDICATORS
(50%)

NRI 2014. It presents the results of the top 10 performers
and selected countries by region, in the following order:
Europe and the Commonwealth of Independent States;
Asia and the Pacific; Latin America and the Caribbean,
sub-Saharan Africa; and the Middle East and North
Africa.
Tables 1 through 5 report the 2014 rankings for
the overall NRI, its four subindexes, and its 10 pillars. In
addition, the Country/Economy Profiles and Data Tables
sections at the end of the Report present the detailed
results for the 148 economies covered by the study and
the 54 indicators composing the NRI. To complement
the analysis of the results, Box 1 presents a classification
of countries based on their NRI 2014 scores and the
change rate of this Index over a two-year period; Box 2
assesses the nature of the digital divide in Europe;
and Box 3 discusses the challenges large emerging
economies must overcome if they are to keep moving
forward in integrating ICTs into more robust innovation
ecosystems that could help them transition from what
appears to be a mid-life crisis toward a knowledge-
based society. Figure 4 presents an intensity map of the
world; economies are color-coded based on their NRI
overall score measured on a 1-to-7 scale, with best- and
worst-performing economies appearing in dark green
and red, respectively. Finally, Appendix A of the present
chapter details the structure of the NRI and describes
the method of calculation.
TOP 10
As in previous years, the top 10 spots continue to be
dominated by Northern European economies, the
Asian Tigers, and some of the most advanced Western
economies. Three Nordic economies—Finland,
Sweden, and Norway—lead the rankings and are
positioned among the top 5. Denmark and Iceland,
the remaining two Nordic economies, also perform
strongly, and despite small slips this year they feature
among the top 20. Overall, their performance in terms of
ICT readiness, with excellent digital infrastructures and
robust innovation systems, allows them to score very
highly both in ICT use—with almost universal Internet
use, for example—and in innovation performances. The
Asian Tigers—composed of Singapore, Hong Kong
SAR, the Republic of Korea, and Taiwan (China)—also
perform very strongly, all of them positioned at the
forefront of the NRI and with Singapore, Hong Kong
SAR, and the Republic of Korea featuring among
the top 10. All these economies continue to boast
outstanding business and innovation environments that
are consistently ranked among the most conducive to
entrepreneurship in the world. Finally, the top 10 includes
some of the most advanced Western economies—the
Netherlands, Switzerland, the United States, and the
United Kingdom—that have recognized the potential of
ICTs to embark in a new economic and social revolution,
and thus have substantially invested in developing their
digital potential.
In dynamic terms, this year the rankings remain very
stable, with no movement in the top 6 and negligible
Figure 4: The Networked Readiness Index map
Value
n 5.4–7.0 (best)
n 5.0–5.4
n 4.0–5.0
n 3.3–4.0
n 1.0 (worst)–3.3
n Not covered

2013 rank
Rank Country/Economy Value (out of 144) Group*
1 Finland 6.04 1 ADV
2 Singapore 5.97 2 ADV
3 Sweden 5.93 3 ADV
4 Netherlands 5.79 4 ADV
5 Norway 5.70 5 ADV
6 Switzerland 5.62 6 ADV
7 United States 5.61 9 ADV
8 Hong Kong SAR 5.60 14 ADV
9 United Kingdom 5.54 7 ADV
10 Korea, Rep. 5.54 11 ADV
11 Luxembourg 5.53 16 ADV
12 Germany 5.50 13 ADV
13 Denmark 5.50 8 ADV
14 Taiwan, China 5.47 10 ADV
15 Israel 5.42 15 ADV
16 Japan 5.41 21 ADV
17 Canada 5.41 12 ADV
18 Australia 5.40 18 ADV
19 Iceland 5.30 17 ADV
20 New Zealand 5.27 20 ADV
21 Estonia 5.27 22 ADV
22 Austria 5.26 19 ADV
23 Qatar 5.22 23 MENA
24 United Arab Emirates 5.20 25 MENA
25 France 5.09 26 ADV
26 Ireland 5.07 27 ADV
27 Belgium 5.06 24 ADV
28 Malta 4.96 28 ADV
29 Bahrain 4.86 29 MENA
30 Malaysia 4.83 30 DEVASIA
31 Lithuania 4.78 32 CEE
32 Saudi Arabia 4.78 31 MENA
33 Portugal 4.73 33 ADV
34 Spain 4.69 38 ADV
35 Chile 4.61 34 LATAM
36 Slovenia 4.60 37 ADV
37 Cyprus 4.60 35 ADV
38 Kazakhstan 4.58 43 CIS
39 Latvia 4.58 41 CEE
40 Oman 4.56 40 MENA
41 Puerto Rico 4.54 36 ADV
42 Czech Republic 4.49 42 ADV
43 Panama 4.36 46 LATAM
44 Jordan 4.36 47 MENA
45 Brunei Darussalam 4.34 57 DEVASIA
46 Croatia 4.34 51 CEE
47 Hungary 4.32 44 CEE
48 Mauritius 4.31 55 SSA
49 Azerbaijan 4.31 56 CIS
50 Russian Federation 4.30 54 CIS
51 Turkey 4.30 45 CEE
52 Montenegro 4.27 48 CEE
53 Costa Rica 4.25 53 LATAM
54 Poland 4.24 49 CEE
55 Barbados 4.22 39 LATAM
56 Uruguay 4.22 52 LATAM
57 Macedonia, FYR 4.19 67 CEE
58 Italy 4.18 50 ADV
59 Slovak Republic 4.12 61 ADV
60 Georgia 4.09 65 CIS
61 Mongolia 4.07 59 CIS
62 China 4.05 58 DEVASIA
63 Colombia 4.05 66 LATAM
64 Indonesia 4.04 76 DEVASIA
65 Armenia 4.03 82 CIS
66 Seychelles 4.02 79 SSA
67 Thailand 4.01 74 DEVASIA
68 Bosnia and Herzegovina 3.99 78 CEE
69 Brazil 3.98 60 LATAM
70 South Africa 3.98 70 SSA
71 Trinidad and Tobago 3.97 72 LATAM
72 Kuwait 3.96 62 MENA
73 Bulgaria 3.96 71 CEE
74 Greece 3.95 64 ADV
2013 rank
Rank Country/Economy Value (out of 144) Group*
75 Romania 3.95 75 CEE
76 Sri Lanka 3.94 69 DEVASIA
77 Moldova 3.89 77 CIS
78 Philippines 3.89 86 DEVASIA
79 Mexico 3.89 63 LATAM
80 Serbia 3.88 87 CEE
81 Ukraine 3.87 73 CIS
82 Ecuador 3.85 91 LATAM
83 India 3.85 68 DEVASIA
84 Vietnam 3.84 84 DEVASIA
85 Rwanda 3.78 88 SSA
86 Jamaica 3.77 85 LATAM
87 Tunisia 3.77 n/a MENA
88 Guyana 3.77 100 LATAM
89 Cape Verde 3.73 81 SSA
90 Peru 3.73 103 LATAM
91 Egypt 3.71 80 MENA
92 Kenya 3.71 92 SSA
93 Dominican Republic 3.69 90 LATAM
94 Bhutan 3.68 n/a DEVASIA
95 Albania 3.66 83 CEE
96 Ghana 3.65 95 SSA
97 Lebanon 3.64 94 MENA
98 El Salvador 3.63 93 LATAM
99 Morocco 3.61 89 MENA
100 Argentina 3.53 99 LATAM
101 Guatemala 3.52 102 LATAM
102 Paraguay 3.47 104 LATAM
103 Botswana 3.43 96 SSA
104 Iran, Islamic Rep. 3.42 101 MENA
105 Namibia 3.41 111 SSA
106 Venezuela 3.39 108 LATAM
107 Gambia, The 3.38 98 SSA
108 Cambodia 3.36 106 DEVASIA
109 Lao PDR 3.34 n/a DEVASIA
110 Zambia 3.34 115 SSA
111 Pakistan 3.33 105 MENA
112 Nigeria 3.31 113 SSA
113 Suriname 3.30 117 LATAM
114 Senegal 3.30 107 SSA
115 Uganda 3.25 110 SSA
116 Honduras 3.24 109 LATAM
117 Zimbabwe 3.24 116 SSA
118 Kyrgyz Republic 3.22 118 CIS
119 Bangladesh 3.21 114 DEVASIA
120 Bolivia 3.21 119 LATAM
121 Liberia 3.19 97 SSA
122 Côte d'Ivoire 3.14 120 SSA
123 Nepal 3.09 126 DEVASIA
124 Nicaragua 3.08 125 LATAM
125 Tanzania 3.04 127 SSA
126 Swaziland 3.00 136 SSA
127 Mali 3.00 122 SSA
128 Gabon 2.98 121 SSA
129 Algeria 2.98 131 MENA
130 Ethiopia 2.95 128 SSA
131 Cameroon 2.94 124 SSA
132 Malawi 2.90 129 SSA
133 Lesotho 2.88 138 SSA
134 Sierra Leone 2.85 143 SSA
135 Benin 2.82 123 SSA
136 Burkina Faso 2.78 130 SSA
137 Mozambique 2.77 133 SSA
138 Libya 2.75 132 MENA
139 Madagascar 2.74 137 SSA
140 Yemen 2.73 139 MENA
141 Timor-Leste 2.69 134 DEVASIA
142 Mauritania 2.61 135 MENA
143 Haiti 2.52 141 LATAM
144 Angola 2.52 n/a SSA
145 Guinea 2.48 140 SSA
146 Myanmar 2.35 n/a DEVASIA
147 Burundi 2.31 144 SSA
148 Chad 2.22 142 SSA
Note: Group classification follows the International Monetary Fund’s classification (situation as of October 2013).
* Groups: ADV = Advanced economies; CIS = Commonwealth of Independent States and Mongolia; DEVASIA = Developing Asia; LATAM = Latin America and the Caribbean; MENA = Middle East
and North Africa; SSA = Sub-Saharan Africa.
Table 1: The Networked Readiness Index 2014

Table 2: Environment subindex and pillars
ENVIRONMENT SUBINDEX
Rank Country/Economy Score Rank Score Rank Score
1 Singapore 5.87 1 5.90 1 5.84
2 New Zealand 5.63 2 5.88 8 5.37
3 Finland 5.62 3 5.86 9 5.37
4 Hong Kong SAR 5.56 11 5.40 2 5.72
5 United Kingdom 5.50 5 5.66 10 5.33
6 Netherlands 5.48 8 5.53 5 5.44
7 Norway 5.46 7 5.54 6 5.38
8 Sweden 5.45 6 5.63 15 5.26
9 Switzerland 5.41 9 5.51 12 5.31
10 Canada 5.39 12 5.31 3 5.46
11 Luxembourg 5.31 4 5.73 29 4.90
12 Ireland 5.27 13 5.29 16 5.25
13 Qatar 5.23 14 5.23 17 5.23
14 Australia 5.20 15 5.23 21 5.17
15 United States 5.19 22 5.00 7 5.38
16 Denmark 5.19 19 5.16 18 5.21
17 Germany 5.14 10 5.41 31 4.87
18 United Arab Emirates 5.10 24 4.91 13 5.28
19 Belgium 5.08 21 5.01 22 5.14
20 Iceland 5.01 27 4.81 19 5.21
21 Japan 4.99 16 5.23 40 4.75
22 Israel 4.97 28 4.67 14 5.27
23 Austria 4.97 18 5.19 39 4.75
24 Malaysia 4.95 25 4.84 24 5.07
25 Taiwan, China 4.94 34 4.43 4 5.45
26 Estonia 4.88 26 4.83 28 4.93
27 Saudi Arabia 4.86 31 4.59 23 5.12
28 Rwanda 4.83 17 5.22 55 4.45
29 Chile 4.83 38 4.34 11 5.32
30 France 4.82 23 4.97 47 4.67
31 South Africa 4.76 20 5.05 53 4.48
32 Puerto Rico 4.75 29 4.64 33 4.86
33 Oman 4.69 32 4.54 36 4.84
34 Korea, Rep. 4.68 42 4.18 20 5.19
35 Portugal 4.63 39 4.26 25 5.00
36 Malta 4.62 30 4.63 49 4.62
37 Mauritius 4.61 33 4.48 43 4.75
38 Barbados 4.58 35 4.42 42 4.75
39 Cyprus 4.57 45 4.16 26 4.98
40 Bahrain 4.52 48 4.07 27 4.96
41 Jordan 4.45 44 4.16 41 4.75
42 Latvia 4.44 53 4.01 32 4.86
43 Lithuania 4.41 52 4.02 37 4.79
44 Turkey 4.38 55 4.00 38 4.77
45 Spain 4.31 47 4.09 51 4.54
46 Panama 4.31 62 3.76 35 4.85
47 Slovenia 4.28 72 3.66 30 4.89
48 Ghana 4.23 41 4.19 66 4.28
49 Czech Republic 4.23 51 4.05 60 4.41
50 Hungary 4.22 54 4.00 56 4.44
51 Uruguay 4.21 57 3.96 54 4.46
52 Montenegro 4.21 80 3.56 34 4.86
53 Macedonia, FYR 4.21 67 3.74 46 4.67
54 Brunei Darussalam 4.20 46 4.15 69 4.25
55 Poland 4.12 65 3.75 52 4.49
56 Thailand 4.12 79 3.56 45 4.69
57 Kazakhstan 4.11 61 3.80 58 4.42
58 Croatia 4.10 88 3.51 44 4.69
59 Namibia 4.10 37 4.38 112 3.81
60 Zambia 4.07 59 3.82 63 4.33
61 Botswana 4.05 40 4.26 107 3.84
62 Seychelles 4.05 49 4.07 90 4.02
63 Indonesia 4.04 68 3.71 62 4.36
64 Costa Rica 4.00 63 3.76 70 4.24
65 Guyana 4.00 64 3.76 71 4.23
66 Lao PDR 3.99 50 4.06 97 3.92
67 Jamaica 3.96 60 3.82 80 4.10
68 Gambia, The 3.95 36 4.39 125 3.50
69 Cape Verde 3.94 58 3.91 93 3.98
70 Azerbaijan 3.94 66 3.75 77 4.13
71 Bulgaria 3.94 105 3.29 50 4.59
72 Mongolia 3.91 98 3.39 57 4.43
73 Georgia 3.91 97 3.40 59 4.42
74 Slovak Republic 3.90 83 3.55 68 4.25
75 Mexico 3.88 70 3.68 85 4.07
76 Bhutan 3.87 43 4.17 123 3.57
77 China 3.87 56 3.97 115 3.76
78 Kuwait 3.85 75 3.61 81 4.09
79 Sri Lanka 3.85 74 3.62 82 4.08
80 Bosnia and Herzegovina 3.83 76 3.59 83 4.08
81 Ecuador 3.81 89 3.51 78 4.12
82 Liberia 3.80 92 3.45 75 4.15
83 Armenia 3.80 104 3.32 67 4.28
84 Morocco 3.79 81 3.56 88 4.03
85 Romania 3.79 101 3.38 72 4.20
86 Iran, Islamic Rep. 3.79 86 3.53 86 4.04
87 Russian Federation 3.78 100 3.39 73 4.17
88 Italy 3.77 99 3.39 76 4.15
89 Greece 3.76 114 3.20 64 4.32
90 Philippines 3.76 87 3.51 92 4.01
91 India 3.76 73 3.64 103 3.87
92 Kenya 3.75 71 3.67 110 3.83
93 Peru 3.75 119 3.10 61 4.39
94 Trinidad and Tobago 3.73 93 3.45 91 4.01
95 Albania 3.72 117 3.14 65 4.31
96 Vietnam 3.68 91 3.47 100 3.88
97 Dominican Republic 3.68 110 3.24 79 4.12
98 Cambodia 3.66 95 3.43 98 3.90
99 Lesotho 3.66 90 3.48 108 3.84
100 Senegal 3.64 106 3.26 89 4.02
101 Colombia 3.64 96 3.41 104 3.87
102 Tunisia 3.64 94 3.44 109 3.83
103 Lebanon 3.63 142 2.62 48 4.63
104 Uganda 3.59 77 3.57 121 3.61
105 Guatemala 3.59 123 3.02 74 4.16
106 Serbia 3.58 118 3.11 87 4.04
107 Malawi 3.57 69 3.70 130 3.43
108 Sierra Leone 3.55 82 3.55 124 3.55
109 Nigeria 3.54 112 3.23 106 3.85
110 El Salvador 3.53 121 3.10 95 3.96
111 Côte d’Ivoire 3.52 116 3.16 102 3.87
112 Ethiopia 3.50 102 3.37 120 3.64
113 Swaziland 3.49 84 3.55 129 3.44
114 Ukraine 3.48 130 2.89 84 4.08
115 Tanzania 3.47 85 3.54 132 3.39
116 Brazil 3.45 78 3.57 135 3.33
117 Mali 3.44 111 3.23 119 3.65
118 Pakistan 3.44 124 3.00 101 3.88
119 Egypt 3.44 115 3.18 117 3.69
120 Madagascar 3.43 129 2.90 94 3.97
121 Moldova 3.42 126 2.98 105 3.85
122 Kyrgyz Republic 3.39 131 2.89 99 3.89
123 Nepal 3.39 125 2.99 113 3.78
124 Mozambique 3.36 113 3.22 126 3.49
125 Cameroon 3.35 132 2.87 111 3.83
126 Burkina Faso 3.35 108 3.24 128 3.45
127 Benin 3.33 107 3.25 131 3.41
128 Nicaragua 3.32 103 3.32 136 3.31
129 Honduras 3.31 128 2.95 118 3.67
130 Paraguay 3.30 136 2.66 96 3.94
131 Bolivia 3.22 109 3.24 139 3.20
132 Bangladesh 3.21 138 2.65 114 3.77
133 Zimbabwe 3.20 122 3.06 133 3.35
134 Gabon 3.20 120 3.10 137 3.30
135 Argentina 3.19 135 2.78 122 3.61
136 Libya 3.17 141 2.64 116 3.69
137 Suriname 3.16 134 2.84 127 3.48
138 Timor-Leste 3.14 127 2.95 134 3.33
139 Yemen 2.94 143 2.58 138 3.30
140 Mauritania 2.91 133 2.86 143 2.95
141 Haiti 2.84 144 2.58 141 3.09
142 Guinea 2.77 139 2.65 144 2.89
143 Algeria 2.76 140 2.64 145 2.87
144 Burundi 2.73 146 2.43 142 3.03
145 Venezuela 2.72 148 2.30 140 3.15
146 Myanmar 2.68 137 2.66 146 2.71
147 Angola 2.59 145 2.52 147 2.65
148 Chad 2.40 147 2.43 148 2.36
Political and Business and
regulatory innovation
environment environment
Political and Business and
regulatory innovation
environment environment

Table 3: Readiness subindex and pillars
READINESS SUBINDEX
Rank Country/Economy Score Rank Score Rank Score Rank Score
1 Finland 6.61 1 6.88 18 6.41 1 6.55
2 Iceland 6.44 2 6.88 13 6.44 13 5.99
3 Sweden 6.39 3 6.85 10 6.48 21 5.83
4 Norway 6.28 6 6.80 28 6.18 17 5.86
5 United States 6.27 4 6.83 21 6.36 32 5.62
6 Singapore 6.20 16 6.30 46 5.88 2 6.42
7 Taiwan, China 6.17 5 6.81 53 5.74 14 5.96
8 Germany 6.16 11 6.48 43 5.94 12 6.05
9 Australia 6.15 8 6.79 49 5.83 20 5.85
10 Switzerland 6.15 9 6.69 66 5.40 3 6.36
11 Austria 6.14 10 6.53 34 6.09 22 5.79
12 Hong Kong SAR 6.11 26 5.88 22 6.36 10 6.08
13 Canada 6.10 7 6.79 65 5.41 8 6.10
14 Denmark 6.06 20 6.15 29 6.17 18 5.85
15 Netherlands 5.97 14 6.42 69 5.37 7 6.12
16 Cyprus 5.95 28 5.80 39 6.00 11 6.05
17 Korea, Rep. 5.93 13 6.42 57 5.72 31 5.66
18 Luxembourg 5.91 17 6.29 56 5.73 27 5.73
19 Japan 5.84 21 6.09 54 5.73 29 5.69
20 Israel 5.76 29 5.71 35 6.05 39 5.51
21 United Kingdom 5.74 15 6.36 79 5.16 28 5.69
22 Estonia 5.73 25 5.94 61 5.51 25 5.76
23 Malta 5.73 18 6.28 90 4.99 16 5.90
24 Lithuania 5.69 45 4.85 12 6.45 23 5.78
25 Belgium 5.66 22 6.04 101 4.59 4 6.34
26 Ireland 5.65 19 6.17 98 4.68 9 6.09
27 France 5.64 27 5.80 72 5.27 19 5.85
28 Slovenia 5.60 24 5.95 82 5.12 26 5.74
29 Latvia 5.60 41 5.03 26 6.21 35 5.56
30 Spain 5.60 32 5.48 41 5.99 50 5.33
31 Kazakhstan 5.57 58 4.50 2 6.88 51 5.32
32 Bahrain 5.52 39 5.05 25 6.29 58 5.23
33 Italy 5.49 42 4.91 32 6.09 43 5.47
34 Ukraine 5.49 74 4.06 3 6.88 37 5.54
35 Czech Republic 5.49 23 6.04 84 5.09 49 5.33
36 Qatar 5.48 31 5.60 100 4.59 5 6.26
37 Russian Federation 5.46 47 4.81 14 6.44 64 5.13
38 United Arab Emirates 5.44 30 5.62 85 5.09 33 5.62
39 Poland 5.40 38 5.07 52 5.78 48 5.34
40 Georgia 5.39 59 4.50 4 6.82 78 4.85
41 Croatia 5.38 54 4.57 36 6.03 36 5.55
42 Turkey 5.35 48 4.78 17 6.43 80 4.85
43 Portugal 5.35 36 5.18 62 5.47 46 5.40
44 Mongolia 5.31 69 4.19 7 6.61 65 5.12
45 New Zealand 5.27 12 6.42 127 3.24 6 6.14
46 Bosnia and Herzegovina 5.25 63 4.38 30 6.12 57 5.25
47 Mauritius 5.22 76 3.88 11 6.47 52 5.32
48 Jordan 5.22 88 3.51 6 6.64 38 5.51
49 Azerbaijan 5.21 55 4.55 40 5.99 66 5.09
50 Costa Rica 5.21 92 3.43 15 6.44 24 5.76
51 Armenia 5.13 53 4.58 63 5.45 47 5.37
52 Romania 5.11 51 4.69 68 5.39 54 5.26
53 Serbia 5.11 49 4.77 67 5.39 63 5.15
54 Saudi Arabia 5.11 33 5.32 96 4.73 55 5.26
55 Moldova 5.10 60 4.44 31 6.12 84 4.74
56 Trinidad and Tobago 5.09 57 4.52 74 5.25 42 5.49
57 Oman 5.07 70 4.14 33 6.09 73 4.99
58 Panama 5.06 65 4.28 27 6.20 86 4.72
59 Malaysia 5.03 71 4.12 48 5.88 67 5.09
60 Chile 5.01 44 4.86 81 5.13 71 5.02
61 Montenegro 4.99 46 4.81 103 4.54 34 5.62
62 Greece 4.97 40 5.04 102 4.59 53 5.29
63 Thailand 4.97 73 4.07 47 5.88 74 4.95
64 Kuwait 4.95 52 4.65 76 5.18 70 5.03
65 Indonesia 4.92 85 3.58 37 6.03 61 5.16
66 Slovak Republic 4.91 62 4.40 71 5.31 72 5.01
67 Hungary 4.89 64 4.35 94 4.80 41 5.50
68 Uruguay 4.88 50 4.76 80 5.14 83 4.75
69 Sri Lanka 4.88 104 3.12 38 6.02 40 5.51
70 Colombia 4.85 80 3.74 44 5.93 76 4.89
71 Macedonia, FYR 4.85 61 4.43 88 5.04 68 5.07
72 Venezuela 4.78 91 3.44 20 6.39 94 4.50
73 China 4.76 86 3.53 60 5.57 59 5.18
74 Seychelles 4.76 43 4.91 113 3.92 44 5.44
READINESS SUBINDEX
75 Bulgaria 4.75 34 5.26 119 3.74 56 5.26
76 Brazil 4.71 56 4.53 91 4.97 91 4.62
77 Vietnam 4.65 121 2.69 8 6.59 88 4.68
78 Brunei Darussalam 4.65 37 5.15 129 3.12 30 5.69
79 Lebanon 4.63 77 3.86 99 4.62 45 5.41
80 Guyana 4.62 98 3.32 70 5.37 60 5.18
81 Philippines 4.60 89 3.51 75 5.24 69 5.07
82 Paraguay 4.60 72 4.10 50 5.81 105 3.89
83 Ecuador 4.59 75 3.94 92 4.89 75 4.94
84 Bhutan 4.58 67 4.22 45 5.89 114 3.63
85 India 4.57 119 2.72 1 7.00 101 4.00
86 Albania 4.57 90 3.49 87 5.07 62 5.16
87 Tunisia 4.55 83 3.59 73 5.25 81 4.80
88 Jamaica 4.52 79 3.81 89 5.03 87 4.71
89 Puerto Rico 4.46 66 4.24 n/a n/a 90 4.68
90 Peru 4.43 95 3.37 59 5.65 99 4.27
91 Barbados 4.40 35 5.25 144 1.99 15 5.95
92 Suriname 4.40 101 3.25 86 5.08 77 4.86
93 Egypt 4.35 99 3.30 16 6.44 120 3.32
94 Mexico 4.34 81 3.72 93 4.89 95 4.42
95 Morocco 4.31 93 3.42 51 5.78 111 3.73
96 El Salvador 4.24 102 3.15 55 5.73 107 3.85
97 Zimbabwe 4.20 128 2.42 24 6.33 108 3.85
98 South Africa 4.17 68 4.21 112 3.97 97 4.32
99 Kenya 4.14 94 3.39 97 4.73 98 4.29
100 Argentina 4.13 78 3.86 121 3.69 79 4.85
101 Algeria 4.12 127 2.43 42 5.96 102 3.99
102 Cape Verde 4.09 107 3.06 106 4.45 82 4.76
103 Dominican Republic 4.05 87 3.52 95 4.75 106 3.88
104 Bangladesh 4.02 112 2.88 23 6.34 128 2.84
105 Pakistan 3.97 110 2.97 19 6.40 136 2.54
106 Kyrgyz Republic 3.95 96 3.35 116 3.83 89 4.68
107 Guatemala 3.92 100 3.29 78 5.17 122 3.31
108 Honduras 3.89 115 2.83 77 5.18 112 3.67
109 Ghana 3.89 124 2.50 64 5.41 110 3.75
110 Iran, Islamic Rep. 3.87 103 3.14 118 3.74 85 4.73
111 Nepal 3.82 141 1.65 9 6.49 121 3.32
112 Uganda 3.80 113 2.87 58 5.68 127 2.86
113 Cambodia 3.73 97 3.35 105 4.50 119 3.34
114 Liberia 3.70 145 1.57 5 6.78 131 2.75
115 Bolivia 3.58 114 2.83 126 3.36 93 4.54
116 Namibia 3.46 106 3.10 125 3.37 104 3.91
117 Gabon 3.34 132 2.28 109 4.09 113 3.66
118 Botswana 3.32 109 3.01 142 2.39 92 4.57
119 Nigeria 3.31 117 2.81 107 4.42 132 2.71
120 Yemen 3.31 129 2.39 83 5.12 138 2.41
121 Nicaragua 3.30 82 3.66 140 2.48 109 3.76
122 Libya 3.23 84 3.58 145 1.73 96 4.37
123 Tanzania 3.17 120 2.70 111 4.03 129 2.77
124 Côte d’Ivoire 3.15 105 3.11 120 3.70 133 2.65
125 Rwanda 3.14 108 3.05 128 3.13 123 3.25
126 Zambia 3.12 130 2.37 124 3.40 117 3.59
127 Senegal 3.08 116 2.83 117 3.78 134 2.64
128 Swaziland 3.07 118 2.79 143 2.28 100 4.14
129 Lao PDR 3.03 125 2.46 130 3.10 118 3.51
130 Lesotho 2.99 131 2.37 138 2.66 103 3.95
131 Gambia, The 2.85 123 2.60 137 2.75 124 3.20
132 Timor-Leste 2.80 111 2.97 133 2.83 135 2.61
133 Mauritania 2.78 139 1.72 104 4.53 145 2.08
134 Benin 2.76 122 2.69 134 2.82 130 2.76
135 Ethiopia 2.70 135 1.95 115 3.85 140 2.30
136 Malawi 2.70 126 2.43 139 2.62 125 3.03
137 Cameroon 2.65 143 1.58 136 2.77 116 3.60
138 Angola 2.63 146 1.55 110 4.07 141 2.26
139 Guinea 2.63 134 2.01 122 3.69 142 2.17
140 Haiti 2.61 142 1.63 108 4.09 143 2.11
141 Sierra Leone 2.59 138 1.85 114 3.88 146 2.03
142 Mozambique 2.31 137 1.86 132 3.06 147 2.00
143 Mali 2.29 140 1.69 135 2.78 139 2.41
144 Madagascar 2.29 144 1.57 141 2.42 126 2.88
145 Burundi 2.28 133 2.12 n/a n/a 137 2.45
146 Chad 2.24 148 1.39 123 3.44 148 1.89
147 Burkina Faso 2.21 147 1.41 131 3.10 144 2.11
148 Myanmar 2.16 136 1.88 146 1.00 115 3.60
Infrastructure
and digital
content Affordability Skills
Infrastructure
and digital
content Affordability Skills

Table 4: Usage subindex and pillars
USAGE SUBINDEX
1 Sweden 6.06 1 6.59 3 5.99 7 5.60
2 Finland 6.01 6 6.42 2 6.02 8 5.57
3 Korea, Rep. 5.89 9 6.32 10 5.48 3 5.85
4 Singapore 5.87 10 6.13 15 5.21 1 6.26
5 Netherlands 5.86 4 6.48 6 5.71 14 5.40
6 Norway 5.79 2 6.57 12 5.44 15 5.36
7 Denmark 5.75 3 6.57 7 5.66 26 5.01
8 Luxembourg 5.73 5 6.43 13 5.27 10 5.48
9 Japan 5.69 16 5.92 4 5.99 22 5.15
10 Switzerland 5.64 11 6.07 1 6.10 35 4.74
11 United States 5.60 18 5.76 9 5.56 11 5.47
12 United Kingdom 5.58 8 6.33 17 5.06 17 5.35
13 Germany 5.51 19 5.72 5 5.85 27 4.97
14 Israel 5.45 26 5.51 8 5.66 19 5.19
15 Hong Kong SAR 5.41 12 6.03 16 5.13 24 5.07
16 New Zealand 5.37 13 5.98 21 4.81 18 5.34
17 Taiwan, China 5.34 28 5.44 14 5.24 16 5.36
18 Qatar 5.33 21 5.69 26 4.53 4 5.77
19 Australia 5.28 15 5.92 24 4.75 21 5.17
20 Austria 5.27 20 5.71 11 5.44 36 4.65
21 United Arab Emirates 5.24 29 5.30 29 4.37 2 6.06
22 Estonia 5.22 17 5.84 28 4.38 12 5.45
23 France 5.16 22 5.65 20 4.88 28 4.96
24 Iceland 5.16 7 6.39 22 4.80 53 4.28
25 Bahrain 5.13 14 5.96 49 3.81 5 5.62
26 Canada 5.04 27 5.46 25 4.63 25 5.05
27 Belgium 5.02 25 5.52 18 5.05 42 4.47
28 Malta 5.01 24 5.55 32 4.09 13 5.40
29 Ireland 4.92 23 5.57 23 4.75 45 4.43
30 Malaysia 4.83 49 4.49 27 4.45 9 5.55
31 Saudi Arabia 4.78 44 4.67 34 4.04 6 5.62
32 Portugal 4.56 42 4.83 35 4.04 33 4.81
33 Spain 4.53 32 5.21 40 3.96 44 4.44
34 Lithuania 4.51 41 4.83 33 4.09 37 4.60
35 Slovenia 4.44 34 5.09 37 4.02 57 4.20
36 Puerto Rico 4.40 63 4.03 19 5.05 63 4.12
37 Oman 4.40 56 4.30 57 3.72 20 5.18
38 Kazakhstan 4.39 51 4.42 66 3.61 23 5.12
39 Chile 4.37 52 4.40 45 3.89 32 4.83
40 Czech Republic 4.36 30 5.26 31 4.10 96 3.72
41 Brunei Darussalam 4.36 50 4.47 56 3.73 30 4.86
42 Latvia 4.35 31 5.25 48 3.81 78 3.97
43 Barbados 4.30 33 5.11 53 3.77 72 4.01
44 Azerbaijan 4.24 61 4.19 52 3.78 34 4.77
45 Hungary 4.21 40 4.91 62 3.67 69 4.04
46 Croatia 4.18 39 4.99 81 3.46 65 4.08
47 Brazil 4.13 59 4.21 41 3.92 54 4.27
48 Cyprus 4.13 45 4.62 58 3.71 66 4.07
49 Slovak Republic 4.11 35 5.09 65 3.66 106 3.60
50 Panama 4.10 68 3.74 39 3.99 39 4.56
51 Italy 4.07 37 5.05 61 3.68 112 3.49
52 Montenegro 4.07 55 4.32 69 3.59 51 4.30
53 Russian Federation 4.06 46 4.61 84 3.45 61 4.13
54 Poland 4.06 36 5.08 75 3.52 108 3.57
55 Uruguay 4.05 48 4.55 86 3.43 59 4.16
56 Macedonia, FYR 4.03 53 4.38 101 3.31 47 4.41
57 Costa Rica 4.02 64 3.95 38 4.01 64 4.12
58 Kuwait 4.00 38 5.01 94 3.40 105 3.60
59 Jordan 3.96 67 3.79 47 3.81 52 4.28
60 Mauritius 3.95 66 3.86 64 3.66 48 4.34
61 China 3.91 80 3.27 44 3.89 38 4.58
62 Colombia 3.91 77 3.40 79 3.47 31 4.86
63 Turkey 3.90 69 3.69 46 3.87 60 4.14
64 Seychelles 3.90 65 3.87 55 3.76 68 4.06
65 Bulgaria 3.87 47 4.61 104 3.29 97 3.71
66 Trinidad and Tobago 3.86 60 4.20 87 3.43 80 3.96
67 Greece 3.83 43 4.74 102 3.30 117 3.45
68 Romania 3.76 62 4.17 98 3.34 90 3.77
69 Indonesia 3.75 95 2.90 36 4.03 49 4.31
70 South Africa 3.72 78 3.39 30 4.15 103 3.62
71 Mexico 3.72 89 3.07 70 3.59 40 4.50
72 Serbia 3.66 54 4.36 133 2.93 100 3.69
73 Armenia 3.65 74 3.52 82 3.45 76 3.98
74 Ecuador 3.63 83 3.18 71 3.59 62 4.13
USAGE SUBINDEX
75 Georgia 3.63 76 3.43 110 3.21 55 4.24
76 Philippines 3.63 91 2.94 43 3.89 67 4.06
77 Argentina 3.62 57 4.26 99 3.33 121 3.26
78 Vietnam 3.60 84 3.18 88 3.43 58 4.19
79 Bosnia and Herzegovina 3.59 70 3.67 92 3.42 99 3.69
80 Thailand 3.58 85 3.17 59 3.70 84 3.88
81 Sri Lanka 3.54 112 2.38 50 3.80 43 4.44
82 Morocco 3.53 72 3.63 111 3.21 92 3.76
83 Moldova 3.53 73 3.55 125 3.05 75 3.99
84 Tunisia 3.51 81 3.25 103 3.30 77 3.98
85 Mongolia 3.50 90 3.04 83 3.45 71 4.02
86 Kenya 3.49 113 2.30 54 3.76 46 4.41
87 Dominican Republic 3.49 93 2.92 67 3.60 81 3.95
88 Botswana 3.46 79 3.31 106 3.25 86 3.82
89 Egypt 3.45 71 3.66 112 3.21 113 3.49
90 Lebanon 3.45 58 4.23 116 3.19 136 2.93
91 India 3.45 121 2.08 51 3.78 41 4.48
92 Cape Verde 3.45 97 2.84 115 3.19 50 4.31
93 El Salvador 3.44 96 2.85 80 3.47 74 4.00
94 Jamaica 3.43 87 3.10 72 3.58 104 3.62
95 Albania 3.41 82 3.25 107 3.24 93 3.75
96 Guatemala 3.41 99 2.77 42 3.90 109 3.57
97 Rwanda 3.39 138 1.68 76 3.52 29 4.96
98 Peru 3.36 94 2.91 89 3.43 94 3.75
99 Gambia, The 3.35 120 2.12 60 3.69 56 4.24
100 Ghana 3.34 100 2.74 90 3.42 85 3.88
101 Ukraine 3.34 75 3.49 93 3.40 129 3.12
102 Guyana 3.34 103 2.54 63 3.66 87 3.81
103 Namibia 3.25 101 2.69 68 3.59 116 3.47
104 Zambia 3.17 123 2.04 77 3.51 79 3.97
105 Nigeria 3.17 110 2.42 73 3.55 111 3.54
106 Senegal 3.17 115 2.26 85 3.45 88 3.80
107 Cambodia 3.16 105 2.51 78 3.48 114 3.48
108 Venezuela 3.13 88 3.07 119 3.15 127 3.18
109 Paraguay 3.13 98 2.79 105 3.27 120 3.33
110 Bhutan 3.10 114 2.28 130 2.99 70 4.04
111 Mali 3.08 118 2.14 114 3.19 82 3.91
112 Lao PDR 3.07 129 1.87 74 3.54 89 3.80
113 Iran, Islamic Rep. 3.05 111 2.39 129 3.00 91 3.76
114 Suriname 3.04 86 3.11 108 3.23 141 2.78
115 Côte d’Ivoire 3.04 117 2.17 96 3.37 107 3.58
116 Bolivia 3.02 108 2.44 118 3.16 115 3.47
117 Honduras 2.95 106 2.46 91 3.42 133 2.98
118 Zimbabwe 2.93 107 2.45 109 3.22 128 3.14
119 Cameroon 2.93 130 1.78 95 3.38 102 3.64
120 Bangladesh 2.91 134 1.72 127 3.00 73 4.00
121 Pakistan 2.91 126 1.93 97 3.36 118 3.43
122 Gabon 2.90 109 2.43 126 3.02 122 3.26
123 Nicaragua 2.84 122 2.08 113 3.20 123 3.25
124 Tanzania 2.84 137 1.69 120 3.13 98 3.69
125 Uganda 2.83 140 1.63 122 3.12 95 3.75
126 Kyrgyz Republic 2.81 102 2.55 137 2.88 132 3.00
127 Burkina Faso 2.73 139 1.67 135 2.88 101 3.65
128 Swaziland 2.72 119 2.13 117 3.19 140 2.84
129 Madagascar 2.71 141 1.60 100 3.32 126 3.22
130 Ethiopia 2.71 146 1.46 141 2.77 83 3.90
131 Mozambique 2.70 144 1.50 124 3.06 110 3.55
132 Liberia 2.68 136 1.69 123 3.10 124 3.24
133 Nepal 2.66 125 1.96 132 2.95 130 3.07
134 Algeria 2.66 104 2.54 147 2.47 134 2.97
135 Sierra Leone 2.64 135 1.70 134 2.88 119 3.35
136 Malawi 2.63 142 1.55 121 3.12 125 3.23
137 Benin 2.63 124 1.98 128 3.00 137 2.90
138 Lesotho 2.57 127 1.92 136 2.88 135 2.93
139 Libya 2.56 92 2.92 144 2.63 148 2.13
140 Mauritania 2.54 116 2.24 139 2.85 146 2.53
141 Timor-Leste 2.48 128 1.90 142 2.66 138 2.89
142 Angola 2.48 133 1.74 143 2.64 131 3.07
143 Yemen 2.44 131 1.78 131 2.95 145 2.57
144 Guinea 2.39 145 1.47 138 2.85 139 2.84
145 Haiti 2.34 132 1.78 140 2.77 147 2.46
146 Myanmar 2.22 143 1.51 145 2.50 143 2.65
147 Chad 2.18 147 1.34 146 2.50 142 2.70
148 Burundi 2.12 148 1.30 148 2.42 144 2.64
Individual Business Governent
usage usage usage
Individual Business Governent
usage usage usage

Table 5: Impact subindex and pillars
IMPACT SUBINDEX
1 Singapore 5.93 6 5.63 1 6.24
2 Finland 5.91 1 6.04 7 5.78
3 Netherlands 5.85 5 5.63 3 6.06
4 Sweden 5.82 2 6.03 10 5.62
5 Korea, Rep. 5.67 7 5.25 2 6.09
6 Israel 5.52 4 5.64 14 5.40
7 Taiwan, China 5.43 12 5.08 6 5.79
8 United States 5.39 9 5.20 12 5.58
9 United Kingdom 5.36 14 5.01 9 5.72
10 Hong Kong SAR 5.32 13 5.03 11 5.62
11 Switzerland 5.30 3 5.64 26 4.96
12 Norway 5.29 15 5.00 13 5.58
13 Estonia 5.23 22 4.58 4 5.88
14 Germany 5.20 8 5.22 20 5.17
15 Luxembourg 5.17 10 5.16 19 5.17
16 Japan 5.12 11 5.12 23 5.13
17 Canada 5.10 17 4.87 16 5.33
18 United Arab Emirates 5.01 27 4.19 5 5.84
19 Denmark 4.99 16 4.94 24 5.05
20 Australia 4.95 23 4.57 15 5.33
21 Qatar 4.84 32 3.95 8 5.72
22 New Zealand 4.81 26 4.44 17 5.18
23 France 4.73 19 4.77 35 4.68
24 Austria 4.67 24 4.51 31 4.83
25 Iceland 4.61 25 4.51 34 4.72
26 Puerto Rico 4.56 21 4.66 38 4.46
27 Lithuania 4.53 28 4.11 27 4.96
28 Malaysia 4.51 30 4.01 25 5.00
29 Belgium 4.50 20 4.67 40 4.33
30 Malta 4.49 29 4.03 28 4.94
31 Ireland 4.43 18 4.83 55 4.03
32 Saudi Arabia 4.40 37 3.65 22 5.14
33 Portugal 4.36 34 3.87 30 4.85
34 Spain 4.30 31 3.97 36 4.63
35 Bahrain 4.26 63 3.35 18 5.17
36 Kazakhstan 4.26 60 3.38 21 5.15
37 Chile 4.23 43 3.55 29 4.91
38 Brunei Darussalam 4.15 51 3.48 32 4.82
39 Slovenia 4.07 33 3.92 43 4.22
40 Oman 4.07 56 3.41 33 4.73
41 Panama 3.99 46 3.49 37 4.48
42 Hungary 3.97 36 3.68 42 4.26
43 Latvia 3.94 35 3.71 45 4.17
44 Russian Federation 3.91 41 3.56 41 4.27
45 Czech Republic 3.87 38 3.63 47 4.12
46 Azerbaijan 3.85 42 3.55 46 4.15
47 Montenegro 3.81 39 3.60 56 4.02
48 Jordan 3.81 44 3.53 51 4.09
49 Colombia 3.79 75 3.16 39 4.42
50 Costa Rica 3.75 52 3.47 54 4.04
51 Rwanda 3.75 53 3.45 53 4.05
52 Cyprus 3.73 45 3.49 57 3.97
53 Uruguay 3.73 61 3.36 50 4.10
54 Croatia 3.69 40 3.56 66 3.83
55 Macedonia, FYR 3.68 65 3.31 52 4.06
56 China 3.67 81 3.11 44 4.22
57 Brazil 3.64 64 3.34 58 3.94
58 Barbados 3.62 57 3.40 64 3.84
59 Mexico 3.62 80 3.12 48 4.11
60 India 3.61 50 3.48 73 3.74
61 Egypt 3.61 59 3.38 65 3.83
62 Philippines 3.57 48 3.49 76 3.66
63 Mongolia 3.57 89 3.04 49 4.10
64 Dominican Republic 3.55 71 3.22 61 3.88
65 Turkey 3.55 68 3.27 67 3.83
66 Slovak Republic 3.54 54 3.44 78 3.63
67 Armenia 3.53 47 3.49 80 3.58
68 Moldova 3.52 76 3.16 60 3.89
69 Sri Lanka 3.47 66 3.30 77 3.64
70 Mauritius 3.47 70 3.25 74 3.69
71 Kenya 3.46 55 3.42 83 3.50
72 Indonesia 3.46 86 3.07 63 3.84
73 Cape Verde 3.45 83 3.09 69 3.81
74 Georgia 3.44 100 2.95 59 3.93
IMPACT SUBINDEX
75 Vietnam 3.41 96 2.98 62 3.85
76 Tunisia 3.39 90 3.03 72 3.76
77 Seychelles 3.39 77 3.16 79 3.63
78 Poland 3.39 62 3.36 88 3.42
79 Gambia, The 3.39 67 3.27 84 3.50
80 Ecuador 3.38 95 2.99 71 3.78
81 Peru 3.37 84 3.08 75 3.67
82 Italy 3.37 58 3.40 94 3.34
83 Thailand 3.35 104 2.88 68 3.83
84 El Salvador 3.31 109 2.84 70 3.78
85 Senegal 3.29 82 3.10 86 3.48
86 Bulgaria 3.27 73 3.20 93 3.35
87 Bosnia and Herzegovina 3.27 88 3.04 85 3.49
88 Lao PDR 3.27 74 3.18 92 3.35
89 South Africa 3.25 49 3.48 113 3.02
90 Greece 3.24 91 3.03 87 3.45
91 Nigeria 3.23 72 3.20 99 3.25
92 Trinidad and Tobago 3.21 92 3.02 89 3.41
93 Serbia 3.19 93 3.00 90 3.38
94 Jamaica 3.18 78 3.13 100 3.24
95 Mali 3.18 69 3.25 109 3.10
96 Argentina 3.18 87 3.07 98 3.29
97 Bhutan 3.17 112 2.78 81 3.56
98 Ukraine 3.16 79 3.12 102 3.20
99 Guatemala 3.16 94 2.99 95 3.32
100 Romania 3.13 97 2.96 96 3.30
101 Ghana 3.12 85 3.08 106 3.17
102 Guyana 3.12 106 2.87 91 3.37
103 Kuwait 3.04 127 2.58 82 3.50
104 Bolivia 3.01 111 2.79 101 3.24
105 Pakistan 2.99 98 2.96 112 3.03
106 Zambia 2.99 113 2.78 104 3.20
107 Iran, Islamic Rep. 2.97 114 2.77 105 3.17
108 Albania 2.95 125 2.60 97 3.30
109 Venezuela 2.94 115 2.76 108 3.12
110 Botswana 2.90 120 2.68 107 3.12
111 Cambodia 2.90 117 2.70 110 3.10
112 Ethiopia 2.88 128 2.57 103 3.20
113 Nicaragua 2.87 122 2.65 111 3.08
114 Lebanon 2.87 101 2.92 121 2.81
115 Paraguay 2.86 99 2.95 125 2.78
116 Cameroon 2.85 103 2.88 123 2.81
117 Namibia 2.85 105 2.88 122 2.81
118 Burkina Faso 2.84 108 2.85 120 2.84
119 Côte d’Ivoire 2.84 102 2.92 127 2.76
120 Honduras 2.80 116 2.75 119 2.85
121 Morocco 2.79 123 2.64 115 2.94
122 Uganda 2.79 126 2.60 114 2.98
123 Mozambique 2.73 121 2.67 124 2.79
124 Swaziland 2.73 118 2.70 126 2.76
125 Malawi 2.72 110 2.79 131 2.64
126 Kyrgyz Republic 2.71 131 2.50 116 2.92
127 Bangladesh 2.71 130 2.50 118 2.91
128 Tanzania 2.68 132 2.45 117 2.91
129 Suriname 2.62 107 2.86 138 2.38
130 Sierra Leone 2.62 124 2.63 133 2.61
131 Zimbabwe 2.61 129 2.54 130 2.69
132 Benin 2.58 119 2.69 136 2.47
133 Liberia 2.57 134 2.43 128 2.71
134 Madagascar 2.52 135 2.42 132 2.61
135 Nepal 2.51 141 2.32 129 2.70
136 Gabon 2.49 136 2.41 134 2.58
137 Algeria 2.39 133 2.44 140 2.34
138 Angola 2.38 137 2.40 139 2.36
139 Timor-Leste 2.34 143 2.30 137 2.39
140 Myanmar 2.33 139 2.37 141 2.30
141 Lesotho 2.31 147 2.08 135 2.53
142 Haiti 2.30 138 2.39 143 2.22
143 Yemen 2.24 140 2.35 145 2.13
144 Mauritania 2.23 142 2.32 144 2.14
145 Guinea 2.15 148 2.04 142 2.27
146 Burundi 2.09 144 2.18 147 2.00
147 Chad 2.08 146 2.12 146 2.05
148 Libya 2.03 145 2.12 148 1.94
Economic Social
impacts impacts
Economic Social
impacts impacts

changes in the rest, with the exception of the significant
improvement by six positions of Hong Kong SAR, which
climbs to 8th place. Box 1 presents and analyzes the
position and evolution of different economies in the
rankings over the past two years, highlighting different
dynamics in building and leveraging their digital
ecosystems.
For a second consecutive year, Finland tops
the rankings with a strong performance across the
board. It ranks 1st in the readiness subindex thanks
to an outstanding digital ICT infrastructure—the best
in the world—and 2nd in both the usage and impact
subindexes, with more than 90 percent of its population
using the Internet and with high levels of technological
and non-technological innovation. The country also
comes in 3rd in the environment subindex, with a
very robust innovation system. This positive digital
landscape has been created over the years, as Finland
decidedly started investing in building its ICT ecosystem
in the mid-1990s as an answer to its financial and
economic crisis at that time. Since then, the active
role of all stakeholders—government, businesses, and
individuals—has resulted in the positive outcome we see
today.
As in past years, Singapore continues to follow
closely in the rankings, remaining in 2nd place. With
the best pro-business and pro-innovation environment
worldwide, the city-state continues to obtain the top rank
in terms of ICT impacts, notably on the social dimension.
Supported by a government with a clear digital strategy
that offers the best online services in the world, an ICT
infrastructure that is relentlessly being improved over
time (16th), and one of the highest quality educational
systems in the world (3rd), notably in terms of math and
science (1st), Singapore has become one of the most
knowledge-intensive economies globally (2nd) and is an
ICT-generation powerhouse (9th). Improving the already
very high number of Internet users (29th) or households
with a personal computer and Internet access (11th)
to the level of some Nordic countries, coupled with
reducing the cost of accessing fixed broadband Internet
(now at 87th), would allow Singapore to lead the overall
rankings.
Sweden (3rd) maintains its position this year despite
a slight improvement in its overall score. Overall, the very
strong performance of Sweden reflects its world-class,
affordable (11th) ICT infrastructure (3rd) and a stable
and pro-business and innovation environment (15th),
despite its high tax rate (123rd). These strengths result in
outstanding uptake and use of ICTs by individuals (1st),
businesses (3rd), and government (7th) and one of the
highest technological and non-technological innovation
performances in the world (2nd), making Sweden a truly
knowledge-based society.
The Netherlands retains its 4th position despite a
slight decline in its score driven by a small drop in the
economic impacts pillar, where it nevertheless ranks a
very respectable 5th globally. The country, as a service-
based economy, has quickly and skilfully recognized
the importance of ICTs to boost its innovation and
competitiveness potential; this is reflected in virtually
all the indicators, where the country ranks among the
best in the world. ICTs have permeated all stages of
society in the Netherlands, with nearly all individuals
having access to a computer (1st) with an Internet
connection at home (3rd), a large number of government
services are online (5th), and businesses use extensive
e-commerce in their transactions with other businesses
(11th) and with consumers (4th). These scores, coupled
with an environment highly conducive to innovation and
entrepreneurship, result in very high levels of ICT-based
innovations and the highest citizen e-participation in the
world (1st). Although the affordability (67th) of a well-
developed ICT infrastructure and digital content (14th)
still remains an unresolved issue, it does not seem to
interfere in the capacity of the country to fully leverage
ICTs to obtain meaningful economic (5th) and social (3rd)
impacts.
Stable at 5th place, Norway continues to leverage
ICTs effectively. With a well-developed and affordable
(28th) ICT infrastructure (6th), ICT uptake is virtually
universal among Norway’s population: 95 percent are
Internet users and more than 90 percent have access to
a personal computer (5th) and Internet (4th) at home. In
addition, the country benefits from a stable pro-business
and innovation environment (6th) and a government that
is aware of the importance of ICTs for the economic and
social development of a geographically vast nation with a
widely dispersed population. Despite these many assets,
compared with its Nordic neighbors, Norway depicts
poorer results in terms of technological innovation (12th),
notably in the domain of ICTs (16th); improving the quality
of the overall educational system (18th), notably in the
area of math and science (47th), may represent part of
the solution.
Switzerland revalidates its 6th position of the
previous edition. The country benefits from very good,
albeit expensive (66th) ICT infrastructure (9th) and a
good educational system that provides the necessary
skills to create a knowledge-based, technology-rich
economy. Those assets, coupled with a stable political
and regulatory environment (9th) and excellent conditions
for innovation and entrepreneurship (12th), have resulted
in outstanding uptake and use of ICTs by businesses
(1st) in all their transactions and in their ability to foster
innovation. In this process of digitization, the government
appears to be lagging slightly behind (35th): government
online services (32nd) continue to be relatively reduced
compared with those of other countries of its economic
and social level of development, which partially affects its
overall social impacts (26th).
The United States moves up two positions to
7th place, thanks to slight improvements in many
areas of the Index. These include the country’s already

Box 1: Which countries are bridging the digital divide and which countries are not? An evolutionary
analysis of the NRI results
The GITR series, through its Networked Readiness Index
(NRI), depicts how countries leverage ICTs to boost
competitiveness and well-being. A constant finding in the
NRI analysis, and one that gets reflected in the overall
rankings, is that developed economies continue to lead the
way in creating robust and impactful digital ecosystems while
many developing and emerging economies have remained
comparatively stagnant. This stagnation persists despite
some improvements to their ICT infrastructures—specifically,
ensuring more and better mobile telephony access and
services. As a result, there has been little progress in bridging
the digital divide across nations. This lack of progress casts
doubts about the capacity of developing and emerging
economies to embrace the full potential that ICTs have to
offer.
However, the NRI rankings reflect the situation only at a
particular moment in time. They do not take into account the
evolution that different countries experience over time.
Figure A presents the situation of each analyzed
economy based on both its current NRI score and its NRI
evolution over the past three years. The position of each
economy is then determined according to whether it falls
above or below the NRI average in 2014, and whether it falls
above or below the average change in the NRI score over
the past two editions. As a result, each country’s position
in the figure reflects its placement in relation to the sample
average—that is, whether it has an above- or below-average
ICT capacity. Each country’s position also indicates whether
it is growing above or below the sample average—that is,
whether it is catching up or lagging behind in the long-term
race of developing and leveraging its digital ecosystem. In
addition, and in order to identify with more precision those
countries that have experienced the starkest changes over
time, the figure includes an upper and a lower band. These
bands are calculated based on the standard deviation of
the NRI change of the past two years; the upper band is the
sample average change plus 1 standard deviation, and the
lower band is the sample average change minus 1 standard
deviation.
Based on this classification, and following the logic of a
slightly modified Boston Consulting Group (BCG)’s “Growth-
Share Matrix,”1
we can identify four groups of countries:
1. Rising stars: Those countries whose NRI scores are
above the sample average and whose scores are also
growing more quickly than average. Armenia, Georgia,
Kazakhstan, Panama, Qatar, and the United Arab
Emirates belong to this group.
2. Sliding stars:2
Those countries whose NRI scores are
above average, but in which evolution seems to be
lagging behind. Barbados and Denmark belong to this
group.
3. Question marks: Those countries whose NRI scores are
below average but that are quickly catching up. Bolivia,
Ecuador, Haiti, Nicaragua, Peru, Suriname, Swaziland,
and Yemen belong to this group.
4. Laggards: Those countries whose NRI is below average
and that are lagging increasingly behind. Benin,
Botswana, Burundi, Chad, Malawi, Mozambique,
Timor-Leste, and Tunisia are in this group.
(Cont’d.)
Figure A: The NRI 2014 compared with its evolution since 2012
21 3 4 5 6 7
–15
–10
–5
0
5
10
15
20
Average
Average
NRI 2014 value
NRIchange,2012–2014
Albania
Azerbaijan
Yemen
Peru
Ecuador
Bosnia and Herzegovina
Panama
TunisiaIndonesia
Georgia
Armenia
Kazakhstan
Qatar
United Arab Emirates
Denmark
Chad
Burundi
Timor-Leste
Mozambique
Benin
Malawi
Honduras
Senegal
Botswana
Barbados
Nicaragua
Bolivia
Zimbabwe
Suriname
Swaziland
Source: Authors’ calculations.

Box 1: Which countries are bridging the digital divide and which countries are not? An evolutionary
analysis of the NRI results (cont’d.)
From this classification, we can learn several interesting
things. First of all, several oil- and gas-rich economies
in the Commonwealth of Independent States and Gulf
Cooperation Council are quickly improving their digital
ecosystems. The governments in these countries have
recognized the importance of investing in ICTs as a way to
diversify their economies and are quickly developing their
digital infrastructure in order not to miss the digital revolution.
Second, many countries in sub-Saharan Africa are lagging
behind, with no sign of improvement over time. This gap may
hamper their capacity to support further economic and social
development as the positive impacts of ICTs become more
and more apparent; this can have important consequences
for their future economic development if actions are not
adopted urgently. Third, in Latin America, those countries that
are particularly lagging behind are making significant strides
to improve their digital potential. The rest of the countries in
the region remain fairly stable. Panama, as an exception, is
one of the leading countries in the region that relentlessly
continues to strengthen its digital potential. Fourth, Barbados,
and especially Denmark (which has always scored at the
forefront of the rankings), should not be complacent but
should address any weaknesses in the conditions that may
hamper their potential to take advantage of their ICT capacity.
Notes
1 See the BCG matrix, available at http://www.
strategicmanagementinsight.com/tools/bcg-matrix-growth-share.
html.
2 In the original Boston Consulting Group’s classification, this group
would be the “cash cows.”
good business and innovation environment (7th) and
improvements in its ICT infrastructure (4th), notably in
terms of wider access to international Internet bandwidth
per user. Overall, the country exhibits a robust uptake
of ICTs by all major stakeholders—businesses (9th),
government (11th), and individuals (18th)—who manage
to leverage well one of the best and more affordable
(20th) ICT infrastructures (4th). Coupled with a pro-
business and pro-innovation environment (7th), these
result in a strong innovation capacity (5th) and significant
ICT-related economic impacts (9th). The ranking of the
United States, the largest economy in the world, in the
top 10 shows that fully leveraging ICTs is not dependent
on small or medium-sized economies, but instead
depends on undertaking the right investments and
creating the right condition for it.
With the most pronounced improvement among
the top 10, Hong Kong SAR climbs six positions to
8th place. The sharp improvement in its score is driven
by improvements in conditions for innovation and
entrepreneurship (2nd) that were already very positive, a
robust skills base (10th), and stronger business (16th) and
government usage (24th). Overall, Hong Kong SAR enjoys
a fairly well developed ICT infrastructure that, coupled
with a stable environment conducive to innovation
and entrepreneurship (4th), results in good economic
(13th) and social (11th) impacts. Notwithstanding these
strengths, individual uptake remains lower than it is in the
Nordic countries that lead the rankings.
Despite a drop of two places, the United Kingdom
continues to exhibit a very strong performance in 9th
position. As in the Netherlands, as a service-based
economy, the country early recognized the importance
of ICTs to support its innovation and competitiveness
performance. As a result, it has managed to build a well-
developed ICT infrastructure (15th), exhibiting one of the
highest population uptakes (8th) and a well-developed
e-commerce (1st), which, coupled with a strong pro-
business environment, has resulted in solid economic
(14th) and social (9th) impacts.
The Republic of Korea moves up one position,
entering the top 10 this year. A country that has largely
based its economic success on the ICT industry, Korea
benefits from a strong ICT infrastructure (13th) and an
excellent ICT uptake by individuals (9th), businesses
(10th), and especially government (3rd), which ranks 1st
in the world in terms of online services. The country’s
strong focus on developing its technological capacity
as part of its economic development strategy has also
resulted in a sturdy technological innovation performance
(8th), notably in the field of ICTs (4th). Improvements
in the conditions needed to support innovation and
entrepreneurship (34th) could help Korea to leverage
further its significant effort to become a leading
knowledge-intensive economy.
EUROPE AND THE COMMONWEALTH OF
INDEPENDENT STATES
Europe has been at the forefront of developing a digital
ecosystem as a key ingredient that fosters innovation
and competitiveness. As a result, several European
countries lead the NRI rankings, with six European
economies—Finland, Sweden, the Netherlands, Norway,
Switzerland, and the United Kingdom—in the top 10.
In addition, in order to maximize the positive impacts
of ICTs throughout the European Union and create
synergies and positive spillover effects, the European
Commission has developed its Digital Agenda as one
of seven flagship initiatives under its growth strategy
Europe 2020.9
Despite these efforts, important
differences remain across European economies,
with Southern and Central and Eastern European

economies continuing to lag behind. A deeper analysis
of the root causes of these differences shows that, in
general, ICT infrastructure and individual uptake is more
homogeneous across EU Member States. However, less
favorable conditions for innovation and entrepreneurship
across European countries result in starker disparities in
terms of the economic impacts—for example, innovation
performance—accruing from their use, which illustrates
the changing nature of the digital divide in Europe and
in the rest of the world. The digital divide should not be
regarded only in terms of access to ICT infrastructure,
but also in terms of the impacts that using ICTs can
provide for the economy and society in general. Box 2
develops this analysis further and provides some
recommendations for policies that would help to bridge
the European digital divide.
Luxembourg, in 11th place, climbs five places
thanks to continued improvements across the board.
The country continues to reap the benefits of significant
past efforts made to develop the ICT sector as a key
economic strategy for diversifying its local economy.
With one of the best ICT infrastructures in the world
(17th), Luxembourg counts on a high ICT uptake
(8th), with both businesses (13th) and individuals (5th)
using these technologies extensively. Combined with
optimal business conditions and a reliable and efficient
political and regulatory environment (4th), the country
has persisted in improving economic impacts, thanks
to more technological and non-technological related
innovations (10th) and the highest share of the workforce
working in knowledge-intensive jobs in the world. In
order to maintain this good momentum, the country
should persevere in enhancing its overall innovation
system (29th) so that it can fully leverage its ICT potential.
Germany continues to rise, this year by one
position, to reach 12th place, thanks to persistent
improvements in its overall business environment (17th)
and the perceived quality of its educational system
(14th), which seems to provide the right set of skills to
engage in an innovation-driven globalized economy. With
a very good ICT infrastructure (11th), which translates
into high levels of individual (11th) and business (5th) ICT
uptake, Germany benefits from high levels of ICT-driven
economic impacts (8th), as reflected in the excellent
innovation capacity (3rd) of local companies.
Despite a drop of five places this year, driven mainly
by a slight deterioration of its business environment
(16th), Denmark, now at 13th place, continues to benefit
from one of the highest rates of ICT usage (7th), with all
stakeholders using ICTs in their everyday activities. More
precisely, ICT uptake among individuals (3rd) is one of
the highest in the world: almost the entire population
has access to a computer and an Internet connection
at home and uses the Internet; businesses use ICTs
for their communication and transactions with other
businesses (18th) and clients (14th); and governments
offer a large share of their services online (13th). Despite
this good performance, in comparison with other Nordic
economies, there is some concern about the decreased
ability of businesses to fully leverage ICTs to generate
new services and products (33rd) and innovate by
offering new organizational models (29th). These issues
can potentially have a negative impact on an innovation-
reliant country’s ability to support further economic
growth and social development.
Estonia moves up one place to 21st position thanks
to improvements across many dimensions of the Index,
notably ICT usage (22nd) by both individuals (17th) and
businesses (28th). Following the model of neighboring
Finland, the country has recognized the crucial role
that ICTs have to play in the local economy and for
social development. The result is that Estonia ranks
1st among the Baltic Republics, followed by Lithuania
at 31st place and Latvia at 39th; it is also 1st among
Central and Eastern European countries and well ahead
of Southern European nations. With a well-developed
ICT infrastructure (25th) and a good environment for
business and innovation (28th), private companies have
developed well-functioning e-commerce strategies (6th)
and the country is managing to yield good economic
(22nd) and social (4th) ICT-related results. Going
forward, Estonia should follow the examples of its Nordic
neighbors in developing a robust innovation system that
can help further its decisive transition into a full-fledged
Moving out of the top 20, Austria drops three
places to land at 22nd position, despite its very stable
profile. Overall, the country boasts a very strong ICT
infrastructure (10th) with high levels of business usage
(11th), especially when interacting with other businesses
(9th). Coupled with other innovation-related investments,
such as research and development (R&D) and a
favorable business environment (23rd), this results in a
good technological performance both in the ICT sector
(13th) and in the economy in general (10th). Further
improvements in the entrepreneurial environment by
easing the procedures to open new businesses (103rd),
and in the quality of education in important areas for
innovation, such as mathematics and science (39th),
could help Austria leverage its digital potential better and
obtain an even more robust innovation performance.
France moves up one position to 25th place, thanks
to slight improvements in all three pillars of the readiness
subindex: ICT infrastructure, affordability, and skills.
Overall, the country presents a very harmonious uptake
of ICTs across all agents, exhibiting one of the highest
broadband Internet subscription rates in the world (4th),
a fairly good development of e-commerce (27th), and a
vast number of government services online (8th). Although
some concerns about the business and innovation system
(47th) exist—France has one of the highest tax systems
in the world (136th) and relatively low levels of venture
capital availability (49th)—the country manages to obtain

Europe is slowly emerging from one the worst financial
and economic crises in decades, but growth prospects
remain unstable and unemployment stubbornly high in
many countries, notably those most severely hit by the
crisis. Technological progress and digitization can represent
one of the main sources of potential economic growth and
employment generation for Europe, as has been recognized
by the European Commission.1
However, the level of digitization is not the same across
European Union (EU) Member States, and thus the potential
to benefit from ICT adoption remains uneven. An analysis
of the Networked Readiness Index (NRI) results shows that,
while many European countries are leading in the rankings
and several Nordic and Western countries are within the
top 10, many others continue to lag behind. A digital divide
persists within the European Union.
Although it is difficult to create homogeneous groups
of European countries in terms of their level of digitization
and the benefits accruing from it, a broad classification by
geography would show that Northern and Western Europe
depicts much stronger results than Southern and Central
and Eastern Europe, even if the situation differs broadly
within these groups. For example, within Central and Eastern
Europe, Estonia presents values similar to those of some of
the countries in Western Europe; within Southern Europe,
Portugal and Spain outperform Greece.
Figure A presents the NRI results for each of the 10
pillars for these three groups of EU Member States. Based on
this analysis, a number of key findings can be highlighted:
1. The gap between Northwestern European economies
and the rest of the Member States is reflected in all of
the 10 pillars of the NRI, from the market and regulatory
conditions that support high levels of ITC uptake to
levels of usage by all stakeholders and the economic
and social impacts accruing from ICTs.
2. Overall, most countries depict fairly well developed
ICT infrastructures. Although some countries should
continue strengthening these infrastructures, they may
not be the main source of the digital divide in Europe.
3. The cost of accessing ICTs is similar in all EU Member
States and thus should not be regarded as a primary
source of different levels of ICT uptake.
4. The gap in ICT usage across countries is bigger for
businesses and narrower across governments. Overall,
governments in most EU Member States have
recognized the importance of developing ICTs and
offer a fairly large number of public services online.
However, the differences among countries in the
capacity of their businesses to develop and integrate
ICTs in their business models are much starker.
5. The gap in terms of social and especially economic
impacts is the widest of the four subindexes, illustrating
the new nature of the digital divide in Europe. Because
the benefits of ICTs increasingly permeate all activities,
the digital divide within Europe becomes starker when
viewed by considering the impacts that benefit different
stakeholders.
These findings have several policy implications both
for EU Member States and for the European Commission’s
effort to build a common Digital Agenda that stimulates a
virtuous circle of investment in ICT infrastructure, higher
uptake levels, and stronger impacts for all. Arguably the
main implication is that digital strategies should focus not
only on developing ICT infrastructure but also on creating
the right conditions for an effective use of ICTs to boost
innovation, competitiveness, and greater social inclusion. In
order to do all that, public policies and company strategies
are needed to improve the digital literacy of the population;
to boost the overall skills capacity of the workforce through
effective educational and training systems; and to encourage
an effective integration of ICTs with other sources of
innovation, such as R&D investments or higher levels of
on-the-job training. To maximize their impact, these activities
will need to be coordinated across stakeholders, so creating
and strengthening public-private collaborations will be key.
Note
1 European Commission 2013.
Box 2: The digital divide in Europe
1. Political and
regulatory environment
6. Individual usage
5. Skills7. Business
usage
4. Affordability8. Government
usage
3. Infrastructure
and digital
content
9. Economic
impacts
2. Business and innovation
environment
10. Social
impacts
1
2
3
4
5
6
7
Figure A: The NRI 2014: European Union
Northwestern Europe
Central and Eastern Europe
Southern Europe
Note: Southern Europe includes Greece, Italy, Portugal, and Spain; Northwestern Europe
includes Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Luxembourg,
the Netherlands, Sweden, and the United Kingdom; and Central and Eastern Europe
includes Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia,
Lithuania, Malta, Poland, the Slovak Republic, and Slovenia.

good economic impacts (19th) and a large share of its
population is employed in knowledge-intensive jobs (10th).
Benefiting from a fairly well developed ICT
infrastructure (19th) and a pro-business and innovation
environment (16th), Ireland moves up one position to
26th place. Since the early days of the Internet revolution,
Ireland has identified ICTs as one of the key industries
that could help diversify its economy and has attracted
many global ICT companies thanks to its favorable
environment for business. As a result, the island boasts
good levels of digital connectivity that, coupled with a
skilful labor force, has resulted in good economic impacts
(18th) derived from technology-related innovations. On a
less positive note, the government seems to lag behind in
embracing ICTs in their offerings of online services (55th),
which affects the country’s capacity to fully leverage ICTs
to increase their social impacts (55th).
Portugal and Spain, at 33rd and 34th position
respectively, present fairly stable profiles. As in past
editions, both countries have managed to develop good
ICT infrastructures (36th and 32nd, respectively) and
ICT uptake has permeated among their populations,
particularly in Spain where almost three-quarters are
Internet users (34th). In addition, both governments have
made significant attempts to increase the number of
services they offer online. Despite these efforts, both
countries continue to struggle to fully leverage ICTs
to boost innovation (42nd and 57th, respectively), and
weaknesses in their innovation ecosystems persist,
notably in Spain (51st). Addressing these weaknesses and
integrating ICT investments better with other innovation-
enhancing investments, such as R&D, would result in
more robust economic outputs, which are needed for the
economic transformation of these countries.
In Southeastern Europe, once again Slovenia,
despite its current economic difficulties, continues to
lead the rankings, moving up one position to reach
36th place. The country boasts a fairly robust ICT
infrastructure (24th), along with good ICT uptake by
individuals (34th) and government in its offering of
online services (35th). Despite these positive features,
Slovenia does not manage to completely leverage the
full economic potential of ICTs for boosting innovation,
where it continues to lag behind other EU countries.
Weaknesses in Slovenia’s innovation system, with low
levels of venture capital (127th), a limited capacity to
innovate (54th), and low levels of on-the-job training
(105th) result in low levels of innovation, in terms of both
new products and services (69th) and organizational
models (68th), which hinder the productivity-enhancing
potential of the economy. Within the region, Croatia
follows at 47th place, with Bosnia and Herzegovina
(68th) and Serbia (79th) lagging behind because of
insufficient development of their ICT infrastructures,
weak ICT uptake, and weaknesses in their innovation
systems that hinder their potential to fully enjoy the
benefits that can accrue from ICT.
In Central and Eastern Europe, the Czech Republic
repeats last year’s position at 42nd place, despite
sharp improvements in rendering a fairly good ICT
infrastructure (23rd) more affordable (84th), which has
resulted in a slightly higher ICT uptake by individuals
(30th). However, this improvement is set off by a slight
deterioration in the political and regulatory environment
(51st) that does not allow for better ICT-related economic
(38th) or social impacts (47th). Overall, the country
continues to depict strong ICT uptake by individuals,
reflected by the fact that a large share of its population
uses the Internet (28th) and e-commerce is well
developed (9th). However, the government continues to
lag behind in supporting and promoting the use of ICTs
in their activities (96th), and weaknesses in the innovation
and entrepreneurial systems (60th) hinder the country’s
capacity to fully leverage ICTs for improving innovation,
competitiveness, and well-being.
Other countries in Central Europe—such as Hungary
and Poland, at 48th and 55th place respectively—have
lost some ground in the rankings because of the relatively
faster progress of other countries, while the Slovak
Republic improves its position by two places to reach
59th position. In addition, Bulgaria and Romania, 72nd
and 76th respectively, drop one position each, depicting
relatively stable profiles.
Despite a slight improvement in many indicators
related to ICT infrastructure (42nd) and uptake (37th),
which gives rise to a stable score, Italy suffers from a
significant drop of eight places in the rankings to reach
58th place: other countries progress faster in building
and fully utilizing their digital ecosystems. Persistent
weaknesses in Italy’s political and regulatory environment
(99th), coupled with some significant challenges in its
innovation system, hinder the country’s capacity to fully
leverage ICTs to boost innovation, competitiveness, and
well-being.
As in the case of Italy, Greece suffers from a
notable drop of 10 ranks despite a slight improvement
in its overall score, coming in at 74th place this year.
Continued improvements in its ICT infrastructure (40th)
and the uptake of its citizens (43rd), as well as a rising
number of broadband Internet subscriptions (27th)
and Internet users (53rd), are not reflected in better,
innovation-led economic (91st) or social (87th) impacts.
Weaknesses in the country’s political and regulatory
environment (114th) coupled with a low capacity to
innovate (117th) and scarce access to venture capital
(146th) affect the country’s capacity to introduce new
services and products (129th) or organizational models
(128th) that could help to transform its national economy
and lead it toward more productive activities.
Within the Commonwealth of Independent States,
several countries improve their performances, reflecting
the key importance and hopes they have placed on ICTs
to diversify their economies and lead them toward more
knowledge-intensive activities.

Since the beginning of the worst financial and economic
crisis of the past 80 years, the global economy has
experienced a change in its traditional growth patterns.
Advanced economies exhibited negative or sluggish
growth, while emerging markets, and notably the BRICS
economies—Brazil, the Russian Federation, India, China,
and South Africa—continued to show robust growth.
Several different reasons may explain these growth patterns
for emerging markets; among them are the development
of stronger domestic markets, an increase in the price of
commodities, and access to more and better financing
thanks to higher capital in-flows.
Notwithstanding this progress of the BRICS, we have
recently observed that many of these emerging economies
are experiencing difficulties in maintaining the rapid economic
growth of these past years. Many of the favorable conditions
fueling that growth have begun to fade away; this can
have consequences not only for these particular countries,
but—given their size and increasing importance in an
interconnected world—also for the global economy.
In order to support sustained and stable growth in the
long term, emerging markets must increase their levels of
productivity, which they can do in two ways: by introducing
the necessary reforms that will improve the functioning
of their markets and boost their innovation potential, and
by better leveraging their digital ecosystems. In terms of
the latter, an analysis of the Networked Readiness Index
(NRI) results for the past years shows that, in general, little
progress has been achieved.
1. Political and
6. Individual usage
usage
usage
3. Infrastructure
and digital
content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
Figure A: The NRI in BRICS economies, 2012 and 2014
BRICS 2012
BRICS 2014
Box 3: Challenges faced by BRICS economies to fully leverage ICTs
(Cont’d)
Table A: The NRI in BRICS economies, 2012 and 2014
BRAZIL RUSSIAN FEDERATION INDIA CHINA SOUTH AFRICA
2012 2014 2012 2014 2012 2014 2012 2014 2012 2014
Rank Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank Value
Networked Readiness Index 65 3.9 69 4.0 56 4.0 50 4.3 69 3.9 83 3.8 51 4.1 62 4.1 72 3.9 70 4.0
Environment subindex 101 3.5 116 3.4 100 3.5 87 3.8 78 3.7 91 3.8 64 3.9 77 3.9 34 4.6 31 4.8
1. Political and
77 3.6 78 3.6 102 3.2 100 3.4 71 3.7 73 3.6 46 4.1 56 4.0 23 4.9 20 5.0
2. Business and
innovation environment
121 3.5 135 3.3 83 3.8 73 4.2 91 3.8 103 3.9 105 3.7 115 3.8 50 4.4 53 4.5
Readiness subindex 72 4.7 76 4.7 32 5.4 37 5.5 64 4.8 85 4.6 66 4.8 73 4.8 94 4.1 98 4.2
3. Infrastructure and
digital content
68 4.0 56 4.5 40 4.8 47 4.8 100 3.2 119 2.7 87 3.5 86 3.5 82 3.6 68 4.2
4. Affordability 67 5.3 91 5.0 17 6.2 14 6.4 1 6.9 1 7.0 42 5.7 60 5.6 94 4.6 112 4.0
5. Skills 86 4.7 91 4.6 53 5.2 64 5.1 100 4.3 101 4.0 57 5.2 59 5.2 101 4.3 97 4.3
Usage subindex 54 3.8 47 4.1 60 3.7 53 4.1 78 3.4 91 3.4 51 3.8 61 3.9 76 3.4 70 3.7
6. Individual usage 66 3.3 59 4.2 52 3.9 46 4.6 117 2.0 121 2.1 82 2.9 80 3.3 96 2.6 78 3.4
7. Business usage 33 4.0 41 3.9 83 3.4 84 3.4 47 3.8 51 3.8 37 4.0 44 3.9 34 4.0 30 4.2
8. Government usage 59 4.0 54 4.3 71 3.7 61 4.1 46 4.3 41 4.5 33 4.6 38 4.6 89 3.6 103 3.6
Impact subindex 53 3.7 57 3.6 73 3.4 44 3.9 52 3.7 60 3.6 41 4.0 56 3.7 81 3.3 89 3.3
9. Economic impacts 52 3.5 64 3.3 53 3.4 41 3.6 41 3.6 50 3.5 79 3.2 81 3.1 59 3.4 49 3.5
10. Social impacts 54 3.9 58 3.9 89 3.4 41 4.3 65 3.8 73 3.7 30 4.8 44 4.2 98 3.3 113 3.0

Kazakhstan is one of the most prominent of this
group, leading the regional rankings at 38th, five positions
up from the last edition. The country has improved its
ICT infrastructure (58th), which remains one of the most
affordable to access in the world (2nd)—an advantage
that is also reflected in stronger ICT uptake by individuals
(54th), with more than half of its population using the
Internet or owning a computer with an Internet connection
at home. The effort to upgrade the digital capacity of
the country has been led by a strong government vision
(27th) that recognizes the importance of promoting ICTs to
diversify an economy that otherwise continues to be very
reliant on the extraction of fossil fuels. Going forward, the
country needs to continue building and strengthening its
innovation system and the capacity of local companies
to innovate (now 74th) to improve the economic impacts
accruing from an increasing uptake of ICTs.
With a similar profile, Azerbaijan follows Kazakhstan
closely in the rankings as it positions itself at 49th place,
seven places up since the last edition. Improvements in
the country’s ICT infrastructure (55th), giving rise to much
higher ICT uptake—especially by individuals (61st)—have
yielded this positive result. Despite this advance, the
country continues to lag behind in terms of fostering
technological innovation, exhibiting low levels of overall
and ICT-related patents (75th). To some extent this lag
reflects the severe impediments existing in an incipient
innovation system (77th) and the insufficient quality of
its educational system (114th). Addressing these long-
term challenges over the coming years will be crucial
for Azerbaijan to diversify its economy and make it less
dependent on oil revenues while transitioning toward a
Similar to other countries in the region, and in
sharp contrast to other BRICS economies (see Box 3)
the Russian Federation moves up four positions to
enter the top 50 this year. Slight improvements in the
country’s ICT infrastructure, which has become more
affordable, along with higher rates of individual uptake
(46th), have resulted in this positive outcome. Despite
this progress, the country continues to suffer from a
fairly inefficient political and regulatory environment
(100th) and weaknesses in its innovation system,
including a poor quality educational system (85th) that
hinders its capacity to fully leverage ICTs to innovate,
either through new products and services (113th) or new
organizational models (93rd). It is also worth noting that,
although individual ICT uptake is rather good (46th), both
government usage (61st) and a poor government vision
for developing ICTs (102nd) as well as inferior business
uptake (84th) to support its innovation potential (64th)
and business activity (94th) remain in need of attention.
Within the region, Georgia at 60th place and
especially Armenia—which has one of the sharpest
improvements and reaches 62nd place this year—
continue on their positive path toward higher positions
in the rankings. In contrast, Ukraine, in the middle of a
difficult political and social context, drops to 80th place,
while the Kyrgyz Republic repeats its 118th position of
the past edition.
ASIA AND THE PACIFIC
With three economies from the region in the top 10
of the NRI rankings and several countries showing
improvement, Asia and the Pacific is very dynamic and
active in developing its ICT agenda. Yet a significant
digital divide persists between the most advanced
economies—such as the Asian Tigers and Japan—and
emerging economies and other trailing countries.
Regardless of their position on the development
ladder, all Asian economies have much to gain from
increased networked readiness. It will allow populations
Figure A compares the NRI scores by pillar for the
BRICS economies in the 2012 and 2014 editions. Overall, the
results have remained stable in virtually all pillars, and notably
so for the economic and social impacts accruing from ICTs.
The only exception has been a slight improvement in the
individual usage score, which reflects the significant effort
made to facilitate a broader access to ICTs for a wider share
of the population.
In comparative terms, however, this stability in the
scores represents a drop in the global rankings because
other economies are progressing faster in developing and
leveraging their ICT potential. Table A shows that since 2012—
with the exception of the Russian Federation, which rises
six places in the rankings—all other BRICS economies have
dropped over time, in some cases significantly, and are now
classified around the middle of the overall rankings. A more
nuanced analysis of the different subindexes confirms that
this drop in rankings is particularly important for economic
and social impacts, where only the Russian Federation
manages to improve in both score and rank.
Reversing this situation will require the adoption of
holistic strategies that support better development of ICT
infrastructure and higher uptake of ICTs throughout these
countries, including in rural areas. Although the vast expanse
of these nations may represent a challenge, they should
continue to invest in strengthening their ICT infrastructures
and facilitating wider access to them. In addition, and very
importantly, the conditions needed to better leverage ICTs need
to be improved by integrating these technologies better into
more robust innovation and entrepreneurial ecosystems that
can support higher economic and social returns. Strengthening
the institutional framework to support the development of ICTs,
improving the quality of the educational systems and of the
scientific and technological base in the country, and supporting
interactions between research institutions and local companies
will be thus be key going forward.
Box 3: Challenges faced by BRICS economies to fully leverage ICTs (cont’d.)

of the least advanced among them to gain access to
much-needed basic services, to improve government
transparency and efficiency, and—for the most
advanced—it will contribute to boosting their innovation
capacity and allow them to attain higher levels of
competitiveness.
Taiwan, China, slips to 14th place in this edition
despite a stable performance in terms of score. This
relative drop in the rankings is primarily the result of
improvements in other countries. Taiwan remains at
the frontier both as a high-tech manufacturer and as
a technology-driven economy, with strong usage of
ICTs among all society’s stakeholders. In addition, the
economy manages to improve its already developed
infrastructure (5th) by, for example, expanding its
international Internet bandwidth by a significant amount
and by making access to ICTs more affordable (53rd).
Consequently, the economy scores strong social (6th)
and economic impacts (12th), although these could be
increased further by addressing some weaknesses in its
political and regulatory environment (34th). This remains
Taiwan’s main area for improvement, especially with its
lengthy procedures to enforce contracts (131st).
Japan moves up five positions to attain 16th place
this year. Although a link between this improvement and
its current economic outlook cannot be established,
renewed business confidence in the political environment
may have contributed to this progress. In addition,
Japan has achieved marginal improvements in its ICT
infrastructure, which has become more affordable,
and maintains its competitive advantage in the high
innovation capacity of local firms (4th). Technology and
innovation continue to play a key role in making Japan
one of the most productive economies worldwide;
these have managed to extend to society, as social
impacts have continued to improve (23rd). Yet further
improvements in social and economic impacts could be
achieved by incentivizing a more dynamic environment
that could, for example, foster innovation through
new organizational models (37th). A more conducive
institutional framework, especially the general business
and innovation environment (40th), could contribute to
delivering better results and boost competitiveness.
Australia occupies the 18th rank and is stable since
last year, despite an improved score. The country registers
a sharp improvement in the affordability of ICTs (49th) and
in some notable aspects of individual usage, such as the
penetration of broadband subscriptions. According to
ITU, the increase in smartphone usage is leading to more
handset data download because owners of smartphones
are more likely to purchase goods, access video and
audio content, pay bills, and use other online services.10
This increased usage is partially reflected in some aspects
of their economic impacts through the creation of new
services, new products, and new organizational models.
Compared with individuals, businesses and government
are less dynamic in taking up ICTs.
Neighboring New Zealand (20th) shows a stable
performance in the rankings with a slight advancement
in score. The country’s regulatory and business
environment remains its strongest competitive advantage
(2nd overall in the environment subindex, just behind
Singapore). New Zealand ranks 1st for the independence
of its judicial system and 1st in both the number of days
and the number of procedures to start a business. The
excellent skill base of its population (6th) also contributes
to the country’s ability to properly use and leverage a
fairly good ICT infrastructure, although it remains rather
pricy (127th), constituting New Zealand’s main weakness.
Malaysia is also stable (30th) and confirms its
leadership as the highest ranked economy in Developing
Asia. Malaysia maintains relatively competitive
regulatory (25th) and business (24th) environments,
and its government continues to use ICTs extensively
(9th), highlighting the high priority of this sector in the
government’s agenda. Business usage (27th) is also
strong, as firms invest to adopt new technologies and
make the effort to become increasingly innovative. The
combination of a favorable environment and an overall
high level of ICT usage results in high positive economic
(30th) and social (25th) impacts. However, individual
usage (49th), although improving in many dimensions,
has yet to expand so that ICTs become a widespread
technology in Malaysian households. This will certainly
increase as the economy develops, but further
investment in infrastructure and digital content (71st) are
needed to ease access and foster even higher economic
and social impacts.
China falls four places in the rankings this year and
occupies the 62nd position overall, despite an increase
in its overall score. The country is slowly improving its
innovation potential but still lags behind in leveraging
the full potential of ICTs. For example, despite an
increase in patent applications, the overall level remains
relatively low (32nd) with just 11.5 applications per
million population. Individual usage is also growing,
which explains most of the progress in China’s score.
However, only just over 40 percent of individuals use the
Internet on a regular basis and there are only 13 fixed
broadband Internet subscriptions for every 100 people
(51st). Mobile broadband Internet has registered more
substantial growth, but its penetration is still low, with 17
subscriptions per 100 population (76th). Consequently,
individual usage in China still ranks low (80th), trailing
behind the level of ICTs used by other stakeholders:
business (44th) and government (38th). Certainly the vast
size of the country and its proportion of rural population
does not allow for rapid improvement in ICT usage
and infrastructure build up, yet China needs to fill the
gap to meet its ICT potential. Some of the constraints
to better leverage ICTs that have been faced by the
country in the past are not improving. The institutional
framework does not lead to higher ICT uptake (56th),
and the business environment (115th) witnesses a relative

worsening because of excessive bureaucracy and red
tape, high taxes (135th), and delayed availability of new
technologies (107th), at least at the national, aggregate
level. In terms of readiness, China is making an effort to
improve its infrastructure, with augmented production
of electricity and higher international Internet bandwidth
capacity and server security. Yet performance in these
dimensions is still relatively low because improvements
hardly keep up with the country’s rapid development.
Moreover, progress on the construction of hard ICT
infrastructure is counterbalanced by a diminished
accessibility of digital content (67th). And although the
skill base of the workforce is growing (for instance,
participation in secondary education is growing to
reach 86.6 percent of its population), it is not growing
as quickly as in other competing economies, and the
country attains a relatively low 59th position on the skills
pillar. Furthermore, the affordability of ICTs has dropped
to 60th place, representing the main area of decline. All
these limitations combine to realize only low economic
impacts (81st), allowing a limited impact of ICTs on new
services and products and a low share of the workforce
employed in knowledge-intensive activities. Further
and sustained efforts should be made to unleash the
innovative potential of ICTs, both through investment in
capacity building and infrastructure and through a more
open and creative environment that could foster new
ideas and business models.
Since the last assessment, Indonesia climbs
12 places to attain 64th position, the third best result
among members of the Association of Southeast Asian
Nations (ASEAN) after Singapore and Malaysia.11
This
result is achieved thanks to balanced improvements
across the board rather than to a sharp increase in one
particular area, although more significant progress takes
place in the environment subindex. Both the political
and regulatory environment (68th) and the business
environment (62nd) are improving. The former is driven
by stronger perceptions about the effectiveness of the
country’s institutions, with enhanced intellectual property
protection (55th), a more efficient legal system (49th),
and better-developed ICT regulations (46th). The latter
is driven by stronger local competition (66th, up several
positions since last year) and the greater availability
of the latest technologies (60th), while venture capital
availability remains high (17th) and slightly improving as
well. However, further advancements can be achieved
in this area, as excessive red tape continues to limit the
creation of new businesses. Indonesia’s readiness also
improves. While affordability remains one of its main
strengths (37th), progress in skills and infrastructure
add to the country’s fairly solid performance. After
having achieved full mobile network coverage last year,
its international Internet bandwidth capacity improves
significantly to reach 77th place. In terms of skills, the
quality of its educational system is improving as well
as its population’s participation in higher education,
with a secondary education enrollment rate of 81
percent. Yet readiness can be further enhanced by
continuing investing in education and especially in ICT
infrastructure, where the capacity of secure Internet
servers (105th) and electricity production (104th) remain
insufficient to sustain future ICT development. Usage,
also on a positive trend, has stayed more stable.
Across stakeholders, businesses (36th) lead, with
companies quickly absorbing the latest technologies
and increasingly integrating ICTs in their daily activities.
Businesses are followed by the government (49th),
while households continue to lag behind (95th). The
penetration of mobile phones passed 100 subscriptions
per population a few years ago, mobile broadband
subscriptions are rapidly evolving, and social networks
are popular. However, the low usage of the Internet
(112th) and the scarce availability of personal computers
(103rd) and home Internet connections (117th) still
reflect gaps in the fulfilment of Indonesia’s ICT agenda.
Consequently, economic and social impacts (86th and
63rd, respectively) are increasing but still low compared
to the tremendous improvements registered in the few
years. It is hoped that the benefits of ICTs are building up
and will be secured in the near future.
Thailand, similar to most other ASEAN members,
improves its performance this year, reaching 67th place
in the rankings. Despite the wide gap vis-à-vis Singapore
and, to a lesser extent, Malaysia, Thailand exhibits
progress in all the subindexes. Its main strengths lie in
its relative affordability of ICTs (47th) and its business
and innovation environment (45th). However, in both
these pillars Thailand alternates good results with areas
for improvement. For example, in terms of affordability,
the accessible mobile phone tariffs (30th) are
counterbalanced by less competitive broadband Internet
prices (86th). Similarly, the business and innovation
environment includes a high level of local competition
(41st) as well as low government procurement of
advanced technology (105th). Business usage is another
area of relative strength (59th), thanks to the widespread
use of ICTs for consumer transactions (50th) and also
to active technology absorption (50th). Individual usage
of ICTs still lags behind (85th), yet it shows significant
improvement with a good penetration of mobile
phones (38th) and usage of virtual social networks
(49th), comparable to those of advanced economies.
Yet, aside from mobile telephony, other technologies
remain relatively scant, especially the breadth of
mobile broadband technologies (132nd). Moreover, the
institutional environment does not seem to be particularly
conducive (79th) and the government does not appear
to be particularly eager to push the digital agenda
nationwide (84th). The resulting economic impact is
therefore not yet satisfactory (104th), with few ICT PCT
applications, few organizational models, and a low share
of the workforce in knowledge-intensive activities.

Sri Lanka, another ASEAN economy, drops
seven places to take the 76th position in the rankings,
but its score continues on a positive upward trend.
The environment subindex, both in its political and
regulatory component and in its business and innovation
component, loses some ground, yet this is compensated
for by stronger usage, especially among business (50th)
and government (43rd) stakeholders. Individual usage is
also improving, but because it is starting from a very low
base (112th) it still needs to fill important gaps across the
board, while the country’s infrastructure (104th) demands
sustained investments to support the ICT sector
adequately.
India is the least performing of the BRICS
economies and is continuing on its declining trajectory
to arrive at 83rd place in this edition. The drop in
rankings can be traced back mainly to difficulties in
improving historical limitations and keeping up with other
emerging economies in several dimensions. Overall,
India’s networked readiness profile remains hindered
by the quality of its political, regulatory, and business
environment (91st) and its lack of digital infrastructure
(119th), which is reflected in low individual usage (121st)
and wide gaps in education participation that limit
the creation of a wide skill base (101st). Red tape and
corporate tax continue to create a difficult environment
for businesses to operate, with almost no improvements
since the last assessment. On this dimension—
despite the positive availability of venture capital (27th),
competitive local markets (24th), the availability of the
latest technologies (58th), and improving perceptions
of judicial independence (40th)—bureaucracy and
administrative costs are extremely burdensome. For
example, enforcing contracts remains a long process,
both in terms of number of procedures (134th) and time
(146th). In terms of readiness, the development of the
country’s infrastructure proceeds slowly: for example,
electricity production expands, but remains insufficient
overall (101st); the number of secure Internet servers also
increases but remains low (108th). However, the most
worrisome signals of insufficient progress on the digital
agenda come from the lack of skills buildup, with dismal
progress made in secondary education participation
(68.5 percent, ranking 105th) and literacy rate (127th).
The inadequate diffusion of basic skills required
in a knowledge society is certainly a drag on ICT
development and hinders leveraging the yet-untapped
innovation potential of large, young Indian human
resources. The low level of ICT usage by households and
individuals reflects the strong human, infrastructure, and
digital divides that characterize India. The penetration
of mobile phones is low (69.9 percent) and not growing,
while the numbers of Internet users (12.6 percent),
households with a personal computer (10.9 percent),
and mobile broadband subscriptions (5 percent) are
disappointing (all ranking below the 102nd position).
The main strength of India lies in its very affordable set
of ICT tariffs (1st) and its businesses’ capacity to adopt
new technology (48th). The government continues to
emphasize ICTs as a promising tool to address some of
the country’s priorities such as job creation, corruption,
red tape, and education. However, this vision has yet
to translate into a structural transformation of India’s
economy and society.
Improving its position both within the ASEAN group
and overall, the Philippines climbs eight places to
reach the 78th position. With a significant improvement
in its overall score, the country continues its positive
trend. The scores of all the 10 networked readiness
pillars register an increase. A significant improvement
in the perceived efficiency in the country’s legal system
and property rights protection drive the political and
regulatory environment up to 87th place. ICT readiness
is the other area where the Philippines improves the
most, thanks to a more affordable (75th) access to ICT
infrastructure and better skills (69th), despite the need
for higher quality in the educational system. Business
usage is, as in many other Asian economies, at a more
advanced stage (43rd) than individual usage (91st).
Progress made in terms of economic impacts registered
last year continues this year, moving up eight positions
and reaching 48th place. The role of ICTs in fostering
innovation by creating new products and services (42nd)
and organizational models (28th) is confirmed and
contributes to this promising result.
With a stable performance, Vietnam is overtaken
by the Philippines in the ASEAN group but remains
84th overall and marginally improves its score. The
affordability pillar is corroborated as the main strength
of Vietnam’s performance (8th), jumping 30 positions
since the last assessment. The business and innovation
environment is the other main area where Vietnam
progresses significantly, albeit from a low base (100th
this year). The other many shortcomings endure: the
poor overall quality of the political and regulatory
environment (91st) and ICT infrastructure (121st) limit the
expansion of the ICT sector, while available skills (88th)
show no signs of development. The usage of ICTs by
individuals (84th), businesses (88th), and government
(58th) remain stable, with little progress registered since
the last assessment. Such a lack of dynamism not
only is detrimental to the development of ICTs, but also
seriously undermines the country’s competitiveness
going forward.
The eight ASEAN members covered by the NRI
in the last edition improve their overall scores and a
majority make progress in the rankings as well, although
some continue to be located toward the lower end:
Cambodia (108th), the newly covered Lao PDR (109th),
and Myanmar (146th) close the regional rankings.
Finally, Bhutan, assessed for the first time this
year, ranks 94th; Pakistan is ranked 105th, down three
places; Bangladesh is 114th, losing one position; and
Nepal is 126th.

LATIN AMERICA AND THE CARIBBEAN
Improving the connectivity of the region continues to
represent one of its main challenges despite the recent
efforts of many countries to develop and update their
ICT infrastructures. Countries such as Chile, Panama,
Uruguay, and Colombia have made significant progress
in developing and ensuring more and better access to
ICT infrastructure, ensuring higher ICT usage across
stakeholders. However, persistent weaknesses in
the broader innovation system hinder the overall
capacity of the region to fully leverage ICTs to foster its
competitiveness potential, highlighting the rise of the
new digital divide—that is, the divide between countries
that are achieving positive economic and social impacts
related to the use of ICTs and those that are not.
Despite a slight increase in its overall score, Chile
drops one notch to 35th place while still leading the
regional rankings. As mentioned above, the country
relentlessly continues to develop its ICT infrastructure
and ensure higher ICT usage across stakeholders,
with one of the highest rates of Internet users (45th),
e-commerce (35th), and online government (24th)
services in the region. Despite this important progress,
weaknesses in its innovation system, which are reflected
in the relatively low capacity of Chilean companies to
innovate (63rd) and concerns about the quality of its
educational system (74th), especially math and science
education (107th), continue to hinder Chile’s capacity
to fully leverage ICTs to support innovation and the
transition to a knowledge-based economy.
Panama continues its ascent in the rankings,
moving up three spots to 43rd place. The country has
recognized the importance of ICTs as one of the key
sources of economic growth for the future both as an
industry and as an enabler for innovation, notably in
the service sector. This governmental vision (22nd) is
reflected in higher rates of ICT uptake by businesses
(39th) and individuals (68th), even if the reported figures
do not reflect the government’s efforts to provide
free universal Internet access. Notwithstanding this
progress, the country still suffers from weaknesses in
its educational system (75th), notably in important areas
for innovation such as math and science (114th), and in
its political and regulatory environment (62nd). Panama’s
overall capacity to innovate is also still low (50th). These
weaknesses hinder its ability to fully leverage its digital
potential to foster higher levels of competitiveness.
Stable at 53rd place, Costa Rica exhibits some
progress in ensuring higher ICT uptake, with an
important increase in the number of households with
access to an Internet connection (59th) and a higher
proportion of its population with mobile broadband
subscriptions (70th), thanks to efforts to guarantee
affordable (15th) access to the ICT infrastructure.
Notwithstanding these strengths, overall individual (64th)
and government (64th) usage remain a bit low, and some
weaknesses in the political and regulatory environment
(63rd)—notably in the effectiveness of law-making
bodies (140th) and the number of days to enforce a
contract (123rd), as well as the conditions needed to
boost innovation and entrepreneurship (70th)—affect
the country’s capacity to leverage its ICT potential to
foster innovation and ensure the transition toward a
knowledge-based economy (65th).
Barbados suffers a significant drop in the rankings,
falling 16 positions to arrive at 55th place. This decline
is driven by sharp increases in the price of accessing its
ICT infrastructure and a drop in the economic impacts
derived from ICT usage (57th). Overall, the Caribbean
island continues to boast an excellent educational
system (6th) that, coupled with a relatively favorable
environment for business (38th), could result in higher
economic impacts than those yet achieved. However,
weaknesses in its innovation system, where companies
report low levels of capacity to innovate (81st) and
difficulties in accessing venture capital financing
(98th), along with long and cumbersome procedures
to start a business (94th), continue to hamper its
innovation potential and thwart its ability to leverage
the digital ecosystem. Going forward, addressing
these weaknesses while continuing to improve its
ICT infrastructure and uptake would result in greater
economic and social impacts.
Despite the improvement in score that reflects the
effort Uruguay has made to boost its ICT infrastructure
and uptake over the past years, because other countries
are progressing more quickly the country drops four
places to land at 56th place. As in past years, Uruguay
continues to improve its ICT infrastructure (50th), which
has become more affordable (80th), resulting in higher
levels of uptake by individuals (48th). Notwithstanding
this progress, the country continues to suffer from
weaknesses in its overall innovation system: of concern
is the quality of its educational system (120th) and its
capacity to provide the necessary skills for a changing
economy, as well as its low capacity to innovate (88th).
This situation results in modest economic impacts (61st)
in terms of fostering innovation and ensuring a faster
transition toward a knowledge-based economy (66th).
Colombia moves up three positions to reach 63rd
place this year. Improvements in its ICT infrastructure,
which has become relatively more affordable (44th),
and in individual uptake (77th)—with a larger number of
Internet users (66th) and households with a personal
computer (74th) and an Internet connection (75th)—
have allowed for this positive result. Notwithstanding
this progress, the country continues to suffer from
poor framework conditions for entrepreneurship
and innovation (104th) and from weaknesses in its
educational sector, notably in the quality of math and
science education (108th), resulting in a poor capacity
to innovate (83rd) and a small share of its population
engaged in knowledge-intensive jobs (89th).

Despite a slight increase in NRI score thanks to
improvements in its ICT infrastructure (56th), Brazil
drops nine positions to arrive at 69th place because
other economies have been faster to embrace the digital
revolution. Overall, the country exhibits relatively high
levels of ICT usage, with about half of its population
using the Internet, a well-developed e-commerce
industry (30th), and a government committed to offering
a significant number of its services online (32nd),
resulting in fairly good citizen e-participation (31st).
However, a poor business and innovation environment
(135th), coupled with weaknesses in its educational
system (121st)—notably in the area of math and science
(136th)—hampers the full attainment of the economic
impacts that ICTs can provide (64th). Only a small
proportion of its population is engaged in knowledge-
intensive jobs (75th).
After a couple of years of important improvements
in the rankings, Mexico does not consolidate past
gains and falls 16 positions to reach 79th place.
Despite some progress in expanding and upgrading
its ICT infrastructure (81st) and uptake by individuals
(89th), this is insufficient to catch up with advances in
other economies, and thus Mexico does not manage
to digitally converge with more-advanced economies.
The cost of accessing its existing ICT infrastructure
remains high (93rd) and the quality of its educational
system (119th) continues to pose a severe challenge
to providing the country with the skills necessary
required for a changing and more digital economy. All
this results in low ICT usage levels by both individuals
and businesses, in spite of the government’s significant
efforts to offer many of its services online (28th), thereby
enabling good rates of citizen e-participation (25th). In
addition, its innovation ecosystem needs strengthening
and, in general, Mexican companies have a low capacity
to innovate (75th), resulting in low economic impacts
(80th) and a population that concentrates largely on
low-productivity activities and few jobs considered to be
knowledge intensive (97th). A full implementation of the
country’s digital agenda and addressing the persistent
weaknesses in the innovation system should help in
resolving several of these persistent challenges.
Peru jumps 13 places to attain 90th position,
thanks to some improvement in its ICT infrastructure—
for example, greater access to international Internet
bandwidth and higher individual uptake. Despite this
progress, the country continues to lag significantly
behind in terms of its capacity to fully leverage ICTs to
build its competitiveness and modernize its economy,
which continues to rely heavily on mining. Overall,
weaknesses in its educational sector (134th), which does
not seem to provide the right set of skills, coupled with
relatively low deployment of its ICT infrastructure (95th),
result in low levels of ICT uptake by both individuals
(94th) and businesses (89th). In addition, weaknesses
in enabling conditions for leveraging digital capacity
for innovation (93rd), along with a weak political and
regulatory environment (119th) and an excessive number
of days to open new businesses (103rd), result in a
poor innovation capacity (106th) and an economy that
is not able to offer many knowledge-intensive jobs
(94th). Improving the development of ICT infrastructure
and access to it, coupled with better conditions and
interaction with other innovation-related investments—
such as education, training, and R&D—would help to
improve the situation going forward.
Argentina drops one position to reach 100th place.
In general, the country boasts few changes since last
year. Although the development of an expensive (121st)
ICT infrastructure (78th) and uptake by individuals (57th)
present values above the Latin American average, the
severe weaknesses in its business environment (135th)
and concerns about the quality of its educational
system (104th) and its ability to provide the workforce
with the necessary skills for the economy result in this
disappointing position.
Finally, Paraguay (102nd), Venezuela (106th),
Honduras (117th), Bolivia (120th), Nicaragua (124th),
and Haiti (143rd) close the regional rankings. These
countries all suffer from both important weaknesses in
the development of their ICT infrastructures and a lack of
the innovation and entrepreneurial conditions that could
help them fully leverage them.
SUB-SAHARAN AFRICA
Sub-Saharan Africa slowly continues to develop its ICT
infrastructure, especially by expanding the share of the
population covered by, and having access to, mobile
telephony and by expanding the number of Internet
users, which in some countries—such as South Africa—
has almost doubled. These improvements have led
to many important innovations that provide more and
better services that were previously unavailable, such
as financial services. Notwithstanding this progress,
the region overall continues to suffer from a relatively
poor ICT infrastructure, which remains costly to
access, although some notable exceptions exist. More
importantly, severe weaknesses persist in the region’s
business and innovation ecosystems, which result in very
low positive economic and social impacts. Addressing
these weaknesses, not only by developing a more solid
ICT infrastructure but also by improving the framework
conditions for innovation and entrepreneurship, will be
crucial to avoid the emergence of a new digital divide
that will be evident in a disparity of the economic and
social impacts associated with what has been called the
digital revolution.
Mauritius recovers the ground lost last year and
moves up seven positions to attain 48th place. Gains
across the board—most notably in terms of a better ICT
infrastructure and skills base, along with a higher level
of individual usage—have led this improvement. Overall,
the government’s vision (31st) to develop ICTs as a key

sector to support the economic development of the
island, coupled with a positive political and regulatory
environment, have resulted in improvements in an
affordable (11th) ICT infrastructure and higher levels of
ICT users, even if less than half of its population uses the
Internet or has a computer with an Internet connection at
home. Improving the economic impacts (70th) accruing
from a higher use of ICTs will require resolving some
important weaknesses in the innovation system, such
as the capacity of local companies to innovate (72nd),
which is still considered low.
Despite some important improvements in the
penetration of ICTs among individuals, which is reflected
in a higher NRI score, South Africa remains stable at
70th place. In the past year, an expensive (112th) ICT
infrastructure (68th) has exhibited little progress. In using
ICTs, the business community (30th) seems to have
taken the lead, using ICTs vigorously in interactions with
other businesses (30th); this business-to-business use
seems much more developed than interactions with
consumers (62nd). On the other hand, the government
(103rd) lags substantially behind in embracing ICTs,
offering few online services (80th), which in turn results in
low social impacts (113th). Weaknesses in the innovation
system, notably in terms of skills development (97th), also
affect the country’s economic potential (49th) despite its
fairly robust political and regulatory environment.
In the bottom half of the rankings, Rwanda moves
up three positions to reach 85th place, regaining some
of the ground lost last year. Overall, the country depicts
a profile similar to that of previous years. It makes little
progress in improving its very expensive (128th) ICT
infrastructure (108th), which results in a low uptake
by its population (138th) despite the clear vision of
the government (5th) to promote ICTs in the country.
Weaknesses in its innovation system and the low
share of its population that graduates from secondary
education (139th) also affect its capacity to fully
leverage ICTs to boost innovation (93rd) or increase its
population’s online participation (112th).
Despite some very significant efforts to boost its
ICT infrastructure and ICT uptake by its population,
Kenya remains stable at 92nd place. As in the case
of Rwanda, despite a strong government vision (26th)
to develop ICTs, the actual uptake by its population
(113rd) continues to remain very low, albeit increasing:
only around 10 percent of households have a computer
(113th) or an Internet connection (103rd). Weaknesses in
the innovation and entrepreneurial environment (110th)
and a fairly low skills base (98th) also affect the capacity
of the country to fully achieve the potential benefits
accruing from ICTs.
In East Africa, Zambia (110th) and Uganda
(115th), swapping places from last year, and Tanzania
(125th) continue to lag behind in developing their ICT
infrastructures, promoting higher ICT uptake, and finally
benefiting from the economic yields associated with
it. The situation is similar in West Africa, where many
countries—such as Senegal (113th), Gabon (128th), and
Cameroon (131st)—remain at the bottom of the rankings
as a consequence of both the insufficient development
of their ICT infrastructures despite important progress
made in their mobile telephony uptake, and weaknesses
in their innovation systems that result in a low capacity to
boost their overall competitiveness. Efforts to close the
digital divide in these countries should focus not only on
developing their ICT infrastructures but also on improving
the framework conditions for innovation in order to avoid
the perpetuation of the gap in economic and social
impacts that constitute the new digital divide.
THE MIDDLE EAST AND NORTH AFRICA
As in previous years, the region depicts a highly
diversified outlook in terms of the capacity of countries
to leverage ICTs to boost competitiveness and well-
being. On the one hand, Israel and several Gulf
Cooperation Council states have continued their efforts
to improve ICT uptake and integrate ICTs better in
more robust innovation ecosystems in order to obtain
higher returns.12
On the other hand, many countries
in North Africa continue to lag behind and suffer from
important weaknesses in their framework conditions and
overall innovation capacity that prevent them from fully
leveraging ICTs and obtaining higher returns.
Israel repeats its position at 15th place, leading
the regional rankings with a stable profile. The country
continues to boast a fairly good ICT infrastructure (29th)
that remains affordable (35th) and results in very high
levels of ICT usage (14th) across all agents. Around
three-quarters of the country’s households count on a
personal computer (21st) and Internet connection (29th)
at home and are Internet users (31st), and more than
half of its population has access to mobile broadband
(26th). The government has also made a significant
effort to offer its services online (15th), and e-commerce
(23rd) is fairly well developed. In addition, the country
benefits from a rather skilful labor force (39th), despite
some concerns about the quality of education (56th),
that—coupled with favorable conditions for innovation
and entrepreneurship (14th) and a high capacity
of companies to innovate (4th)—result in very high
technological capacity, as evidenced by the high number
of overall patents (5th), notably in ICT-related fields (4th).
Qatar remains stable at 23rd place and leads
the rankings in the Arab world. In the past year, the
country has continued to improve and upgrade its
ICT infrastructure (31st) and uptake (18th), thanks
to a decisive effort led by the government’s strong
vision (3rd) that has identified ICTs as one of the key
industries that will diversify the local economy and
boost the productivity of all sectors. Qatar is among
the top 10 in the world in terms of Internet users (9th)
and households having access to a computer (8th) and
Internet connection (10th), which has become almost

universal and has helped to achieve very high social
impacts (8th). Economic impacts (32nd), while improving,
could be higher. Technological innovation (46th) remains
modest, and just a quarter of its population is employed
in knowledge-intensive jobs (61st). Continuing to address
some of the weaknesses in its innovation system, which
is quickly evolving and strengthening, would result in a
higher technological potential.
The United Arab Emirates continues to move
up in the rankings, this year by one position, to reach
24th place. Improvements in its ICT infrastructure
(30th) and ICT uptake by individuals (29th) have led to
greater economic impacts (27th) and thus the rise in
the rankings. As in Qatar, the government has a strong
vision (1st) to develop ICTs as one of the key industries
to diversify the local economy; this is reflected in the
already high and rapidly increasing levels of ICT uptake
across all stakeholders. More precisely, 85 percent of its
population use the Internet (14th) and have access to a
personal computer at home (18th); government services
are largely available online (9th) and e-commerce is
relatively well established (20th). Benefiting from a
pro-business environment, the country also obtains
fairly good economic impacts (27th), even though its
technological innovation capacity remains low (49th).
Sustaining efforts to strengthen its innovation ecosystem
will be important going forward in order to boost
the potential results of a fairly well developed digital
ecosystem.
Stable at 29th place, Bahrain continues to depict
a robust performance. Although ICT infrastructure,
especially in terms of international Internet bandwidth
(74th), may not be as well developed in Bahrain as in
other countries, it is less costly to access (25th) and
uptake by individuals is one of the highest in the world
(14th), with a very high number of Internet users (10th);
the number of households with a personal computer
(3rd) is similar to that of the Nordic countries. As for
other countries in the region, the government has a
strong vision to develop the sector (14th) and offers a
wide range of services online. Notwithstanding these
strengths, Bahrain suffers from an overall low capacity to
innovate (82nd), which reflects persistent weaknesses in
its innovation system. Along with some concerns about
the quality of its educational system (48th), notably in
math and science (77th), these weaknesses hamper
the country’s capacity to obtain higher economic
impacts (63rd) and transition toward a knowledge-based
economy (74th).
Despite some significant improvement in the
uptake of ICTs by individuals and development in its
infrastructure, Saudi Arabia falls one position to reach
32nd place. Overall the country depicts a very stable
profile compared with that of previous editions. Similar to
others in the region, the government (6th) is leading the
effort to digitally connect and advance the country, while
individual uptake (44th), despite recent improvements,
and business uptake (34th), with a limited development
of e-commerce (54th), lag a bit behind. Notwithstanding
a fairly business friendly environment (23rd) that should
continue its efforts to cut red tape (107th), weaknesses in
its innovation system do not allow the economic impacts
that ICTs could bring (37th). Going forward, strengthening
its innovation system through more and more efficient
investments to foster the scientific and technological
capacity of the country will be important to increasing
the share of its population working at knowledge-
intensive jobs ( 67th) and helping the transition from a
resource-based economy toward an innovation-driven
one.
In the Levantine, both Jordan and Lebanon
improve their scores, but while Jordan moves up three
positions to 44th place, reaffirming its leadership in the
area, Lebanon drops three to 97th place.
In North Africa, countries—except Algeria—suffer
significant drops and are positioned in the lower half of
the rankings, illustrating the difficulties they face if they
are to fully develop their ICT potential and leverage it to
obtain great social and economic impacts. Tunisia, at
87th place, leads the area, followed by Egypt, Morocco,
and Algeria at 91st, 99th, and 129th place, respectively.
Egypt, at 91st place, falls 11 positions despite a
slight improvement in individual ICT uptake (71st), which
has nevertheless been smaller than the improvement
seen by other countries that have evolved faster. Overall,
Egypt continues to lag behind in terms of developing
its ICT infrastructure (99th), although it remains
fairly affordable (16th). ICT uptake by government is
average (42nd), but penetration among citizens (71st)—
with less than half its population using the Internet
(75th)—and businesses, in their interaction with other
businesses (81st) and consumers (70th), remain modest.
Weaknesses in the political and regulatory environment
(115th) and the business and innovation environment
(117th) result in a low innovation capacity by Egyptian
companies (111th) and thus limited economic (59th) and
social (65th) positive outcomes.
Falling 10 positions, Morocco, at 99th place, barely
ranks among the first 100 analyzed economies. As in
the case of Egypt, ICT infrastructure (93rd) has slowly
improved. This is also the case of individual ICT uptake,
although progress in Morocco has been slower than
in other countries that are moving faster. Overall, the
country’s economic (123rd) and social (115th) impacts
remain very low, partly as a consequence of the relatively
low ICT uptake, partly because of the poor conditions for
boosting innovation and entrepreneurship (88th) in the
country, and partly because of its low skills base (111th).
In comparative terms, individuals are more advanced in
using ICTs, with more than half of its population using
internet (57th), than both businesses, where e-business
still lags behind, and the government, which notably
scores poorly in terms of its offerings of online services
(125th).

After a sharp drop in the past edition, Algeria
manages to move up two positions to reach 129th
place. With very poor general conditions for business
and innovation development (145th), a poorly developed
ICT infrastructure (127th), and very low ICT penetration
across all stakeholders, it is not surprising that the
country does not achieve higher economic (133rd) and
social (140th) impacts.
CONCLUSIONS
With the advent of the information revolution, ICTs have
become ubiquitous and the world hyperconnected,
deeply transforming the economic and social
relationships across stakeholders. In this environment
of fast-paced change, a new form of asset that can
be thought of as the gold or oil of previous economic
revolution periods has emerged: data. Large amounts
of data, often referred to as big data, are constantly
generated both in a structured and non-structured
manner. Thanks to advances in ICTs, the volume and
velocity of generation of these data are unprecedented,
as is the capacity of organizations to capture and treat
them, potentially generating great economic and social
value. However, success in extracting this value requires
more than just the generation of or access to big data.
Organizations, both public and private, need to decide
how to acquire, treat, and interpret these data. This
will frequently require new management philosophies
and organizational structures capable of adapting and
benefiting from the new market opportunities. At the
same time, the potential of big data to be misused is
also increasingly becoming a source of concern. Privacy
issues, and sometimes concerns about geopolitical and
strategic matters regarding national security, have been
raised. Measures that can build resilience and avoid
these perils will need to be developed.
Against this backdrop, for the past 13 years, the
Networked Readiness Index (NRI) of the GITR series has
contributed to better understanding and measuring the
determinants and impacts that ICTs can make, analyzing
national conditions and stakeholders’ readiness to fully
leverage the potential that ICTs unveil. An analysis of
the digital landscape confirms some of the key findings
that have been presented in previous editions. Overall,
the digital divide between advanced economies and
emerging and developing ones persists. This is notable
especially in terms of the economic and social impacts
that ICTs can provide and that characterize the changing
nature of this digital divide. The NRI results show that
many developing and emerging countries have made
significant attempts to develop their ICT infrastructure—
mainly by increasing mobile telephony, which has
become increasingly available for a large share of the
population and has resulted in new services, such as
financial services, that were previously unavailable.
However, progress in building and upgrading the
enabling infrastructure to allow for more and higher
quality Internet connections or to expand ICT uptake has
been slower, especially in the least-developed countries.
In addition, these countries suffer from unfavorable
business and innovation conditions and weaknesses in
their educational systems, hindering their capacity to fully
leverage the existing ICT uptake and resulting in lower
innovation and competitiveness capacity. This situation is
particularly worrisome in sub-Saharan African countries.
On a more positive note, some countries, notably in
the Commonwealth of Independent States and the Gulf
Cooperation Council, have made significant progress
over the past years, channeling many of the fossil
fuel revenues toward ICT investments as a strategy to
diversify their economies and make them less dependent
on volatile international energy prices.
Another trend that is confirmed by the results is that
large intra-regional disparities persist across all regions:
from Latin America to Asia and the ASEAN countries,
from the Middle East and North Africa to Europe and
the EU countries. In Latin America, the regional rankings
continue to be led by Chile, Panama, Costa Rica, and
Uruguay—countries that portray relatively good results,
albeit with some weaknesses in their overall innovation
systems, in terms of increasingly developing their
digital ecosystems. On the other hand, little progress is
recorded for countries such as Brazil, Mexico, Argentina,
Bolivia, and Venezuela. Within Asia and the ASEAN
countries, the differences are also stark. Although
Singapore continues to be at the forefront of the global
rankings, Malaysia is the only other economy from the
region that manages to score within the top 30, followed
distantly by Indonesia and Thailand just above the top
half of the rankings; Pakistan, Bangladesh, and Nepal
come in toward the bottom of the rankings. In the Middle
East and North Africa, the differences have become even
more pronounced. While many countries from the Gulf
Cooperation Council, especially Qatar and the United
Arab Emirates, have continued their decisive progress
toward strengthening their digital and innovation
ecosystems, countries in North Africa continue to suffer
from significant challenges to promoting ICT uptake
and from poor conditions that present obstacles to
integrating ICTs and leveraging them to boost innovation
and competitiveness. Finally, in Europe, notably even
within the European Union, the differences are also
significant. A deeper analysis reveals that differences
in terms of fostering ICT infrastructure and uptake are
not so prominent—to a large extent, this is thanks to the
efforts of the European Commission to develop a robust
digital infrastructure throughout the Union. However,
differences across countries in the conditions under
which innovation can occur affect their capacity to fully
take advantage of the existing infrastructure. Efforts to
continue bettering these conditions for innovation will be
key to reducing this new digital divide going forward.
Finally, of special importance given their size and
influence in the global economy, is the situation of the

BRICS economies—more precisely, their inability to
make decisive progress in developing and leveraging
their ICT potential. Overall, although the situation
differs across the five economies, they all seem to face
difficulties in developing and benefiting from their digital
potential. While their vast geographical expanse may
hinder their capacity to quickly and more economically
develop their ICT infrastructure and reach out to larger
shares of the population, weaknesses in their innovation
systems persist despite the many efforts to mitigate
these limitations. This situation hampers their potential to
benefit from the economic and social benefits that ICTs
could bring about to boost their competitiveness and
allow them to transition toward full-fledged knowledge-
based societies.
The GITR series and the NRI provide a
comprehensive analytical framework for assessing not
only the progress made in raising ICT connectivity in
different countries, but also—and more importantly—the
progress made in obtaining the desired economic and
social impacts that higher connectivity and the rise of
big data can yield in generating growth and high-quality
employment in a rapidly changing context. Designed
and produced as a framework for multi-stakeholder
dialogue, it also serves to identify and define policies
and measures that can catalyze change toward better
leveraging ICTs and achieving their full potential.
NOTES
1 Alexander 1983.
2 Google, no date, Explore Flu Trends Around the World, available at
http://www.google.org/flutrends/.
3 Schaefer et al. 2011.
4 Gawande 2011.
5 Parry 2012.
6 The Economist Intelligence Unit 2013.
7 See Lanier 2010; see also Kakutani 2010.
8 Browne et al. 2013.
9 See European Commission, 2010a, b.
10 ITU 2013, Box 2.4.
11 The members of the Association of Southeast Asian Nations
(ASEAN) are Cambodia, Indonesia, Laos PDR, Malaysia, Myanmar,
the Philippines, Singapore, Thailand, and Vietnam.
12 The six Gulf Cooperation Council states are Bahrain, Kuwait,
Oman, Qatar, Saudi Arabia, and the United Arab Emirates.
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This appendix presents the structure of the Networked
Readiness Index 2014 (NRI). As explained in the chapter,
the NRI framework separates environmental factors
from ICT readiness, usage, and impact. That distinction
is reflected in the NRI structure, which comprises four
subindexes. Each subindex is in turn divided into a
number of pillars, for a total of 10. The 54 individual
indicators used in the computation of the NRI are
distributed among the 10 pillars.
In the list below, the number preceding the period
indicates the pillar to which the variable belongs (e.g.,
indicator 2.05 belongs to the 2nd pillar; indicator 8.03
belongs to the 8th pillar). The numbering of the indicators
matches the numbering of the data tables at the end of
the Report.
The computation of the NRI is based on successive
aggregations of scores, from the indicator level (i.e., the
most disaggregated level) to the overall NRI score (i.e.,
the highest level). Unless noted otherwise, we use an
arithmetic mean to aggregate individual indicators within
each pillar and also for higher aggregation levels (i.e.,
pillars and subindexes).a
Throughout the Report, scores in the various
dimensions of the NRI pillars are reported with a
precision of two decimal points. However, exact figures
are always used at every step of the computation of the
NRI.
Variables that are derived from the World Economic
Forum’s Executive Opinion Survey (the Survey) are
identified here by an asterisk (*). All the other indicators
come from external sources, as described in the
Technical Notes and Sources section at the end of the
Report. These variables are transformed into a 1-to-7
scale in order to align them with the Survey’s results. We
apply a min-max transformation, which preserves the
order of, and the relative distance between, scores.b
NETWORKED READINESS INDEX 2014
Networked Readiness
Index = 1/4 Environment subindex
+ 1/4 Readiness subindex
+ 1/4 Usage subindex
+ 1/4 Impact subindex
Environment subindex = 1/2 Political and regulatory
environment
+ 1/2 Business and innovation
environment
1st pillar: Political and regulatory environment
1.01 Effectiveness of law-making bodies*
1.02 Laws relating to ICTs*
1.03 Judicial independence*
1.04 Efficiency of legal system in settling disputes*c
1.05 Efficiency of legal system in challenging
regulations*c
1.06 Intellectual property protection*
1.07 Software piracy rate, % software installed
1.08 Number of procedures to enforce a contractd
1.09 Number of days to enforce a contractd
2nd pillar: Business and innovation environment
2.01 Availability of latest technologies*
2.02 Venture capital availability*
2.03 Total tax rate, % profits
2.04 Number of days to start a businesse
2.05 Number of procedures to start a businesse
2.06 Intensity of local competition*
2.07 Tertiary education gross enrollment rate, %
2.08 Quality of management schools*
2.09 Government procurement of advanced technology
products*
Appendix A:
Structure and computation of the Networked Readiness Index 2014

READINESS SUBINDEX
Readiness subindex = 1/3 Infrastructure and digital content
+ 1/3 Affordability
+ 1/3 Skills
3rd pillar: Infrastructure and digital content
3.01 Electricity production, kWh/capita
3.02 Mobile network coverage, % population
3.03 International Internet bandwidth, kb/s per user
3.04 Secure Internet servers per million population
3.05 Accessibility of digital content*
4th pillar: Affordabilityf
4.01 Mobile cellular tariffs, PPP $/min.
4.02 Fixed broadband Internet tariffs, PPP $/month
4.03 Internet and telephony sectors competition index, 0–2
(best)
5th pillar: Skills
5.01 Quality of educational system*
5.02 Quality of math and science education*
5.03 Secondary education gross enrollment rate, %
5.04 Adult literacy rate, %
USAGE SUBINDEX
Usage subindex = 1/3 Individual usage
+ 1/3 Business usage
+ 1/3 Government usage
6th pillar: Individual usage
6.01 Mobile phone subscriptions per 100 population
6.02 Percentage of individuals using the Internet
6.03 Percentage of households with computer
6.04 Households with Internet access, %
6.05 Fixed broadband Internet subscriptions per 100
population
6.06 Mobile broadband Internet subscriptions per 100
population
6.07 Use of virtual social networks*
7th pillar: Business usage
7.01 Firm-level technology absorption*
7.02 Capacity for innovation*
7.03 PCT patent applications per million population
7.04 Business-to-business Internet use*g
7.05 Business-to-consumer Internet use*g
7.06 Extent of staff training*
8th pillar: Government usage
8.01 Importance of ICTs to government vision of the future*
8.02 Government Online Service Index, 0–1 (best)
8.03 Government success in ICT promotion*
IMPACT SUBINDEX
Impact subindex = 1/2 Economic impacts
+ 1/2 Social impacts
9th pillar: Economic impacts
9.01 Impact of ICTs on new services and products*
9.02 PCT ICT patent applications per million population
9.03 Impact of ICTs on new organizational models*
9.04 Employment in knowledge-intensive activities, %
workforce
10th pillar: Social impacts
10.01 Impact of ICTs on access to basic services*
10.02 Internet access in schools*
10.03 ICT use and government efficiency*
10.04 E-Participation Index, 0–1 (best)
NOTES
a Formally, for a category i composed of K indicators, we have:
When two individual indicators are averaged (e.g., indicators 1.04)
and 1.05 in the 1st pillar), each receives half the weight of a normal
indicator.
b Formally, we have:
6 x
country score – sample minimum
+ 1
(sample maximum – sample minimum )
The sample minimum and sample maximum are, respectively, the
lowest and highest country scores in the sample of economies
covered by the GCI. In some instances, adjustments were made
to account for extreme outliers. For those indicators for which
a higher value indicates a worse outcome (i.e., indicators 1.07,
1.08, 1.09, 2.03, 2.04, 2.05, 4.01, and 4.02), the transformation
formula takes the following form, thus ensuring that 1 and 7
still corresponds to the worst and best possible outcomes,
respectively:
–6 x
country score – sample minimum
+ 7
(sample maximum – sample minimum )
c For indicators 1.04 and 1.05, the average of the respective scores
is used in the computation of the NRI.
d For indicators 1.08 and 1.09, the average of the respective
normalized scores is used in the computation of the NRI.
e For indicators 2.04 and 2.05, the average of the respective
normalized scores is used in the computation of the NRI.
f The affordability pillar is computed as follows: the average of the
normalized scores of indicators 4.01 mobile cellular tariffs and
4.02 Fixed broadband Internet tariffs is multiplied by a competition
factor, the value of which is derived from indicator 4.03 Internet
and telephony sectors competition index. It corresponds to the
score achieved by an economy on this indicator normalized
on a scale from 0.75 (worst) to 1.00 (best), using the min-max
transformation described above. A normalized score of 0.75
is assigned to an economy with a competition index score of
0, which means that a monopolistic situation prevails in the 19
categories of ICT services considered. A normalized score of
1.00 is assigned to an economy where all 19 categories are fully
liberalized. Where data are missing for indicator 4.03 (i.e., Puerto
Rico and Timor-Leste), the score on the affordability pillar, which is
simply the average of the normalized scores of indicators 4.01 and
4.02, is used. The competition index score for Taiwan, China, was
derived from national sources.
g For indicators 7.04 and 7.05, the average of the respective scores
is used in the computation of the NRI.
categoryi
K
⌺
k=1
indicatork
K
ϭ

CHAPTER 1.2
The Internet of Everything:
How the Network
Unleashes the Benefits of
Big Data
ROBERT PEPPER
JOHN GARRITY
Cisco Systems
Exabytes (1018
) of new data are created every single
day. Much of this information is transported over Internet
protocol (IP) networks. First described by Clive Humby
as the “new oil,”1
this data growth is fueling knowledge
economies, sparking innovation, and unleashing waves
of creative destruction. But most of these data are
unstructured and underutilized, flowing at a volume and
velocity that is often too large and too fast to analyze.
If data do, in fact, comprise the new raw material of
business, on par with economic inputs such as capital
and labor,2
then deriving insight and added value
from this new input will require targeted transmission,
processing, and analysis.
A rising share of this data growth is flowing over IP
networks as more people, places, and things connect
to this Internet of Everything (IoE). Proprietary networks,
built on industry-siloed standards such as those in
manufacturing or electric utilities, are increasingly
migrating to IP networks, facilitating the growth of
big data, and fast becoming the key link among data
generation, processing, analysis, and utilization.
How can we effectively maximize value from this
data explosion and avoid the pitfall of diminishing
marginal data value? This chapter details how IP
networks underpin the IoE and can accelerate big data’s
transformational impact on individuals, businesses, and
governments around the world. After first highlighting
four major trends driving data growth over IP networks
and detailing how networks are central to maximizing
analytical value from the data deluge, the chapter
identifies critical technology and public policy challenges
that could either accelerate or encumber the full impact
of big data and the IoE.
ACCELERATING DATA PRODUCTION AND
DATA TRAFFIC
Data growth is skyrocketing. Over 2.5 quintillion bytes
of data are created each day; 90 percent of the world’s
stored data was created in the last two years alone.3
To
put this into context, one hour of customer transaction
data at Wal-Mart (2.5 petabytes) provides 167 times
the amount of data housed by the Library of Congress.
The research consultancy IDC estimates that the
digital universe—all digital data created, replicated, or
consumed—is growing by a factor of 30 from 2005 to
2020, doubling every two years. By 2020, there will be
over 40 trillion gigabytes (or 40 yottabytes) of digital
data—or 5,200 gigabytes for every person on earth.4
Much of this data growth is traversing IP networks.
Cisco’s Visual Networking Index estimates that, from
2012 to 2017, total traffic over IP networks will grow
threefold, rising at a compound annual growth rate
(CAGR) of 23 percent. Mobile data traffic, however, is
growing at an even faster pace: over the same period,
mobile data will grow 13-fold, with a CAGR of 66
percent, capturing a greater share of all data created and
transmitted (Figure 1).5

Despite the rapid growth in data production and
transmission, however, only a small fraction of all
physical objects in the world are currently connected to
IP networks. Cisco estimates that less than 1 percent
of physical objects are connected to IP networks.6
But
the IoE is expanding as more devices and users are
connecting to IP networks every day, conducting more
transactions and processes online.
For individuals, the impacts of the IoE are felt
daily. Sensors embedded in shoes, for example,
track the distances that fitness enthusiasts run and
automatically upload information to social media profiles
to immediately compare athletic achievements with those
of friends. Internet-enabled alarm clocks gather data on
weather and traffic, combining that information with a
user’s schedule, determining the optimal time to wake
local residents. And applications on smart phones can
control home electronic devices, adjusting heating and
cooling levels as well as arming (or disarming) security
settings remotely.
At an industrial level, applications using sensor
technologies are capturing vast amounts of data to
improve decision-making. Sensors embedded in
agricultural fields monitor temperature and moisture
levels, controlling irrigation systems. Devices in oil fields
and deep well rigs track all aspects of drilling and fuel
delivery, increasing production efficiency. And sensors in
vehicles are able to monitor usage, informing decisions
around refueling and repair as well as vehicle design.
For governments, IoE and big data applications
are helping to monitor pandemics and environmental
conditions, improve public safety and security, and
increase efficiency in the delivery of public services such
as municipal traffic systems that incorporate real-time
remote monitoring to streamline traffic flows.
As more people, places, and things connect to the
IoE, the data universe will continue to grow rapidly. The
IoE will not only fuel the expansion of big data and data
transmission, but can also provide targeted, automatic,
data-driven analysis for our day-to-day lives.
CRITICAL DRIVERS OF DATA GROWTH
In 1944, the first digital computer, the Colossus,
was deployed in the United Kingdom to decipher
codes during World War II. The Colossus was able
to process data at 5,000 characters per second (~25
Kb/s).7
Currently the world’s fastest supercomputer,
the MilkyWay-2, can process 54,902 × 1012
operations
per second (54,902 TFlop/s).8
This intensive growth in
data processing power continues today, coupled with
extensive growth in data production. This data growth
also supports four major trends that lead to a rising
share of data transmission over IP networks in the world
of the IoE, as described below.
• Internet protocol (IP) is becoming the common
language for most data communication.
Proprietary industrial networks are migrating to IP,
bringing previously isolated data onto public and
managed IP networks. The Internet’s history is
built on the migration of proprietary networks to IP.
Proprietary data networks such as AppleTalk and
IBM Systems Network Architecture (SNA) have
migrated to IP over time, and traditional time-division
multiplexing (TDM) voice networks are migrating to
0
500
1,000
1,500
2,000
2015 (forecasted)2014 (forecasted)2013201220112010
Figure 1: Growth rates and rising share of mobile data
Sources: Cisco 2013b; EMC² 2013; authors’ calculations.
Indexeddatatrafficseries,2010levelsofbytes=100
— Mobile data traffic
— Total data universe
— Total IP traffic
1.2: The Internet of Everything

Voice over Internet Protocol (VoIP). Today electricity
grids, building systems, industrial manufacturing,
oil systems, and a multitude of other sectors with
networks that were previously built with proprietary
protocols are increasingly migrating to IP as
industries and enterprises recognize the value of
interoperability and scale. Each migration shifts a
large amount of data production and transmission
onto IP networks (see Box 1).
• Previously unconnected places, people, things,
and processes are connecting to networks for
the first time. Billions of people and devices will
come online in the next five years, adding heavily
to the endpoints collecting data and to the devices
consuming information. Cisco’s Visual Networking
Index estimates that, between 2012 and 2017, 7
billion more devices will connect to the Internet,
reaching a total of 19 billion connected devices.
These figures are conservative projections; other
estimates of the total number of connected devices
range from around 20 billion to 50 billion by 2020.9
By 2017, nearly half of the world’s population (3.6
billion out of 7.6 billion people) will be connected
to the Internet. Of the world’s total inhabited areas,
mobile network coverage will increase to 85 percent
in 2017, up from 79 percent in 2012.10
In addition, a
diversity of processes are migrating online. These
include transactional activities (such as payments
and requests), environmental monitoring (such as
environmental sensors and remote monitoring), and
government interactions (including census taking,
tax collections, and benefit distributions).
• Existing physically stored information is being
digitized in order to record and share previously
analogue material. Over the last decade, the
digital share of the world’s stored information has
increased from 25 percent to over 98 percent.11
Information previously stored on other media—such
as paper, film, and other analogue formats—is
being digitized, along with meta-information about
the data itself (e.g., descriptive statistics, frequency,
distribution, dispersion, etc.). This digitization of
information is leading to greater exchange of stored
media and data over the Internet.
• The introduction of Internet protocol version
6 (IPv6) allows for trillions of trillions (1038
) of
devices to connect to the Internet. IPv6 is the
latest update to the protocol that underpins the
Internet. It defines the system for routing traffic on
the Internet by giving identification and location to
all points connected to the global IP network. The
previous version of the protocol, IPv4, enabled
only approximately 4 billion IP addresses. But
IPv6 provides more than 340 trillion, trillion, trillion,
addresses,12
ensuring no immediate exhaustion of IP
Box 1: Big data: Huge and growing data volume
from industrial applications
Industrial applications of the Internet of Everything (IoE)
generate immense data flows, which are increasingly
shifting over to Internet protocol (IP) networks. One reason
for the shift is that IP networks have increased reliability.
Industrial networks have traditionally been concerned with
uptime and latency, and IP networks have evolved to be
able to handle industrial demands and the data flows that
come with them.
In the oil and gas industry, for example, data are
utilized across the entire value chain, from exploration,
production, refining, and distribution to marketing and
retail. Sensors and computing are used to capture and
monitor seismic data, borehole activity, environmental
readings, weather, production utilization, storage capacity,
spot pricing (trading), transportation, inventory levels,
demand and forecasts, and location data. In seismic
exploration, the cost, size, and speed of data are all
rising as exploration moves to 3D imaging. Data capture
amounted to around 300 megabytes per square kilometer
in the 1990s. By 2006, data per square kilometer
amounted to 25 gigabytes, while today the amount per
square kilometer is in the petabytes.1
According to Chevron
and industry-wide estimates, a “fully optimized” digital oil
field based on data utilization results in 8 percent higher
production rates and 6 percent higher overall recovery.2
In electric utility grids, data utilization also improves
efficiency. Current grids monitor data to control electricity
flows (both to and from the grid) based on real-time
demand, thus improving generator efficiency and ensuring
more-sustainable energy sources. Upgrading standard
electric meters to “smart meters” allows information to be
communicated over a network back to a control center and
increases the amount of data captured. While traditional
meters are read once a month, some smart meters can
report usage rates in 15-minute intervals. For every million
meters, this leads to 96 million measurements per day,
an estimated 3,000-fold increase in data collection.3
Conservative estimates of the total amount of data that will
be generated by smart meters by 2019 in the United States
alone (assuming only two readings per day, and below full
deployment) yields measurements in the order of hundreds
of petabytes per year.4
In an example from another industry, aircraft
manufacture and operation, sensors on General Electric
(GE)’s jet plane turbines illustrate the vast amount of data
generated daily. GE estimates that each sensor on a GE
turbine generates approximately 500 gigabytes of data
every day. Each turbine has 20 sensors, and globally GE
owns approximately 12,000 turbines. This aggregates to
petabytes of data daily.5
Notes
1 Beals 2013; see also note 4 at the end of this chapter.
2 Leber 2012.
3 IBM Software 2012.
4 Danahy 2009; Fehrenbacher 2009.
5 Lopez 2013.

addresses or limits to the number of IP connections.
The sheer number of available addresses allows for
every single star in the known universe to have 4.8
trillion addresses.
THE GAP BETWEEN DATA GROWTH AND DATA
VALUE
Current estimates suggest that only half a percent of
all data is being analyzed for insights;13
furthermore,
the vast majority of existing data are unstructured and
machine-generated.14
Applying analytics to a greater
share of all data can lead to productivity increases,
economic growth, and societal development through the
creation of actionable insights.
Data alone are not very interesting or useful. It is
when data can be used and become actionable that
they can change processes and have direct positive
impact on people’s lives. The IoE generates data, and
adding analysis and analytics turns those data into
actionable information. Building on the framework of
the knowledge hierarchy,15
aggregated data become
information that, when analyzed, become knowledge.
Knowledge can lead to insights and informed decision-
making, which at the highest level is wisdom (Figure 2).
For example, society at large can benefit from
tracking trends observed from metadata such as
anonymized mobile phone data used to track population
migration after the earthquake and cholera outbreaks in
Port-au-Prince, Haiti.16
Likewise, analyzing social media
discussions can identify crises or flu outbreaks.
At an industrial level, big data analysis can yield very
large benefits. For example, the value of modernizing
the US electricity grid to be data-driven is estimated at
US$210 billion. A reconstituted electricity grid would
be based on an architecture driven by “technology
selections to fully harness the convergence of data,
controls and transactions.”17
According to Bradley et al. in a recent Cisco White
Paper, harvesting data for critical decision-making
though the IoE can create approximately US$14.4 trillion
dollars of added value in the commercial sector over
the next 10 years across a wide range of industries.18
This opportunity exists in the form of new value created
by technology innovation, market share gains, and
increasing competitive advantage. It translates into
an opportunity to increase global corporate profits by
approximately 21 percent, driven by improvements in
asset utilization (reducing costs and improving capital
efficiency), employee productivity (improved labor
efficiency), supply chain logistics (eliminating waste and
improving process efficiency), customer experience
(adding more customers), and innovation (reducing time
to market).
Similarly, research by the Economist Intelligence
Unit and Capgemini indicates that big data analytics
were responsible for a 26 percent improvement in
business performance among a cohort of companies
examined, and forecasts that the impact could increase
to 41 percent in three years.19
Capturing these gains,
however, may require concurrent investment in resources
to manage the rise in data. It is forecasted that by 2020,
an average business will have to manage 50 times
more information than it does today, while the average
information technology (IT) staff is expected to rise only
by 1.5 times.20
Beneﬁts to society via improved outcomes
Figure 2: Turning data into insight
Sources: Ackoff 1989; authors’ interpretation.
Insight
(wisdom)
Process optimization
KnowledgeDecision-making
InformationMetrics and scorecards
Data
Individual data
points

EQUIPPING IP NETWORKS TO DELIVER BIG DATA
INSIGHTS
Moving up the knowledge pyramid from data to insights
and informed decisions is a critical challenge facing
businesses and governments. Equipping IP networks
to better transmit data to processing centers as well as
enabling the network to create, analyze, and act on data
insights is one comprehensive approach. Building this
capability will require improving network infrastructure,
building analytical capabilities and “intelligence” into
the network, and distributing computing and analytical
capabilities throughout the network, particularly at the
edge. Specifically, these are:
• Network infrastructure improvements. These
improvements include connecting all things,
including unintelligent ones (those that are capable
only of transmitting data, not receiving them);
securing infrastructure; improving inter and intra-
data center traffic flows; and increasing the ability to
manage private and public networks.
• Building intelligence into the network. This will
require building in the ability to compute data
in motion and host partner applications in an
ecosystem where applications can be built to
analyze data inflow, particularly enabling machine-
to-machine (M2M) services.
• Distributing computing and storage. Efficient
distribution will require moving the ability to analyze
data only in the data center to add processing at the
edge (or near the edge) of the network, to prevent
delays in processing caused by latency as well as
delays caused by network congestion.
TECHNICAL AND POLICY CHALLENGES
Building a network that will maximize the impact of big
data requires powerful and seamless interactions among
sensors, devices, computing, storage, analytics, and
control systems.
But although IP networks are primed to support the
expansion of big data and the IoE, technical and policy
challenges exist in the ability of current IP networks to
fully exploit big data expansion (Figure 3). An approach
that tackles these issues concurrently will help to
create the right ecosystem. The discussion below
highlights specific issues that will need to be addressed
thoughtfully.
Standards and interoperability issues span both the
technical and policy domains. Agreement on standards
is critical to develop economies of scale by encouraging
product and service innovation around a common
language, and generally accepted global standards
allow for greater interoperability between devices.
Requirements differ for closed critical networks (such
as utilities) and open networks (for example, those that
may monitor parking space availability), but common
standards allow information to be exchanged within, and
among, these networks when those needs arise.
Privacy issues arise with the growth of data,
particularly with regard to data generated by or about
individuals. Policymakers must identify the appropriate
balance between protecting the privacy of individuals’
data and allowing for innovation in service delivery and
product development. New technologies and services,
Source: Authors.
POLICYTECHNICAL
Figure 3: Policy and technical issues facing big data and the IoE
Standards &
interoperability
Privacy &
security
Spectrum &
bandwidth
constraints
Reliability
Scaling
Electrical power
Cross-border data traffic
Legacy regulatory models

such as location-based services, are bringing these
privacy issues to the forefront, offering users enhanced
experiences while raising concerns of identity protection.
Some policies—such as transparency in the use of
data and effective mechanisms for consumer control of
personal data—can help in this regard. The key security
issues for big data include the reliable prevention of
hacking and access by unauthorized and unwanted
users to large databases and data flows. In order to
ensure a healthy ecosystem where users, consumers,
and businesses feel safe in engaging in big data
activities, network security is essential.
Over the next five years, the growth of mobile data
traffic will require greater radio spectrum to enable
wireless M2M, as well as people-to-people (P2P) and
people-to-machine (P2M), connectivity. Ensuring device
connectivity and sufficient bandwidth for all of the uses
of wireless sensors will require careful planning. The
spectrum requirements are going to be heterogeneous
and will include narrowband and broadband frequencies,
short haul and long haul spectrum, continuous data
transmission and short bursts of data transmission,
and licensed spectrum in addition to license-exempt
spectrum. Bandwidth constraints will also be an
obstacle in transmitting data over existing networks.
The examples cited in Box 1 reflect the volume of data
being generated by proprietary networks, resulting in
the need to move computing close to the network edge
in a distributed intelligence architecture. Data loads will
be lumpy across various applications of the IoE, and
matching bandwidth needs to bandwidth availability will
be a continuous challenge.
As more critical processes are conducted as
part of the IoE, the need for reliability in IP networks
increases. Healthcare applications that require instant
communication between end users and medical
professionals, safety and security applications, utility
functions, and industrial uses are examples where
continuous, uninterrupted, real-time communications
require reliable and redundant connectivity. Low latency
(the time required for round-trip data transmission)
is already required for advanced cloud computing
applications such as high-definition video conferencing
and industrial collaboration. Any interruption to the
transmission of data over networks negatively impacts
these processes.
Constraints on the technological limits of electrical
efficiency and on computer memory and processing
already pose limits to computing and data analysis. Data
centers, for example, exemplify the boundaries where
electrical power, cooling resources, and space design
are constantly redesigned and re-imagined to advance
current capabilities. As the IoE expands into tens of
billions of connected devices, the technological aspects
of IP networks have to be able to manage the huge scale
of device connectivity. One aspect of this expansion,
Internet addressing, is being resolved with the migration
from IPv4 to IPv6. Other challenges include determining
how virtualized computing environments may support a
reallocation of computing resources. And new sources
of electrical power (advanced batteries, simple chemical
reactions, etc.) will be needed to power the multitude of
new devices that will emerge.
IoE applications that collect and handle data across
sovereign jurisdictions could be negatively affected by
policies restricting cross-border data traffic and global
trade in IoE-related services. Emerging cross-border
issues include national data protection rules and data
transfers, data portability and interoperability standards,
and liability costs for cloud service providers. Furthermore,
trade in some IoE services may fall under existing
international trade agreements, while others do not.
As the IoE permeates across business sectors, the
application of IoE technology in traditional industries
presents new challenges to legacy regulatory models.
IoE technology is impacting business models, input/
output markets, and end users in markets ranging
from healthcare to utilities. The heavily regulated
energy markets, in particular, face a range of issues
from “connected energy” technologies. At the
consumer level, smart meters may present privacy and
security challenges. However, at the aggregation and
distribution levels, utility companies face the new reality
of a changing energy source mix and must adapt to
transactional loads and markets along with existing grid
control that needs to adapt to distributed intelligence as
well as challenges to traditional regulated utility pricing.
THE CENTRALITY OF THE NETWORK
Since the beginning of our species, humans have been
processing data. We have been our own primary data
machines. But today, with the advent of vast arrays
of computing power, we increasingly rely on data
processed by others, and the IoE and the era of big data
are transforming our lives.
Data flows and the ability to capture value from data
are changing industries, creating new opportunities while
impacting others. For example, the “app economy”—the
business created by software applications running on
smartphones—has created hundreds of thousands of
jobs.21
One recent study estimates that the marginal
impact of data utilization in the IoE could raise US gross
domestic product by 2 percent to 2.5 percent by 2025.22
The IoE—where more data are being captured
by more devices, interacting with more people and
changing the processes by which we live, learn, work,
and play—is having a profound impact on the world.
But the value derived from the IoE can be measurably
increased if IP networks are able to facilitate the rise of
big data and generate added positive impact for society.
NOTES
1 Palmer 2006.
2 The Economist 2010.

3 IBM 2013.
4 Gantz and Reinsel 2012. A useful reminder in the sequence of
data storage and memory is that the measure increases by the
thousands and the sequence is from byte, kilobyte, megabyte,
gigabyte, terabyte, petabyte, exabyte, zettabyte and beyond.
5 Cisco 2013b
6 Cisco 2013a.
7 McLellan. 2013.
8 Top500.org 2013.
9 Biggs et al. 2012.
10 GSM Association 2012.
11 Cukier and Mayer-Schoenburger 2013.
12 More specifically, 340,282,366,920,938,463,463,374,607,431,768,
211,456 addresses, or roughly 3.4 times 1038.
13 Gantz and Reinsel 2012.
14 Canalys 2012.
15 Ackoff 1989.
16 Bengtsson et al. 2011.
17 De Martini and Von Prellwitz 2011; Taft et al. 2012, p. 2.
18 Bradley et al. 2013.
19 EIU 2012.
20 EMC2
2013.
21 In the United States, according to Mandel and Scherer (2012), over
500,000 jobs have been created through application development
since 2007; in the European Union, Vision Mobile and Plum (2013)
found that nearly 800,000 jobs have been created this way.
22 Mandel 2013.
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Cukier, K. and V. Mayer-Schoenburger. 2013. “The Rise of Big
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De Martini, P. and L. von Prellwitz. 2011. “Gridonomics: An Introduction
to the Factors Shaping Electric Industry Transformation.” Cisco
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smart-grid-data-about-to-swamp-utilities/.
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Creating Jobs and Driving Growth. Report sponsored by ACT,
September. Available at http://www.act4apps.org/wp-content/
uploads/2013/09/ACT-The-European-App-Economy-20131.pdf.

CHAPTER 1.3
Big Data Maturity:
An Action Plan for
Policymakers and
Executives
BAHJAT EL-DARWICHE
VOLKMAR KOCH
DAVID MEER
RAMEZ T. SHEHADI
WALID TOHME
Booz & Company
The total volume of structured and unstructured data
generated by individuals, enterprises, and public
organizations is multiplying exponentially; 90 percent
of the total data stored today is less than two years
old.1
So-called big data has the potential to improve
or transform existing business operations and reshape
entire economic sectors. It can also pave the way for
disruptive, entrepreneurial companies and allow new
industries to emerge.
THE BIG DATA IMPERATIVE
If they are to capitalize on this potential, organizations
should avoid a common misapprehension. Much debate
has focused on the need to develop the technology to
store and analyze the deluge of data that threatens to
drown companies. Although this technology is indeed
necessary, it is not sufficient to enable big data to be
exploited fully.
Organizations must instead remold their decision-
making culture so that senior executives make more
judgments based on clear data insights rather than
on intuition. They must build the necessary internal
capabilities, deploying the technical and human
resources to interpret data in an astute manner.
Moreover, because they rely on governments to provide
the requisite environment, they must ask policymakers
to create the regulatory framework and information
and communications technology (ICT) infrastructure to
remove external obstacles.
We propose a Big Data Maturity Framework
that is based on the experiences of organizations
that have undergone a big data transformation. This
framework will allow organizations to assess their
progress in this arena and determine what they need
to do to extract greater business and organizational
benefits from the vast volume of data. The framework
incorporates three elements: (1) environment readiness;
(2) internal capabilities; and (3) the various, steadily more
sophisticated, ways to use big data that range from
increased efficiency in existing operations to a complete
change in an organization’s business model.
WHAT IS BIG DATA?
Big data represents the newest and most
comprehensive version of organizations’ long-term
aspiration to establish and improve their data-driven
decision-making. It is characterized by what are known
as the “three Vs”—large data volumes, from a variety
of sources, at high velocity (i.e., real-time data capture,
storage, and analysis). Besides structured data (such as
customer or financial records), which are typically kept
in organizations’ data warehouses, big data builds on
unstructured data from sources such as social media,
text and video messages, and technical sensors (such
The authors wish to thank Dr. Andreas Deckert for his contribution to this
chapter.

as global positioning system, or GPS, devices)—often
originating from outside the organization itself.
The magnitude and complexity of data being
produced far exceed the typical capacities of traditional
databases and data warehouses for the purposes of
storing, processing, analyzing, and deriving insights.
Usage statistics emanating from social media sites
illustrate the sheer volume of unstructured data. For
example, in 2012 Facebook reported that it was
processing around 2.5 billion new pieces of content
daily.2
Big data has the potential to infuse executive
decisions with an unprecedented level of data-driven
insights. However, research indicates that many
organizations are struggling to cope with the challenges
of big data. For example, in 2012 the Aberdeen Group
found that the proportion of executives who reported
that their companies were unable to use unstructured
data, and who complained that the volume of data was
growing too rapidly to manage, had increased by up to
25 percent during the previous year.3
EVOLUTION, NOT REVOLUTION
Despite the rapid growth of big data, organizations
should keep its influence in perspective. Although
remarkable, the big data phenomenon is merely
the continuation of a journey in which ever-more-
elaborate data have influenced decision-making. From
organizations’ first attempts at data analytics in the
1960s and 1970s, this journey has proceeded through
various stages, described by buzz words such as data
mining and business intelligence, all of which sought
to transform raw data into meaningful information for
business purposes (Figure 1).
The latest development, big data, may appear
all-enveloping and revolutionary. However, the essential
principles for exploiting its commercial benefit remain
exactly the same as they were in previous moves toward
increased data-driven decision-making. Executives
must harness this recent data explosion by focusing on
carefully formulating the business questions that enable
the swift and accurate identification of those nuggets of
data that they believe can improve their organization’s
1970 1980 1990 2000 2010 Now and future
Figure 1: Evolution of data-driven decision-making
Source: Booz & Company.
Linear programming
Management information
systems/dashboards
Data marts Data warehouses
Data clusters
Operations
research Credit scoring
Cloud storage
Nonlinear
programming
Crowd-sourcing
Internet of Things
Neural networks
Web analytics
Industry 4.0
Decision
support
systemts
Customer
relationship
management
Sentiment analysis
Image
analysis
Web crawling
Natural language
processing
Data
visualization
Monte Carlo
simulations
Standard reporting
Knowledge discovery
Operational
intelligence
Heuristic
problem-
solving
Risk modeling
Alerting
Expert systems
Ad-hoc reporting
Data cubes/drill down
Forecasting
Statistical analysis
Web search
Yield management
Data mining
Telematics
Predictive modeling
Discrete-
event
simulation
Businesss intelligence
Machine learning
Speech recognition
Text mining
Video analysis
Contextual marketing
User activity tracking
Real-time analytics
Advanced analytics
Social media
monitoring
Anticipatory analytics
Artificial intelligence
Analytics:Degreeofsophistication
Volume/complexity of data
BIG DATA
Chapter 1.3: Big Data Maturity

performance or allow them to gain access to new
revenue pools.
This continuation of a trusted managerial approach
does not, however, imply an endorsement of inertia.
Rather, organizations must foster a new decision-making
culture to exploit the opportunities presented by big data
and prepare their own internal capabilities to handle
this new era. At the same time, they must encourage
governments to nurture an environment conducive to the
exploitation of big data.
THE BUSINESS IMPACT OF BIG DATA
Many organizations are still in the early stages of reaping
the benefits of big data. Writing in the Harvard Business
Review, Andrew McAfee and Erik Brynjolfsson explored
the impact of big data on corporate performance. The
authors interviewed executives in 330 publicly traded
companies in the United States. They then examined
relevant performance data, enabling them to measure
the extent to which corporate attitudes toward big data
correlated with how the respective companies were
faring.
McAfee and Brynjolfsson’s conclusions were
remarkable for establishing a connection between
big data and performance: “The more companies
characterized themselves as data-driven, the better
they performed on objective measures of financial
and operational results.” The advantage gained by
these companies over their rivals was also marked: “In
particular, companies in the top third of their industry
in the use of data-driven decision-making were, on
average, 5 percent more productive and 6 percent more
profitable than their competitors.”4
Despite these findings, broad adoption of advanced
big data practices has not yet materialized. A 2013
Gartner survey found that less than 8 percent of
companies surveyed have actually deployed big data
technology.5
Investment in forthcoming projects is much more
widespread; the research firm IDC has forecasted that
the market for big data technology and services will
reach US$16.9 billion by 2015, up from US$3.2 billion
in 2010. This represents a 40 percent annual growth
rate, seven times the rate for the overall ICT business.6
This trend is affecting all regions. For example, over 40
percent of chief information officers in the Middle East,
according to IDC, are considering big data technology
investment in 2013. Although few have actually
undertaken large-scale big data or analytics programs
to date, IDC forecasts investment in this area to grow at
a compound annual growth rate of over 20 percent over
the coming five years.7
Both expenditure and implementation vary
substantially across regions, industries, and functional
domains. For example, highly digitized industries such
as telecommunications and travel still tend to spend
substantially more on big data projects than the energy
sector, and there is far more implementation of big data
initiatives in the United States than in the Asia Pacific
region. Meanwhile, the Economist Intelligence Unit found
that big data is most frequently enlisted to assist financial
management and marketing/sales, and deemed least
valuable in human resources management.8
How big data is used
The big data maturity stages (Figure 2) depict the
various ways in which data can be used, from selective
adoption to large-scale implementation. Depending on
the maturity of an organization’s big data capabilities,
big data can significantly increase top-line revenues
and markedly reduce operational expenses. The path
to business model transformation, the highest level
of maturity, promises potential high returns but often
involves major investment over many years.
The first maturity stage, performance management,
enables executives to view their own business more
clearly through, for example, user-friendly management
information dashboards. This stage typically relies on
internal data, with an organization establishing key
performance indicators (KPIs) to evaluate its success at
achieving stated goals.
During stage 2, functional area excellence,
organizations start to experiment with internal and
external data to improve selected facets of their
business. This may involve sales and marketing
techniques such as customer segmentation and
targeting, or entry-stage analytical methods for product
recommendations. For example, one retailer analyzed
data recounting the past purchasing behavior of
individual customers in conjunction with the company’s
most recent sales to predict and recommend each
customer’s most likely next purchase. This resulted in a
revenue increase of up to 5 percent, depending on the
customer segment.
Advances in operational efficiency through big data,
such as the efficient deployment of staff resources and
the optimization of the supply chain, also reside within
this maturity stage. Recent examples include a German
car manufacturer that used real-time performance
monitoring of production machinery to trigger a 20
percent increase in productivity. Each machine was
tightly monitored to highlight downtime and plan around
those production disruptions to optimize the utilization of
the overall plant. In the public sector, a Canadian hospital
observed previously unseen patterns in streaming
data from monitoring of newborns, enabling detection
of dangerous infections 24 hours before symptoms
appeared.9
At the value proposition enhancement stage (stage
3), organizations start to monetize big data, positioning it
as a value driver of the business that offers a new source
of competitive advantage beyond the mere improvement
of operations or services. In many instances this
involves obtaining data from external sources and

deriving insights from it. This may include innovations
such as customized, real-time recommendations or the
personalization of services to augment the customer
experience.
For example, one leading European bank tailored
its website content to trigger an increase of 12 percent
in sales. After customers logged in, the bank presented
one of several alternative websites based on the relevant
individual’s transaction history and segment and the
company’s overall product portfolio. The content was
specific to the predicted needs of the customer to
maximize sales potential.
Data-rich organizations, such as retailers or
telecommunications companies, are better equipped
than others to utilize their internally generated data in
this way. For instance, a global mass merchant was
able to increase its profit per customer by 37 percent
by applying advanced customer analytics, such as
behavioral segmentation, to identify its best customers
and provide them with personalized offers. The
frequency of those target customers’ purchases rose by
approximately 25 percent, and the average basket size
increased by around 10 percent.
Another instructive case involved the US city of Los
Angeles, which introduced demand-responsive pricing
for parking. The city sets specific prices for hourly
parking in each street, varying according to the time
and day. These prices are based on in-depth choice
modeling, fed with data from parking sensors, surveys,
weather forecasts, information about holidays, local
business activities, and other information. The goal is
to reach a steadily high, but not excessive, utilization
Figure 2: Big data maturity stages and related use cases
Maturity stages Typical use cases/applications
Stage 1:
Performance
management
• Financial reporting
• Regulatory/compliance
reporting
• Dashboards for management
reporting
• Performance measurement via
key performance indicators/
metrics
Stage 2:
Functional area
excellence
• Smart pricing
• Targeted mailings
• Customer segmentation
• Customer value analysis
• Choice analysis
• Website clickstream analysis
• Loyalty schemes
• Customer satisfaction opt.
• Supply chain balancing
• Facility optimization
• Optimization of staff utilization
• Route optimization for fleet
• Fraud monitoring/prevention
• Patient treatment control
• Traffic optimization
• Crime monitoring
Stage 3:
Value proposition
enhancement
• Targeted advertising/
customized recommendations
in real time
• Preventive health monitoring &
disease detection
• Data monetization
• Online telematics services
• Personalization of customer
experience/products
Stage 4:
Business model
transformation
• Selling of data to open new
revenue pools
• Data-centric business models
(e.g., web search, web adver-
tising)
• Quantitative management of
investment funds
• Crowdsourcing to augment
internal data
Large-scale
implementation
Experimenting/
selective adoption
BIG DATA

of parking space at all times. The initial results are
impressive. Although city parking revenues increased
by 2.4 percent due to higher utilization, 60 percent of
parking rates actually fell and congestion during peak
hours decreased by 5 percent.10
In the final maturity stage, business model
transformation, big data permeates the whole
organization. It becomes deeply embedded within the
operation, determining the nature of the business and
the mode of executive decision-making.
This stage can be reached by both product and
services organizations alike. One example of the latter
involves the recent merger of the two large advertising
companies Omnicom and Publicis. Their industry is
moving away from the creative “Mad Men” approach,
where a catchy phrase was the pinnacle of aspiration,
toward a more science-based, data-driven business
that aims to personalize ads. The ultimate goal is to
deliver the right message to the right person at the right
time. The Internet and mobile devices play a major role
in this development. This new “ad tech” world will be
dominated by those major players that possess the most
comprehensive data about consumers and are thus
able to understand them better—who they are, where
they are, what they like, who their friends are, and so on.
Omnicom and Publicis believe that their combined size
will produce the desired volume of data.
General Electric (GE) provides a prominent example
of a product organization placing great faith in big data.
GE expects that machinery and equipment will soon
be loaded with sensors, making in-depth status data
available both in real time and across longer time spans.
To be at the forefront of this development, GE is investing
more than US$1 billion in building up its data science
capabilities to provide data and analytics services across
business functions and geographies.11
Another showcase for the transformative potential
of big data comes from the public sector. Regional
and national-level policymakers around the world are
launching “open data” initiatives, making data available
to the public via integrated web portals and automated
interfaces. Recent examples involve the United Kingdom
and New York City.12
Although originally conceived as
a way of increasing the transparency of government
decisions, the release of public data is an important
environmental factor enabling organizations to use big
data, creating novel applications and services.
However, some organizations do not have to
progress through all the big data maturity stages.
A data-driven business model has been integral to
companies such as Google, Facebook, and Twitter,
which have burst on to the scene in recent years and
are introducing new technologies to capture the digital
advertising market. Such companies actually started
operations at the final stage. By doing so, they have
prompted others to proceed through the earlier stages to
keep up.
Obstacles to progress
Despite widespread interest in data-driven decision-
making in one form or another, companies face many
potential pitfalls in extracting the maximum commercial
benefit from big data usage. Some of these relate to their
own internal systems and culture; others are tied to the
external environment.
The most prominent obstacle is the shortage of
available talent specializing in data analytics—data
scientists with an advanced education in mathematics or
statistics who are also able to translate raw data material
into exploitable commercial insights. Although many
educational institutions have now started to establish
courses to address this scarcity, the market demand is
already considerable.
Many organizations also suffer from poor-quality
data that are fragmented across various systems,
geographies, and functional silos. Embracing the
potential of big data as a concept will take organizations
only so far. First and foremost, they must get the basics
right. Internal data has to be of high quality—consistent,
accurate, and complete—and available across the
organization.
The prevailing decision-making culture presents
a major internal obstacle—the one that is the least
straightforward to identify and then overcome. To
complicate matters, changing this culture may impinge
on personal concerns relating to status. Companies
and public institutions typically rely on the intuition of
senior managers for important decisions. As big data
extends its reach, executive instinct is challenged by the
facts of hard data. However, while data can be of great
assistance in solving an actual problem, it still holds true
that senior management has to ask the right questions.
Many of the external challenges that companies
face revolve around data privacy considerations. For
example, very specific details of an individual’s lifestyle
preferences and buying habits are now captured and
analyzed by companies through their own websites
or by monitoring social media. These details are all
collected without that person’s explicit consent, leading
to significant public reservations about big data. Such
concerns about privacy will strengthen demands for
tighter regulatory control, potentially limiting companies’
ability to exploit big data opportunities or exposing them
to threats of legal and regulatory intervention.
HOW TO REACH BIG DATA MATURITY
Our big data maturity framework (Figure 3) comprises
three elements: the enablers of environment readiness,
the organization’s internal capabilities, and the different
stages of maturity and sophistication in which big data
can be used. The framework enables organizations to
view the extent of their success in overcoming obstacles,
and identifies what more can be done to promote big
data maturity and reach the desired destination.

The environment readiness dimension considers
how far the relevant governments have enabled
organizations in their jurisdiction to use big data freely
and productively. This is achieved through appropriate
regulations and a supportive infrastructure.
The organization’s internal capabilities dimension
sheds light on a company’s readiness to execute big
data. By building up these capabilities and integrating
them effectively, organizations move further along
the path of data-driven decision-making and position
themselves to extract greater benefits from big data.
While environment readiness serves as an enabler
for big data usage, internal capabilities act as critical
success factors for organizations seeking to progress
through the maturity stages.
The following two sections explain the full range
of actions that governments and organizations need to
undertake and which of these take precedence.
Environment readiness: Priorities for policymakers
Big data will soon become ubiquitous practice in
both the public and private worlds. Policymakers
therefore need to act in a timely manner to promote an
environment that is supportive to organizations seeking
to benefit from this inevitable progression. Failure to
develop comprehensive environment readiness carries
the risk of losing competitive advantage vis-à-vis other
countries.
Policymakers must therefore:
• formulate a vision for the usage of data consistent
with the public interest, fostering a common
understanding with citizens and obtaining their
buy-in;
• enable a big data ecosystem by establishing policies
to facilitate valid business models for third-party
data, service, and information technology system
providers; and
• speed and scale up the education of talent to
address the likely significant shortage of talent
with the requisite skills to leverage and handle big
data—from both a business and an IT perspective,
potentially in public-private partnerships (PPPs).
Such PPPs can avoid having students in tailored
university programs graduate with outdated learning
by fostering and exploiting private contributions to
the educational agenda. PPPs can involve public
sponsorship of private education programs, and
private support for university education in terms of
knowledge transfer and financial endowments.
Figure 3: Big data maturity framework
Enablers of
environment readiness
Success factors
for internal capabilities
Maturity stages
in the usage
of big data
Traditional applications
(getting more out of data you already have)
New horizons of big data
Technical capabilities/infra-
structure
Regulatory framework
for data privacy
Data availability
and governance
ICT infrastructure
Sponsorship
Big data ecosystem
Organizational capabilities
and resources
Public perception
and awareness
Data-driven
decision-making culture
Education/training
Stage 1:
Performance
management
Stage 2:
Functional area
excellence
Stage 3:
Value proposition
enhancement
Stage 4:
Business model
transformation
What can we
read from the data?
What can we learn
from the data
to become better?
How can we make
data a value driver
of our business?
How can we use data
to fundamentally
reinvent our business?

Priorities for policymakers will vary in different
parts of the world. Developing countries, for example,
will concentrate on building up the required ICT
infrastructure and education programs to prepare for
large-scale demand from organizations intent on using
big data. In more developed countries, however, the
government’s primary concerns should be ensuring
transparent regulation and promoting a public-interest
argument for big data.
Policymakers must make the case for big data
In particular, policymakers should set clear rules
regarding data privacy so that organizations know which
personal data they can store and for how long, and
which data are explicitly forbidden by privacy regulations.
If the scope of permissible data is to expand,
skeptical citizens must first be persuaded that big data
will work in their favor by paving the way for better
products and services. Forward-thinking governments
will initiate and inform this public debate about the
benefits of big data. Indeed, Jules Polonetsky and
Omer Tene, in their Stanford Law Review article (2013),
argue that finding the right balance between individuals’
legitimate privacy concerns and the overall rewards
offered by big data practices may be the greatest
contemporary public policy challenge.13
The outcome of this debate will vary depending on
the country. Cultural factors will have a strong bearing on
the decision about the right level of data privacy in any
given country, and this decision will result in a regulatory
regime appropriate for citizens and organizations. On a
regional level, groups such as the European Union allow
possible harmonization of data privacy regulation across
borders, given that the constituent countries may share
attitudes on this issue.
Policymakers should promote harmonization
On a global level, though, no binding agreement to
harmonize regulation around data privacy currently looks
likely in the short to medium term. Because countries
have legitimate differences on this issue, this lack of
harmonization threatens the adoption of big data on an
international scale.
The prevailing patchwork situation accentuates
the lack of clarity on lawful data usage—especially the
question of which jurisdiction holds sway for certain
cross-border cases. For example, if data are owned
by a company in the European Union, but hosted on
servers in the United States, which privacy law applies?
Legal inconsistency between countries can even inhibit
free commerce across borders. This problem arises, for
example, when an organization plans to outsource data
operations to a foreign provider, yet some personal data
are prohibited from being transferred out of the country
concerned.
The Organisation for Economic Co-operation and
Development (OECD) Guidelines on the Protection of
Privacy and Transborder Flows of Personal Data, which
were revised and updated in 2013, may represent a
practical step in the direction of harmonization.14
In the
absence of binding rules, a common understanding for
data privacy and data protection regulation on the basis
of guidelines and recommendations from a high-profile
international organization is the most sensible option
currently available.
The OECD guidelines follow a best-of-breed
approach. They formulate basic principles around
the limitation of collection of personal data, the
specification of the purpose of data collection, the
protection of collected data, the prevention of data loss
or unauthorized access, and the right of individuals to
obtain information about collected data. The guidelines
have in the past influenced national legislation, including
privacy acts in Australia, Japan, Mexico, and New
Zealand. We encourage both OECD members and non-
members to review and adopt those basic principles and
recommendations to establish common ground.
An organization’s internal capabilities: Priorities for
executives
There is no general rule dictating how organizations
should navigate the stages of big data maturity. They
must each decide for themselves, based on their
own situation—the competitive environment they are
operating in, their business model, and their existing
internal capabilities. In less-advanced sectors, with
executives still grappling with existing data, making
intelligent use of what they already possess may have a
substantial impact on decision-making.
The main priorities for executives are to:
• develop a clear (big) data strategy;
• prove the value of data in pilot schemes;
• identify the owner for “big data” in the organization
and formally establish a “Chief Data Scientist”
position (where applicable);
• recruit/train talent to ask the right questions and
technical personnel to provide the systems and tools
to allow data scientists to answer those questions;
• position big data as an integral element of the
operating model; and
• establish a data-driven decision culture and launch
a communication campaign around it.
Quick wins
Organizations should resist expensive upfront
infrastructure investments for overly ambitious big data
projects. Instead, they should select opportunities for
high business impact and adopt pilot schemes that also
allow for periodic refinements along the way.
Seeking out proprietary data that can be
immediately exploited for commercial gain may provide

one such quick win. For example, a mobile phone
operator can collect anonymized real-time travel
patterns, which are of value for navigation system
operators that want to provide up-to-date traffic
information to their customers.
Help from outside
External data providers can offer all types of data to
organizations and can therefore complement existing
data-gathering efforts. Typical datasets offered by
external providers include contact, lifestyle, and
demographic information on (market segments
of) individuals. Social media platforms are also
demonstrated to be great sources of relevant big data—
for example, for sentiment analysis (to determine the
voice and desires of the consumer) or for personalizing
product offerings. In addition to sourcing data from
outside the organization, the selective use of external
analytics service providers can also prove instrumental
in establishing big data maturity quickly, while potentially
training employees to take on these tasks themselves.
CONCLUSION
We currently see big data as poised to have significant
impact in public and business spaces alike. Large-
scale investment is flowing into establishing big data
capabilities in many organizations, despite the limited
number of cases in which it has been successfully used
in completed projects and initiatives. Decision makers
already acknowledge the future influence of data-driven
decision-making.
However, organizations confront vast differences
in their ability to utilize big data to good effect, as seen
in their stages of big data maturity. These differences
range from adopting big data practices for operational
improvement in selected functional areas or building
or revamping an organization’s value proposition to
completely transforming their business model based on
big data. At the more advanced stages, organizations
learn to monetize big data far beyond simply getting
better at what they are currently doing; learning
this lesson is an accomplishment that can mean a
fundamental shift for them. Environment readiness
plays a pivotal role in enabling such success, because
its effect is far greater than the evolution of individual
organizations’ internal capabilities and usage levels of big
data.
Nonetheless, policymakers and organizations in
general still have much to do if they want to realize the
full potential of big data. For their part, governments
throughout the world need to create a supportive
environment for the usage of big data to attract business
to their region. Meanwhile, organizations must act
in a timely manner to determine how they can most
effectively deploy big data. They will have to predict
what the world of data-driven insights will look like in the
medium term, anticipate which trends will lead there, and
position their organization accordingly.
Within the next five years, big data will become the
norm, enabling a new horizon of personalization for both
products and services. Wise leaders will soon embrace
the game-changing opportunities that big data affords
for their societies and organizations, and will provide the
necessary sponsorship to realize this potential. Skeptics
and laggards, meanwhile, look set to pay a heavy price.
NOTES
1 IBM, no date. “What Is Big Data?”
2 Constine 2012.
3 Aberdeen Group 2013.
4 McAfee and Brynjolfsson 2012, p. 6.
5 Gartner 2013.
6 The New York Times 2012.
7 ITP.net 2013.
8 The Economist Intelligence Unit 2013.
9 This chapter contains examples from many clients whose identities
are not stated due to confidentiality reasons.
10 Munford 2013.
11 Catts 2012.
12 In the UK, the initiative is available at http://data.gov.uk/; in
New York City it is available at https://data.cityofnewyork.us/.
13 Polonetsky and Tene 2013.
14 OECD 2013.
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Aberdeen Group. 2013. “Big Data Trends in 2013,” February 1. Available
at http://www.aberdeen.com/Aberdeen-Library/8244/RA-big-data-
trends.aspx.
Catts, T. 2012. “GE’s Billion-Dollar Bet on Big Data.” Bloomberg
Businessweek, April 26. Available at http://www.businessweek.
com/articles/2012-04-26/ges-billion-dollar-bet-on-big-data.
Constine, J. 2012. “How Big Is Facebook’s Data? 2.5 Billion Pieces of
Content and 500+ Terabytes Ingested Every Day.” Tech Crunch
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content-and-500-terabytes-ingested-every-day/.
The Economist Intelligence Unit. 2013. “The Evolving Role of Data in
Decision-Making.” Available at http://www.economistinsights.com/
analysis/evolving-role-data-decision-making.
Gartner. 2013. “Survey Analysis: Big Data Adoption in 2013 Shows
Substance Behind the Hype.” Available at http://www.gartner.com/
id=2589121.
IBM. No date. “What Is Big Data?” Available at http://www.ibm.com/
big-data.
ITP.net. 2013. “DWTC to Highlight Big Data at GITEX: Event Organiser to
Host First Ever Big Data Conference on October 22,” September
29. Available at http://www.itp.net/595102-dwtc-to-highlight-big-
data-at-gitex#.UkrZ9oasiSo.
McAfee, A. and E. Brynjolfsson. 2012. “Big Data: The Management
Revolution.” Harvard Business Review, October. Available at
http://hbr.org/2012/10/big-data-the-management-revolution.
Munford, M. 2013. “Don’t Follow the Leaders, Watch the Parking
Meters.” The Daily Telegraph, September 15. Available at
http://www.telegraph.co.uk/technology/news/10307926/Dont-
follow-leaders-watch-the-parking-meters.html.

The New York Times. 2012. “IDC Sizes Up the Big Data Market,” March
7. Available at http://bits.blogs.nytimes.com/2012/03/07/idc-
sizes-up-the-big-data-market/?_r=0.
OECD (Organisation for Economic Co-operation and Development).
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Transborder Flows of Personal Data. Available at
http://www.oecd.org/internet/ieconomy/oecdguidelinesonthe
protectionofprivacyandtransborderflowsofpersonaldata.htm.
Polonetsky, J. and O. Tene. 2013. “Privacy and Big Data: Making Ends
Meet.” 66 Stanford Law Review 25, September 3. Available at
http://www.stanfordlawreview.org/sites/default/files/online/topics/
PolonetskyTene.pdf.

CHAPTER 1.4
Big Data: Balancing the
Risks and Rewards of
Data-Driven Public Policy
ALEX PENTLAND
MIT
In June 2013, massive US surveillance of phone
records and Internet data was revealed by former
National Security Agency (NSA) contractor Edward
Snowden, who called these activities the “architecture
of oppression.” His disclosures ignited an overdue
public debate on the balance between personal
privacy and our growing digital capabilities regarding
the collection and use of personal data. Finding this
balance is an issue of vital and urgent interest to
corporations and governments as well as to ordinary
citizens around the world. This chapter will outline both
the risks and the rewards of this new age of big data,
address policy issues in this area, and provide practical
recommendations for a way forward.
Data about human behavior, such as census data,
have always been essential for both government and
industry to function. In recent years, however, a new
methodology for collecting data about human behavior
has emerged. By analyzing patterns within the “digital
breadcrumbs” that we all leave behind us as we move
through the world (call records, credit card transactions,
and global positioning system, or GPS, location fixes,
for example), scientists are discovering that we can
begin to explain many things—such as financial crashes,
revolutions, panics—that previously appeared to be
random events. These new tools, with the perspective
they provide on life in all its complexity, shape the
future of social science and public policy. Just as the
microscope and telescope revolutionized the study of
biology and astronomy, “socioscopes” have the potential
to revolutionize regulation and public policy.
The risk of deploying this sort of data-driven policy
and regulation comes from the danger of putting so
much personal data into the hands of either companies
or governments. Fortunately, new approaches to
regulation and technology that can help protect personal
privacy from exploitation have been developed. These
approaches can mitigate the problem of government
overreach as well. Both regulation and technology must
continue to evolve in order to provide more scientific,
real-time public policy while protecting citizens from the
dangers of exploitative companies or an all-knowing
authoritarian government. This chapter will provide
practical recommendations to achieve these goals.
A BIG DATA TAXONOMY
It is probably hopeless to try to provide a detailed
taxonomy of data types and uses because the
technology is progressing so quickly. But it is possible
to provide a broad taxonomy framed in terms of control.
The three main divisions within the spectrum of data
control are: (1) data commons, which are available to
all, with at most minor limitations on use; (2) personal
or proprietary data, which are typically controlled by
individuals or companies, and for which legal and
technology infrastructure must provide strict control and
auditing of use; and (3) the secret data of governments,

which typically has less direct public oversight and more
stringent controls. The issues of data commons will be
addressed first, followed by concerns about personal
and proprietary data, and, finally, issues of secret
government data.
The preferred lens for examining these issues is
experimentation in the real world rather than arguments
from theory or first principles, because using massive,
live data to design institutions and policies is outside of
our traditional way of managing things. In this new digital
era we cannot rely only on existing policy, tradition, or
even laboratory science, because the strengths and
weaknesses of big data analysis are very different
from those obtained through standard information
sources. To begin to manage our society in a data-
driven manner requires us to move beyond academic
debate and laboratory question-and-answer processes.
Instead, we need to try out new policy ideas within living
laboratories—real, diverse communities that are willing to
try a new way of doing things—in order to test and prove
our ideas. This is new territory and so it is important for
us to constantly try out new ideas in the real world in
order to see what works and what does not (see Box 1).
Data commons
The first entry in the data taxonomy is the data
commons. A key insight is that our data are worth more
when shared because they can inform improvements
in systems such as public health, transportation, and
government. Using a “digital data commons” can
potentially give us unprecedented ability to measure how
our policies are performing so we can know when to act
quickly and effectively to address a situation.
We already have many data commons available:
maps, census data, and financial indices, for example.
With the advent of big data, we can potentially develop
many more types of data commons; these commons
can be both accessible in real time and far more detailed
than previous, hand-built data commons (e.g., census
data, etc.). This is because the new digital commons
depend mostly on data that are already produced as a
side effect of ongoing daily life (e.g., digital transaction
records, cell phone location fixes, road toll records, etc.),
and because they can be produced automatically by
computers without human intervention.
One major concern with these new data commons
is that they can endanger personal privacy. Another,
secondary, concern involves the tension between
proprietary interests, both commercial and personal,
and the goal of putting data in the commons. Acceding
to these proprietary interests might tend to reduce the
richness of such a commons, which would diminish the
ability of such a data commons to enable significant
public goods.
To explore the viability of a big data commons, what
is perhaps the world’s first true big data commons was
unveiled on May 1, 2013. In this Data for Development
(D4D) initiative, 90 research organizations from around
the world reported hundreds of results from their analysis
of data describing the mobility and call patterns of the
citizens of the entire African country Côte d’Ivoire.1
The
data were donated by the mobile carrier Orange, with
help from the University of Louvain (Belgium) and the
MIT Human Dynamics Laboratory (United States), along
with collaboration from Bouake University (Côte d’Ivoire),
the United Nation’s Global Pulse, the World Economic
Forum, and the GSMA (the mobile carriers’ international
trade association). The D4D program was led by Nicolas
De Cordes (Orange), Vincent Blondel (Louvain), Alex
Pentland (MIT), Robert Kirkpatrick (UN Global Pulse), and
Bill Hoffman (World Economic Forum).
The research projects conducted by the 90
participating organizations explored the use of this data
commons, covering many different aspects of better
governance. An example of using the D4D data to
improve social equality was highlighted by work done
by researchers at the University College of London,
who developed a method for mapping poverty from
the diversity of cell phone usage. As people have
more disposable income, they explore or sample their
environment more, and their patterns of movement
and patterns of phone calls become increasingly
diverse. Measurement of this additional exploration
allows us to make a surprisingly accurate estimate of
their disposable income. Another example of using the
D4D data to enhance social equality is the mapping of
ethnic boundaries by researchers from the University
of California, San Diego. This method relies on the
fact that ethnic and language groups communicate far
more within their own group than they communicate
with other groups. This project is significant because,
while we know that ethic violence often erupts along
such boundaries, the government and aid agencies are
usually uncertain about the geography of these social
fault zones.
The D4D data were also utilized to understand and
promote operational efficiency through an analysis of
Côte d’Ivoire’s public transportation system by IBM’s
Dublin laboratory. This analysis showed that, for very
little cost, the average commute time in Abidjan—Côte
d’Ivoire’s biggest city—could be cut by 10 percent.
Other research groups demonstrated similar potential for
operational improvements in the areas of government,
commerce, agriculture, and finance.
Finally, examples of using D4D data to improve
social resiliency include analysis of disease spread
by groups from Novi Sad University (Serbia), École
Polytechnique Fédérale de Lausanne (EPFL,
Switzerland), and Birmingham (United Kingdom). These
research groups showed that small changes in the public
health system could potentially cut the spread of flu by
20 percent as well as significantly reduce the spread of
HIV and malaria.
Chapter 1.4: Big Data: Balancing the Risks and Rewards of Data-Driven Public Policy

Box 1: The future of big data and governance
The Data for Development (D4D) data commons is only a
small first step toward improving governance by using big
data. Much more can be accomplished because our current
understanding of policy and human society is based on
very limited data resources. Currently, most social science
is based either on analysis of laboratory experiments or on
survey data. These approaches miss the critical fact that
it is the details of which people you interact with, and how
you interact with them, that truly matter. Social phenomena
are made up of billions of small transactions between
individuals—people trading not only goods and money but
also information, ideas, or just gossip. There are patterns in
those individual transactions that drive phenomena such as
financial crashes and Arab Springs. We need to understand
these micro-patterns because they do not just average out
to the classical way of understanding society. Big data gives
us—for the first time—a chance to view society in all its
complexity, composed of millions of networks of person-to-
person exchanges.
Figure A compares social science living labs with
traditional experiments. The horizontal axis presents the
duration of the data collection; the vertical axis shows the
richness of the information collected.
If we had an all-seeing view, we could potentially
arrive at a true understanding of how society works and
develop scientifically proven methods to fix our problems.
Unfortunately, as illustrated in Figure A, almost all data from
traditional social science (labeled “1” in the figure) are near
the (0,0) coordinate, meaning that these datasets represent
information gathered from under a hundred people and for
only for a few hours. The studies labeled “2” and “3” are
some of the largest social science studies to date.1
In the
last decade, computational social scientists have begun
to discover how to leverage big data and have been using
datasets from companies such as cell phone carriers and
social media firms. Typical examples of these studies are
labeled “4.”
Unfortunately, even these large datasets are
impoverished because they measure only a few variables
at a time, thus providing only a very limited view of human
nature. Recently data scientists have developed living lab
technologies for harvesting digital breadcrumbs, and are now
obtaining much richer descriptions of human behavior. The
studies labeled “5,” “6,” “7,” and “8” are living lab studies that
use smart phones or electronic name badges (sociometers)
to collect data.2
The point labeled “9” is the D4D dataset that
covers the entire country of Côte d’Ivoire.3
Just a brief examination of Figure A makes it easy to see
that these living lab datasets are many orders of magnitude
richer than previous social science datasets. These large,
digital datasets contain extraordinary amounts of objective,
continuous, dense data that allow us to build quantitative,
predictive models of human behavior in complex, everyday
situations.
Importantly, the point labeled “10” shows where the
world is headed. In just a few short years we are likely to have
available incredibly rich data about the behavior of virtually
all of humanity on a continuous basis. The data mostly
already exist in cell phone networks, credit card databases,
and elsewhere, but currently only technical gurus have
access to them. As these digital data become more widely
available for scientific inquiry, we will be able to understand
and manage ourselves in ways better suited to our complex,
interconnected, and networked society.
Notes
1 See Barker 1968; Dawber 1980.
2 For details about these living lab studies, see Pentland
2014; Mobile Territorial Lab (MTL), available at http://www.
mobileterritoriallab.eu/.
3 See the D4D challenge, available at http://www.d4d.orange.com/
home.
Figure A: Qualitative overview of social science living labs and traditional experiments
Note: Datasets identified in the figure are derived from the following: 1 = most social science experiments, 2 = the Midwest Field Station Study, 3 = the Framingham heart study,
4 = large call record datasets, 5 = reality mining, 6 = social evolution, 7 = friends and family, 8 = sociometric badge studies, 9 = the D4D dataset, and 10 = where the world is headed
(see text for explanation).
1 2 34
5
6
7
8 9
10
l Traditional social science studies
l Social physics studies
l Where the world is headed
Duration of observation, log scale from minutes to years
No.ofmeasurementsperperson,perminute

These selected results are just a small sample of
the impressive work that is made possible by this rich
and unique data commons. These results and others like
them are available at http://www.d4d.orange.com/home.
Each of these D4D research projects has demonstrated
the great potential of a big data commons for improving
people’s living conditions. From the point of view of
Orange, it also demonstrates the potential for new
lines of business that combine this data commons with
customers’ personal data: imagine phone applications
that advise commuters about which bus will get them to
work quickest, or that help citizens reduce their risk of
catching the flu.
The work of these 90 research groups also
suggests that many of the privacy fears associated
with the release of data about human behavior may be
generally misunderstood. In this data commons, the
data were processed by advanced computer algorithms
(e.g., sophisticated sampling and the use of aggregated
indicators) so that it was unlikely that any individual could
be re-identified. In fact, no path to re-identification was
discovered even though several of the research groups
studied this specific question.
In addition, although the data were freely available
for any legitimate research in which a group was
interested, the data were distributed under a legal
contract that specified that they could be used only
for the purpose proposed and only by the specific
people making the proposal. A similar technology-
legal framework is used in trust networks described in
the next section. The use of both advanced computer
algorithms and contract law to specify and audit how
personal data may be used and shared is the goal of
new privacy regulations in the European Union, the
United States, and elsewhere.
Personal and proprietary data
The second category in the data taxonomy is personal
and proprietary data, which are typically controlled
by individuals or companies, and for which legal and
technology infrastructure that provides strict control and
auditing of use is needed. The current best practice is
a system of data sharing called trust networks.2
Trust
networks are a combination of a computer network
that keeps track of user permissions for each piece of
personal data and a legal contract that specifies both
what can and cannot be done with the data and what
happens if there is a violation of the permissions. This
is the model of personal data management that is most
frequently proposed within the World Economic Forum
Personal Data Initiative.
In such a system, all personal data have attached
labels specifying what the data can, and cannot, be
used for. These labels are exactly matched by terms
in a legal contract between all the participants stating
penalties for not obeying the permission labels and
giving the right to audit the use of the data. Having
permissions, including the provenance of the data,
allows automatic auditing of data use and allows
individuals to change their permissions and withdraw
their individual data.
Today, long-standing versions of trust networks have
proven to be both secure and robust. The best known
example is the SWIFT network for inter-bank money
transfer; its most distinguishing feature is that it has
never been hacked. When asked why he robbed banks,
bank robber Willie Sutton famously said, “Because
that’s where the money is.” In today’s world, the SWIFT
network is where the money is—trillions of dollars are
moved through the network each day. This trust network
has not only kept the robbers away, but it also makes
sure the money reliably goes where it is supposed to go.
Until recently, such systems were available only to the
“big guys.” To give individuals a similarly safe method
of managing personal data, the MIT Human Dynamics
Laboratory (http://hd.media.mit.edu), in partnership
with the Institute for Data Driven Design (http://idcubed.
org), have helped build openPDS (open Personal Data
Store)—a consumer version of this type of system.
We are now testing it with a variety of industry and
government partners.3
A major concern about trust networks is the cost
associated with keeping track of permissions and
supporting the capability for automated auditing. Since
many companies already maintain such data structures
in order to support internal compliance and auditing
functions, the cost concern does not appear to be a
major barrier. Another more serious concern, however,
is the extent to which incidental data about human
behavior must be included in the permissions and
auditing framework. Such data are typically collected
in the course of normal operations in order to support
those operations (e.g., the location of a cell phone is
required to complete phone calls), but without specific
informed consent. A final concern is that a trust network
system may be too complex for average people to use,
or that it will not inspire (or deserve) the sort of user trust
that the name suggests.
In order to investigate these concerns, a living
lab has been launched with the city of Trento in Italy,
supported by Telecom Italia, Telefonica, the MIT Human
Dynamics Laboratory, the Fondazione Bruno Kessler,
the Institute for Data Driven Design, and local companies
within Trento. Importantly, this living lab has the approval
and informed consent of all its participants—they know
that they are part of a real-world experiment whose goal
is to invent a better way of living.4
The objective of this living lab is to develop
new ways of sharing data to promote greater civic
engagement and information diffusion. One specific goal
is to build upon and test trust-network software such
as the openPDS system by deploying a set of “personal
data services” designed to enable users to collect,
store, manage, disclose, share, and use data about

themselves. For example, the openPDS system lets
the community of young families learn from each other
without the work of entering data by hand or the risks
associated with sharing through current social media.
These data can then be used for the personal self-
empowerment of each member, or (when aggregated)
for the creation of a data commons that supports
improvement of the community—for example, a map
that shows disposable income for each neighborhood
can stimulate better distribution of community services.
The ability to share data safely should enable better idea
flow among individuals, companies, and government;
we want to see if these tools can in fact increase
productivity and creative output at the scale of an entire
city.
The Trento living lab will also investigate how to
deal with the sensitivities of collecting and using deeply
personal data in real-world situations. For example, it
will explore different techniques and methodologies
to protect the users’ privacy while at the same time
being able to use personal data—typically mobility,
financial, and medical records—to generate a useful data
commons. It will also explore different user interfaces
for privacy settings, for configuring the data collected,
for the data disclosed to applications, and for those
data shared with other users, all in the context of a trust
framework. Although the Trento experiment is still in its
early days, the initial reaction from participating families
is that these sorts of data-sharing capabilities are
valuable, and they feel safe sharing their data using the
openPDS system.
Government data
The third category in the taxonomy is secret government
data. A major risk of deploying data-driven policies and
regulations comes from the danger of putting so much
personal data into the hands of governments. But how
can it happen that governments, especially authoritarian
governments, choose to limit their reach? The answer is
that unlimited access to data about the citizen behavior
is a great danger to the government as well as to its
citizenry. Consider the NSA’s response to the recent
Snowden leaks:
“This failure originated from two practices
that we need to reverse,” Ashton B. Carter,
the deputy secretary of defense, said
recently. “There was an enormous amount
of information concentrated in one place,”
he said. “That’s a mistake.” And second,
no individual should be given the kind of
access Mr. Snowden had, Mr. Carter said.5
That is, the government must organize big data
resources in a distributed manner, with each different
type of data separated and dispersed among many
locations, using many different types of computer
systems and encryption. Similarly, human resources
should be organized into cells of access and permission
that are localized both spatially and by data type.
Both computer and human resources should always
be redundant and fragmented in order to avoid overly
powerful central actors.
The logic behind this observation is that databases
that have different types of data that are physically and
logically distributed, and that also have heterogeneous
computer and encryption systems, are hard to attack,
both physically as well as through cyberattack. This
is because any single exploit is likely to gain access
to only a limited part of the whole database. Similarly,
the resilience of organizations with a heterogeneous
cell-like human and permissions structure is
familiar from intelligence and terrorist organizations.
Importantly, resistance to attack by adopting a
distributed organization is a particularly pressing issue
for authoritarian governments, because unfettered
access to data about citizen behavior can be a major
aid to organizing a successful coup to overthrow the
government.
What does all this have to do with the danger that
a big data government will trample individual freedoms?
The key insight is that for these types of data systems,
each type of data analysis operation has a characteristic
pattern of communication between different databases
and human operators. As a consequence, it is possible
to monitor the functioning of the data analysis process
without gaining access to, or endangering, the analysis
content. In short, one can use “metadata about
metadata” in order to monitor the use of metadata,
and with some reasonable confidence one can ensure
that only normal and usual analysis operations are
being conducted without reference to specific content.
Governments that structure their data resources in this
manner can more easily monitor attacks and misuse of
all sorts.
As a concrete example, let us assume a system
in which different types of databases are physically
distributed. In this case one can observe the amount
and pattern of traffic between the different databases.
These patterns are characteristic of the analysis being
performed, and so deviations from the normal patterns
of communication between databases are cause for
concern. In this manner, an open civil authority can
perform substantial, fairly effective monitoring of the
functioning of a classified agency. In most cases it is
sufficient that each element of the system monitor only
local traffic.
A familiar example of this type of monitoring is
the “many eyes” security strategy. When patterns of
communication among different departments are visible
(as with physical mail), then the patterns of normal
operations are also visible to many employees, even
though the content of the operations (the content of

the requested records) remains hidden. For example, a
health official responsible for maintaining health records
will be able to see if those records are suddenly being
accessed by the finance records office with unusual
frequency, and may inquire if that is proper. In contrast,
when copies of all the data types are all in one place (as
when all the records are located in one filing cabinet), it
is easy for people to conduct unauthorized analyses.
The computer architecture for the type of system
that relies on multiple, distributed types of oversight
is very similar to that of the trust networks described
in the previous section: distributed data stores with
permissions, provenance, and auditing for sharing
among data stores. In this case, however, the data
stores are segmented by their referent—for example,
tax records for individuals, tax records for companies,
import records from country X to port Y, and so
on—rather than having one data store per person.
Because the architecture is so similar to the citizen-
centric personal data stores, it enables easier and safer
sharing of data between citizens and government. For
this reason, several states within the United States are
beginning to test this architecture for both internal and
external data analysis services.
Finally, it should not escape the reader’s attention
that all of these lessons also apply to companies with
large, complex databases. Misbehavior by employees,
industrial espionage, and cyberattack are among the
greatest dangers that companies face in the big data
era. A distributed architecture of databases joined with
a network that supports permissions, provenance,
and auditing can reduce risk and increase resilience of
companies’ internal data analysis functions.
SUMMARY
We are entering a big data world, where governance
is far more driven by data than it has been in the past.
Basic to the success of a data-driven society is the
protection of personal privacy and freedom. Discussions
at the World Economic Forum have made substantial
contributions to altering the privacy and data ownership
standards around the world in order to give individuals
unprecedented control over data that are about
them, while at the same time providing for increased
transparency and engagement in both the public and
private spheres.
We still face the challenge that large organizations,
in particular governments and corporations, may be
tempted to abuse the power of the data that they
hold. To address this concern, we need to establish
best practices that are in the interest of both large
organizations and individuals. This chapter has
suggested one path that can limit potential abuses of
power while at the same time providing greater security
for organizations that use big data. The key policy
recommendations for all large organizations, commercial
or government, are that:
1. Large data systems should store data in a
distributed manner, separated by type (e.g., financial
vs. health) and real-world categories (e.g., individual
vs. corporate). These systems should be managed
by a department whose function is focused on
those data, with sharing permissions set and
monitored by personnel from that department.
Best practice would have the custodians of data be
regional and use heterogeneous computer systems.
With such safeguards in place, it is difficult to attack
many different types of data at once, and it is more
difficult to combine data types without authentic
authorization.
2. Data sharing should always maintain provenance
and permissions associated with data, and should
support automatic, tamper-proof auditing. Best
practice would share answers only to questions
about the data (e.g., by using the pre-programmed
structured query language, or SQL, queries
known as “Database Views”) rather than sharing
the data themselves, whenever possible. This
allows improved internal compliance and auditing
and helps to minimize the risk of unauthorized
information leakage by providing the minimum
amount of information required.
3. Systems controlled by partner organizations, and
not just one’s own systems, should be secure.
External data sharing should take place only
between data systems that have similar local
control, permissions, provenance, and auditing,
and should include the use of standardized legal
agreements such as those employed in trust
networks, as described earlier. Without such
safeguards, data can be siphoned off at either the
data source or at the end consumer, without even
attacking central system directly.
4. The need for a secure data ecosystem extends to
the private data of individuals and the proprietary
data of partner companies. As a consequence, best
practice for data flows to and from individual citizens
and businesses is to require them to have secure
personal data stores and be enrolled in a trust
network data sharing agreement.6
5. All entities should employ secure identity credentials
at all times. Best practice is to base these
credentials on biometric signatures.7
6. Create an “open” data commons that is available
to partners under a lightweight legal agreement,
such as the trust network agreements. Open data
can generate great value by allowing third parties to
improve services.
Although these recommendations might seem
cumbersome at first glance, they are for the most part
easily implemented with the standard protocols already

found within modern computer databases and networks.
In many cases, the use of distributed data stores and
management are already part of current practice, and
so the entire system will be simpler and cheaper to
implement than a centralized solution: all that is really
new is the careful use of provenance, permissions, and
auditing within a legal or regulatory framework such as a
trust network. Most importantly, these recommendations
will result in a data ecosystem that is more secure and
resilient, allowing us to safely reap the advantages of
using big data to help set and monitor public policy.
NOTES
1 See the D4D challenge, available at http://www.d4d.orange.com/
home.
2 For examples of trust networks, see Pentland 2009; World
Economic Forum 2011; and the Institute for Data Driven Design,
available at http://idcubed.org.
3 For details about openPDS, see http://idcubed.org/open-platform/
openpds-project/.
4 For information about the Mobile Territorial Lab (MTL), see
http://www.mobileterritoriallab.eu/.
5 Sanger 2013.
6 Pentland 2009; World Economic Forum 2011; http://idcubed.org.
7 See http://openid.net/connect/.
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Dawber, T. 1980. The Framingham Study: The Epidemiology of
Atherosclerotic Disease. Cambridge, MA: Harvard University
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ID3 (Institute for Data Driven Design, or idcubed). Available at
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MTL (Mobile Territorial Lab). Available at http://www.mobileterritoriallab.eu/.
OpenID Connect. Available at http://openid.net/connect/.
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a New Deal on Data.” In The Global Information Technology
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main/fullreport/files/Chap1/1.6.pdf.
———. 2014. Social Physics: How Good Ideas Spread—The Lessons
from a New Science. New York: Penguin Press.
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The New York Times Sunday Review, August 3. Available at
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New Asset Class. Geneva: World Economic Forum.
Available at http://www3.weforum.org/docs/WEF_ITTC_
PersonalDataNewAsset_Report_2011.pdf.

CHAPTER 1.5
Managing the Risks
and Rewards of Big Data
MATT QUINN
CHRIS TAYLOR
TIBCO
One of the biggest challenges of the term big data is
deciding on a standard definition of what those words
really mean. For many companies that have worked in an
environment of large datasets, fast-moving information,
and data that lack traditional structure, working in an
environment of big data is just business as usual. In
this chapter we will discuss how managing the growing
challenge of data is not new for a regional healthcare
organization in the Midwestern United States, a global
logistics company, and a major American retailer. But for
a majority of organizations, which have neither integrated
data nor built a strategy around its use, the term big
data itself is a way to express the sudden digitization
of many things that have been with us forever but were
not previously captured and stored as data. For most
companies, big data represents a significant challenge
to growth and competitive positioning. In some cases, it
represents the survival of the business.
BIG DATA: RISKS AND REWARDS
Digitization itself is not new, but the maturation and
availability of the Internet; the rapid growth of mobile
computing; and, more recently, the addition of sensor
data (data derived from devices that sense their
environment) to the mix have all pushed the boundaries
of how we think about data and its uses. The term big
data represents the need for a new way of thinking but
also implies new tools and new ways of managing data.
Like many things, data can be used to do positive things
for the world, but it can also be used to manipulate,
embarrass, or repress. Data can be highly accurate
and efficiently structured or unstructured, fragmented,
and highly suspect. Data can also be managed well or
carelessly. Big data, in its outsized properties, amplifies
those effects. It is in those extremes that the risks and
rewards of big data are decided.
THREE KEY BIG DATA TRENDS
As the world becomes more familiar with big data,
three key trends that have a significant impact on those
risks and rewards are emerging. First and foremost,
big data leverages previously untapped data sources.
Those sources are of several types. The first includes
wearable devices that stream data about an individual
and his or her surrounding environment on a moment-
by-moment basis—such sensors include the applications
on a smartphone that sense movement. The sensor in
a runner’s shoe is a very consumer-facing example, but
business-facing sensors, which track all kinds of things,
are proliferating very quickly. A pacemaker is a sensor
that has been around a while (the newer models give
feedback to healthcare workers).
The next type comprises connected sensors that
instantly digitize and report what is happening in any
moment and in any location. Examples of this type
include the global positioning system (GPS) device that
reports location back to a central computer or a user,

and devices in the soil of a farm that sense when and
how much to irrigate. There are also sensors in trains,
for example, that watch for signals that maintenance is
necessary before a human could ever see them, such
as brake heat, brake wear, movement in the rails, and so
on. This new breed of sensors is coming into service and
is connected to the Internet, making big data even bigger
than human-generated information.
The third type of sensor provides constant
reporting by machines that perform the work critical
to our security, health, and lifestyle. Machines can be
something as large as an aircraft or locomotive or they
can be components of one of those things. Some of
the most interesting of these sensors are the ones
that measure the way an aircraft engine is performing
mid-flight. Machines used to be purely mechanical but
are increasingly computer controlled. Those computer
controls mean not only that data are constantly being
fed into machines but that they are also coming out of
machines at a quickly increasing rate.
We have reached a point of information discovery
that reveals correlation before causation, leaving
researchers scratching their heads to find the underlying
causes for correlations that data analysis clearly
demonstrates. TIBCO’s chief executive officer, Vivek
Ranadive, is fond of saying that we have reached a point
where we may know the “what” without knowing the
“why.”
The previously untapped information sources create
a data ecosystem that can be modeled in a way that
blends historical with in-the-moment information and
is remarkably useful for anticipating the future. These
models accurately predict such diverse outcomes as the
spread of disease, the failure rate of aircraft components,
and consumer behaviors. Big data’s effectiveness is
tightly coupled to an organization’s ability to bring the
right data together in the right moments that allow for the
right response and outcome. Whatever we may know
today, the continued discovery of previously untapped
data sources will continue to change and improve our
models, allowing us to better anticipate future events
and to continue to increase our ability to affect desired
outcomes.
The desire to affect outcomes brings about the
second trend of big data: the need for automation
technologies. Richard Hackathorn wrote about the value-
time curve of information back in 2004 in “Real-Time to
Real-Value,” just as the world was becoming broadly and
acutely aware of the explosion of data.1
Hackathorn’s
curve describes the decreasing value of data over time
as it passes through stages of use (Figure 1).
The challenge of the decreasing value of data over
time has become even more meaningful in the age of
big data. Today, the volume, velocity, and variety of data
continue to push the curve down and to the right as
organizations struggle to capture, analyze, and decide
in a gradually more difficult environment. Added to this
complexity is the increasing access to real-time data
that leaves organizations in some industries attempting
to reduce their response time to microseconds,
understanding that this is a crucial part of being
successful in their business.
The value-time curve challenge makes big data
management a function of creating automation wherever
possible. Machines have always been humanity’s friend
in making work more efficient, and big data follows the
same path. Big data’s growth in each of its dimensions
eliminates the ability for humans to intervene and
reprogram processes in real time, opening the door for
better and better tools that can manage data far more
quickly and efficiently than a human can. Data exist in
a moment, ready for decision and action, but there is a
higher-level purpose for information. Data comprise the
digital representation of events, or things that happen in
patterns that occur over time, in conjunction with other
events or in isolation, and even with things that may be
expected but do not occur (such as when a patient fails
to pick up a prescription after being discharged from a
hospital, starting a likely string of events that will lead to
readmission).
The idea of keeping track of what does not occur is
a level of complexity higher than the old ways of waiting
for data to arrive or change. Automation is especially
well suited to the complexity of predicting, and then
anticipating, events. In many organizations, automation
is also a significant part of the actions that events
precipitate.
The big data conversation often centers on the use
of machines as the best resource for the storage and
analytic processing of vast amounts of data, but this is
only a piece of the story. Automation is increasingly a
logical response to the need to find, filter, and correlate
each piece of data as it flows over the enterprise so
that decisions can be made—some through automation
and some using a hybrid approach combining human
and machine. Once decisions are reached, automation
becomes the path for taking action in the shortest time
frame possible before the value of data decays further.
The third trend being driven by big data is the
necessity for adaptable, less fragile systems. For
big data to leverage previously untapped sources of
information, organizations need to quickly adapt to
the opportunities and risks represented by these new
sources. Automated systems that manage big data
ecosystems cannot be developed around rigid schemas
that require redevelopment for each new stream of
information. Instead, systems need to absorb new
information in an adaptable way that also adds value to
existing data that have already been collected. Adaptable
systems treat new sources of data coming constantly as
the means to improve analytical models, create better
decisions, and drive more appropriate actions.
Chapter 1.5: Managing the Risks and Rewards of Big Data

RESOLVING TWO PRIMARY CHALLENGES OF
BIG DATA
Most organizations need to overcome two primary
challenges before becoming productive with big data.
The first is the need for powerful visualization that
allows the business to explore data to find questions
worth answering. This stands the traditional business
intelligence model on its head, as the pre–big data
model began with the business asking a question and
ended with information technology structuring data
to answer those questions in a very repeatable way,
typically as dashboards. Visualization instead begins with
capturing all data available so that multi-structured and
iterative discovery can take place that reveals information
with or without having the right question. Visualization
lets the data speak for themselves.
Humans are extremely well suited to visual analysis.
Our brains are wired to very rapidly assimilate what we
see and spot patterns. Using our eyes, we can spot a
trend or an outlier in a fraction of a second, far faster
than we can by sifting through numbers on a screen. If a
picture is worth a thousand words, visualization is worth
petabytes, terabytes, and more of raw data. Visualized
data and the human mind make for a highly efficient
combination. Most importantly, visualized data have the
effect of engaging the non-technical but business-savvy
human in the iterative process of discovering exploitable
insight. This lessens the organization’s reliance on
technical resources and, specifically, on data scientists.
The second hurdle that organizations face is the
need to manage ever-larger amounts of data. Systems
scoped for today’s needs quickly become insufficient
when the data are increasing in size, speed, and
complexity. Unfortunately, when people talk about “big
data” they often use the term to compartmentalize it and
give it boundaries. This is a natural reaction and harkens
to the beginning of computerization when data were
processed as batches of transactions that represented a
finite amount of information. Thinking of big data in those
terms fails to take into account all of the data being
created everywhere, every day. This compartmentalized
view can also deprecate data that may not appear
useful or valuable or may be difficult to process. At a
point in the future, organizations will very likely look back
and wish they had considered all data when deciding
what to store. When we consider data without specific
boundaries, we can focus our efforts on linking data
together and analyzing them more broadly. We will
probably find the data have value for a wider range of
people in the organization than originally anticipated.
When we consider all data, we can see the value
of discovering the connectivity of data. This brings into
consideration different data types that are used to adorn
our original data and make them more valuable as a
source of visual, predictive, and operational analytics.
Why does that matter? We have grown accustomed to
having instantaneous answers to our questions. As data
grow, they have the very real likelihood of slowing down
how decisions are made. Nonlinear growth taxes our
systems and creates the scenario that every day we get
bogged down more as untapped data sources become
newly available, our clever automations become less
effective, and our systems seem less adaptable than
before. An all-data approach allows the organization
to see today’s information as the best we have in the
moment, knowing that we will continue to layer on more
DATALATENCY
ANALYSIS LATENCY
DECISION LATENCY
Business event
Data stored
Information delivered
Action taken
Figure 1: The value-time curve
Source: Hackathorn 2004.
l Process entry and exit
l Process intermediate steps
Time
Value

data—not with the goal of having a larger dataset, but
instead with the goal of using all of the data available to
gain the best outcome. Rather than slowing down the
results, using all available data takes into account data
linkages and permits a broad analysis that allows the
most organizational clients to constantly arrive at the
best possible outcome.
Enabling the organization with visualization and the
constantly additive benefits of all data allows experts
to be able to explore data to find their value. For a
retailer, that means being able to explore diverse data
that include historical visits to the website as well as
transactions completed or shopping carts abandoned;
with the addition of geographical information from a
mobile society, the retailer has an ability to understand
the ambient circumstances at the time decisions are
being made.
ENSURING THAT HUMANS STAY IN THE LOOP
For exactly this reason we need to take a very careful
approach to how big data is being used and apply the
right level of oversight. There are two specific reasons for
having an appropriate governance model, each tackling
the problem from the opposite perspective. The first is
a need to ensure that data are not being used in a way
that goes against the organization’s best interests. Such
unfortunate (even inappropriate) uses can be the result of
rogue individuals with no checks and balances on their
access and actions, or it can be the result of individuals
acting with the best intentions but incurring unintended
consequences that go against the goals of the
organization. Data are very powerful, and organizations
need to ensure that information is being collected,
stored, analyzed, and acted upon in ways that can be
audited and that raises alarms when necessary.
The second need for governance is demonstrated
by the danger of having machines talking to machines
without a human supervising the conversation. Systems
need to leave an aperture for control by humans to avoid
the problems of passive neglect or runaway processing.
Finding the right balance is the challenge, and it involves
looking at the value of the decisions being reached and
the risk associated with the decision. There is a broad
spectrum of judgments that covers small, incremental
decisions that have moderate impact on an overall
risk profile versus large, occasional decisions that can
have enormous impact. Machines are exceptionally
good at monitoring and executing detail, but the
need for humans to focus on the macro decisions is
significant. Consider the car analogy: a human cannot
be involved in every firing of every cylinder. The human
has absolute responsibility, however, for the speed of the
car under the current conditions, monitoring the engine
temperature, and a host of other variables.
STRIKING A PRIVACY BALANCE
We have watched the sharing of personal data increase
year after year since people first connected across
the Internet. Many of the risks and rewards of big data
are coupled tightly to the use of all of those data. On
the reward side, data can be used to create far better
customer service by knowing the customers’ needs and
histories. They can be used to create more personalized
offers based on customers’ preferences and their loyalty
to a brand. From this perspective, data can be used to
engage the customer and to create a better relationship
that serves everyone’s needs. Healthcare-related
personal information improves treatment and saves lives
both at the individual level and in aggregate, as clinical
trials of sample patients give way to all data about every
patient.
Personalization and healthcare offer two standout
opportunities for big data to reward us. At the same
time, big data comes with privacy concerns that are
not simply related to technology but are also about very
human things such as privacy, all-knowing “creepiness,”
and personal security. Given enough personal data,
information can be correlated that can be both unsettling
and unwanted. Today’s public, legislative, and legal
sentiments may not be tomorrow’s, and these attitudes
tend to diverge by government and region of the world.
What is standard practice in terms of collecting personal
information in the United States is frowned upon in
many parts of Europe. Managing the “Facebook Effect,”
where people willingly share ever-increasing amounts of
personal information, is a challenge for individuals and
governments as well as for the software companies that
sit in the middle, confronted with inconsistent norms and
laws across different locations in the world.
Privacy paradigms are in constant flux, but the need
for a consistent approach to meet privacy expectations
never changes. Protecting privacy has, at its roots,
the need to protect data both at a discrete level and,
maybe even more importantly, at an aggregate level.
Learning a great deal about a person by combining
factors that may seem harmless at a discrete level but,
when taken together, may give away information that
the person would not want generally known is one such
example. This could happen, for instance, by combining
someone’s Facebook status with the location where he
or she logged in to pay an electric bill with the home
zip code; this could target wealthy people by knowing
that they are not at home, making them vulnerable to
burglaries. Each discrete piece of information is not
meaningful, but in the aggregate can make someone a
potential victim.
Systems exist that can manage the access,
movement, and dissemination of data, but in our haste
to build out the largest datasets and the maximum
computational power, the need to put the right controls
in place has been consistently overlooked. Some of
this has been naiveté, and some has been a deliberate

stretching of the boundaries of individual expectations.
Throughout the evolution of big data, the capability
to govern data appropriately has existed, but unless
organizations make the choice themselves or are pushed
by legal or public pressure, the protection of personal
privacy remains a low priority.
SHOWING BIG DATA’S SOPHISTICATED SYSTEMS
Gaining benefits from big data while mitigating risks is
entirely a matter of data systems sophistication. This
section will explore three examples that demonstrate the
successful use of big data.
The first example of that sophistication is on display
at a major network of hospitals in the Midwest to
address the problem of sepsis—the systemic infection
of the body—which is a constant threat to hospitalized
patients. Sepsis is usually acquired in the healthcare
facility; it is not the reason a patient arrives. Instead,
sepsis appears somewhere between a patient’s travel
between the emergency room, the laboratory, the
radiology department, and any other department where
treatment is given. If not treated immediately, sepsis
usually results in the death of the patient.
This healthcare company realized that, in order
to tackle the sepsis problem, they had to create
a sophisticated system that could follow a patient
throughout his or her stay. The system needed to track
patient data despite that patient’s location within the
hospital and despite the siloed information technology
systems that are all too common in healthcare. Most
of all, the system needed to bring data together in a
way that allows high-speed correlation, based on prior
analysis of sepsis data, so that medical staff can be
alerted within life-saving time frames. This company’s
sophisticated system was successful at significantly
shortening time frames for response to sepsis and
significantly decreased the mortality rate in their facilities.
They were successful enough, in fact, to allow their
system to be turned into a Software-as-a-Service and
contracted to other facilities.2
The second example is one of logistics. Like
healthcare, logistics is an age-old practice undergoing
big data transformation. It has become far more
complicated in recent years because of the explosion
of data that connect the customer’s customer and the
supplier’s supplier. We are able to know significantly
more in the form of digital data that not only allow the
prediction of outcomes but that also allow us to make
operational decisions at any point along the supply
chain. For a global package delivery company, knowing
their business means being able to access all available
data to monitor not just the arrival and departure of
aircraft but also the aircraft altimeter and attitude in
order to provide additional layers of data that provide
better insight on the nuanced status of the flight.3
In a
similar fashion, today’s complex contracts encompass
the global movement of pharmaceuticals and other
sensitive cargos that require constantly monitoring all
data. A global logistics company must monitor discrete
data such as package temperature, location, and time to
delivery that continually describe a shipment’s ambient
conditions; furthermore, these data must be available
alongside expiration data and acceptable data ranges.
Those aggregate data form the basis for ensuring
non-stop compliance to local and international
standards for moving items that require special handling.
Those same data ensure that contract terms are
being respected and provide the basis for improving
profitability while decreasing waste and inefficiency
within a contracted service. It is a gift that keeps on
giving, as detailed historical shipment data allow better
pricing of potential new contracts, making the logistics
carrier more competitive and reducing the risk of
negotiating and accepting poor contracts. Without the
ability to manage all relevant data, logistics companies
and their customers would be unable to effectively move
cargoes that bring enormous benefits to all parts of the
planet.
The third example is seen in retail markets. In retail,
the management of big data supports a brand’s ability
to predict the best product offering and to establish
effective marketing and loyalty programs. It also supports
better ways to sell and greatly improves customer
service execution.4
Big data offers an enormous reward
to retail because successful selling is ultimately about
having an excellent understanding of customers and the
circumstances in which they buy. Even more importantly,
successful retail is about creating the circumstances
that turn a customer into a fan. A fan feels a personal
connection to the brand and is much more likely to be an
advocate. From a revenue perspective, a fan has a much
greater total lifetime value.
But creating a fan is not a simple exercise in better
customer service. Predictive analytics, heavily dependent
on powerful visualization, form the basis for knowing the
best moments and the best ways to engage with the
customer. Understanding the past is key to predicting
the future, and visualization reveals the meaningful
patterns in data that tell us what happened under a host
of variables in the past. Visual analytics tell the retailer
what can be anticipated in today’s real-time situations
and set the stage for blending information streaming
constantly from the website, store, and logistical
systems, along with data coming from mobile devices.
That information is vitally important to knowing not only
how to provide information and offers to help a customer
through a purchase, but also how to best serve a
customer’s needs after products have been purchased.
The brand that knows its customers using this approach
is leaps and bounds ahead of the one that lacks these
capabilities.
Although the rewards are clear, a risk remains in
gaining the customer’s favor while requiring access to
so much personal information. Loyalty programs are the

ideal way to gain that access and avoid the creepiness
factor. Focused customer loyalty management elicits
the customer’s permission through a system of rewards
and exclusive offers that provides benefit back to the
customer, mitigating the risk of a brand being perceived
as stalking the customer or invading their privacy.
ENSURING THE BENEFITS, MITIGATING THE RISKS
Managing the three key trends of leveraging previously
untapped data sources, using automation wherever
possible, and creating less fragile data systems are
crucial parts of ensuring the benefits of big data while
mitigating its risks. Accomplishing these three objectives
requires successfully meeting big data’s two main
challenges: the need to visualize by using analytics tools
and the need to systematically discover, capture, govern,
and secure ever-larger amounts of data.
Big data has a remarkable ability to change the
world. Its benefits need to be considered as a function
of how well its risks are managed. Truly expert handling
of big data brings the reward of being able to react
to world-changing events, both big and small, at an
unprecedented rate and scope. Epidemics can be
tracked and miracle drugs developed, but at the same
time, there is a need to ensure that humans are not cut
out of the loop. Organizations need to carefully plan
for the right level of oversight that gives an aperture of
control to humans—after all, big data should be working
for the benefit of humans, not the other way around.
Organizations that manage big data have an
obligation to monitor security device, server, and
application logs, all of which generate machine data
that provide insight into how, when, and why machines
are communicating with other machines. Monitoring the
activities of machines allows organizations to watch for
patterns and avoid runaway transactions or manipulation
that can lead to fraud and other unintended results.
Server logs also provide indications of who accessed
data and how these data were used, affording critical
oversight into potential illegal or unethical access and
use of data. Machine data are monitored by healthcare
organizations to show compliance with Health Insurance
Portability and Accountability Act (HIPAA) standards,
banks to prevent credit card fraud, and governments and
corporations to watch for and prevent data loss.
Today’s public, legislative, and legal sentiments
may not be tomorrow’s; these attitudes will continue to
diverge by government and region. Governments and
other organizations need to balance the Facebook Effect,
which entails the deliberate sharing of more and more
personal information, with the requirements of security
and what the marketplace can use for better customer
service and marketing. Organizations, both public and
private, need to proactively take steps to prevent privacy
intrusion whether the public demands such measures or
not. European governments provide an example with the
“right to be forgotten” for minors across the European
Union. Those steps may include obtaining approval,
either by asking permission or by gaining permission
in exchange for tangible benefits for the collection and
use of personal data—a common technique used by
customer loyalty programs. Organizations should also
consider the use of anonymization techniques to mask
personal identities where that is the appropriate path.
Organizations, both public and private, must
balance the risks and rewards of big data—especially as
big data moves from low impact “experiments” to driving
real-time operations and decision-making. Although
social acceptance of what data can and will be shared
is changing and evolving, its impact on privacy and
personal security and the introduction of the creepiness
factor are all things to consider. Big data is a fast-moving
technology space that will affect all aspects of our lives.
Transparency about what, how, and why data will be
used will become more important as organizations
seek to provide better services and products at both
the government and private levels. Taken together, the
trends and challenges will shape the path forward for
organizations that wish to be deliberate and wise about
their use of big data.
NOTES
1 Hackathorne 2004.
2 The website for the service is http://mercytelehealth.com/services/
safe-watch/.
3 Confidential client example.
4 Confidential client example.
REFERENCES
Hackathorne, R. 2004. “The BI Watch: Real-Time to Real-Value.”
DM Review, January (2004). Available at http://www.bolder.com/
pubs/DMR200401-Real-Time%20to%20Real-Value.pdf.
Mercy Services. Telehealth Services, Safe Watch. Available at
http://mercytelehealth.com/services/safe-watch/.

CHAPTER 1.6
Rebalancing
Socioeconomic Asymmetry
in a Data-Driven Economy
PETER HAYNES, Atlantic Council
M-H. CAROLYN NGUYEN, Microsoft
It is more than half a century since economist Fritz
Machlup, in his book The Production and Distribution of
Knowledge in the United States,1
developed the concept
of the knowledge economy, a term later popularized
by management theorist Peter Drucker.2
Both used
the phrase to differentiate between an economy based
largely on goods and services produced by manual labor
and an economy based on the production of new ideas,
information, and knowledge.
The wide availability of low-cost information and
communication technologies (ICTs)—which started
in the early 1980s but gained real traction in the mid-
1990s with the advent of the public Internet—drove the
economic transformation that Machlup and Drucker
envisioned. Research by the Organisation for Economic
Co-operation and Development (OECD) shows how
rapidly the combination of personal computers, digital
telecommunication devices, and the Internet impacted
economic growth in the early years of that troika. A 2004
OECD paper estimates that inflation-adjusted investment
in ICTs accounted for an average of 0.5 percentage
points of annual growth in real GDP in OECD countries
between 1995 and 2001.3
This represents about 20
percent of total growth in real GDP—a significant
impact. And a 2002 OECD study demonstrated
that corporate investment in networked computer
systems is consistently associated with increased labor
productivity—for example, productivity was found to be
11 percent higher in US manufacturing plants that have
computer networks.4
It took about 80 years for steam
engines to increase labor productivity by approximately
that amount, about 40 years for electricity, and more
than 20 years for pre-Internet ICTs.5
Yet it could be argued that what we saw in the years
from the early to mid-1990s was less the emergence of
a knowledge economy than an information economy.
Early Internet-era ICTs enabled more efficient and
effective processing and use of data, resulting in
information that was, for the most part, used to improve
the performance of existing processes, businesses,
and industries. Although in some cases that information
transformed businesses completely (e.g., Amazon), these
are still traditional businesses. To describe what was
created during this period as a knowledge economy
would be an exaggeration. Much information has been
generated from the raw data collection made possible
by advances in ICTs, and significant new knowledge has
been created, but neither is yet the foundation on which
the global economy is built. That honor still belongs to
physical goods and services.
The recent emergence of big data, along with what
is being called the “data-driven economy,” may finally
make possible a true knowledge economy—by which
we mean entirely new classes of economic activity
predicated on insights and value derived from analyzing,
contextualizing, and commingling vast datasets in ways
that were previously either unknown or impossible. For

companies (and entire economies) to keep growing,
the development of such an economy is an imperative:
battered by global competition, commoditization, and
shrinking product/service cycles, corporations seeking
to maintain or grow their profit margins will increasingly
rely on creating new (and hard-to-emulate) products and
services based on insights derived from the datasets
that they own or can gain access to, especially those
pertaining to their customers.
Two decades after the emergence of the consumer
Internet, the world is awash in data. By one estimate,
almost 3 zettabytes (3 billion terabytes) of information
had been created by 2012, a digital deluge that is
growing at around 50 percent a year.6
By the end of
2013, the number of wirelessly connected devices,
at an estimated 8 billion, will have exceeded the 7.2
billion people on the planet.7
By 2020, as many as 50
billion devices could be wirelessly connected to the
Internet,8
while the world population is forecast to be
fewer than 8 billion people.9
At the same time, from
2012 to 2017 machine-to-machine data traffic is set to
grow an estimated 24 times, to reach 6 × 1017
bytes
per month—an astonishing compound annual growth
rate of 89 percent.10
Indeed, the majority of big data will
be collected passively and automatically, via machine-
to-machine transactions, and users will not be actively
involved in the majority of those transactions.
Big data, analytics, and machine learning promise
new solutions to previously intractable problems (e.g.,
in healthcare, disaster response, the environment, and
transportation); new businesses will be able to create
innovative services by selecting, combining, and parsing
data in groundbreaking ways; and individuals will be
empowered because they will be able to draw on a
wide range of yet-to-be-invented data-based services
and tools to improve the quality of their lives. Big data,
then, truly does promise to create new knowledge—and
indeed new kinds of knowledge—on which an entirely
new economy can be founded.
However, the knowledge economy relies on the
availability of an adequate supply of data to enable
the discovery of new knowledge. This requires policy
frameworks that permit data—including personal data—to
be collected, analyzed, and exchanged freely, across
geopolitical boundaries, while minimizing risks and
harms to individuals and enterprises globally. Existing
regulatory approaches that are based on the principles
of notice and consent to restrict the collection of data
pre-designated as personal may overly restrict the supply
of data available, hampering the foundation for the new
economy. Furthermore, what is considered personal
and acceptable use are individual decisions, subject to
context, perceived value, and social and cultural norms—
all of which are in a constant state of flux.
In reality, it is not the collection of data that is the
source of potential harm, but its unconstrained use.
Moreover, in the world of big data, it would be impractical,
if not impossible, for individuals to give express consent
for all the data that may be generated about them.
Together, the above factors necessitate a change in
policy approach from a collection-based model toward a
use-based model, where individuals give permission for
the use of data related to them.
What is increasingly clear about an economy
based on the collection, use, and analytics of big data
is how little we actually know about it—its potential risks
and rewards, as well as its implications for individuals,
organizations, policy development, and growth. The rest
of this chapter will focus on some core challenges that
the authors believe could be particularly problematic—
and that may threaten to impede the development of a
promising 21st-century knowledge economy.
THE DEMISE OF FAIR VALUE EXCHANGE
All previous economic revolutions have been based
on the idea of an explicit (i.e., transparent) fair value
exchange. For example, in return for $850, early 20th-
century consumers could obtain a 1909 Ford Model
T; $1,565 bought a base-model 1981 IBM PC; and
today, a hardback edition of Adam Smith’s The Wealth
of Nations is available for a price of about $17. The
costs and benefits to those on both sides of this value
equation (usually an individual and a corporation) are
both clear and easily discoverable. The process by
which the transaction is executed is well established in
modern economics: rational, self-interested economic
actors determine the price they are willing to pay for a
good or service based on their subjective perception
of its utility—something that is usually quite simple for
them to determine. Assuming that the market in which
the economic actors are engaging is not subject to
monopolistic or other distortions, prices tend to settle at
the point where supply equals demand.
Research sponsored by Microsoft and published
last year by the International Institute of Communications
found that, among other things, users do consider
fair value exchange in allowing the use of their data.11
They have some expectation of what they will receive
in return—for example, discounts, better service, an
improved product, or potential benefits to the larger
community in which they live.
Retailer loyalty cards are an example of this
type of transaction, and they also illustrate one of the
challenges of the data-driven economy. Most consumers
understand that the discounts they receive via a loyalty
card are provided in exchange for data they supply to
the retailer. But very few realize that the primary value
to the retailer is the ability to track and analyze the
spending patterns of both individuals and aggregated
datasets of groups of consumers. In other words,
significant information asymmetries are embedded in
the transaction, and the average consumer lacks all the
information required to make a rational decision about
whether he or she should participate in it.
Chapter 1.6: Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy

As the global economy becomes increasingly
grounded in the exchange of data, the ways in which
those data are collected and analyzed will become
even more opaque to the consumer and the value
exchange even harder to discern; trust will decrease
correspondingly. An individual may have only a vague
idea of what data exist about him or her and what
is being done with these data. Some will have been
actively volunteered by the consumer; some will have
been obtained passively, with or without his or her
explicit knowledge; and yet more may have been inferred
by commingling a range of public and private, personal
and non-personal datasets in ways that might expose
new information or knowledge about each consumer’s
habits, lifestyle, health, or financial situation. Although
the individual may receive something in return for this
information, the real values of both the data provided
and the service returned (in other words, the underlying
exchange of value) may be almost impossible to
determine.
Today little agreement exists about how best to
value online data. The most comprehensive survey of
valuation methodologies was presented in a recent
OECD study (on which the authors of this chapter
consulted) that identified numerous ways in which
data might be valued in the market (refer to Box 1).12
However, each of these methods has significant flaws,
and none addresses the potential social and economic
benefits of personal data. For example, corporate
revenues per record/user are problematic because
revenues contribute to economic growth only insofar
as they generate added value (or surplus). Revenue of
$4 per record/user with near-zero profitability is very
different from $4 per record/user with 40 percent net
profit. Similarly, the vast amount of personal data on
Facebook have a relatively low per-person value because
the company, while making significant profits from the
sheer scale of its data holdings, has yet to find the Holy
Grail of social-media data monetization. Amazon, by
contrast, collects far less personal information from
individuals, but its business model is predicated on
advanced purchase analytics. Thus, on a per-user level,
its inferred personal data (which are at present mostly
outside the user’s control) are more valuable than
Facebook’s volunteered personal data (which the user
has painstakingly assembled, and over which she or he
has at least nominal control).
Distinguishing personally beneficial uses of data
from socially beneficial uses is a further challenge
because each creates separate and significant value.
For example, the personal value of using an electronic
health record is improved treatment for the patient—and
this undoubtedly has direct monetary value in the form of
reduced costs, better outcomes, and so on. But socially
beneficial uses also create (or could create) value—
for example, by facilitating research into new drugs,
identifying new epidemiological trends, or improving
medical protocols. However, because the value created
does not involve explicit market transactions, attributing
this benefit directly to data involves some inspired
approximation. And even though one estimate puts the
savings in this case at up to US$300 billion,13
most of
the ways in which data are valued today would consider
such benefits an externality to be ignored.
For many people, however, the various ways in
which data might be valued are largely irrelevant today,
because they have already given away their digital crown
jewels for free. Individuals are passing massive amounts
of personal and other data to large corporations with
little or no thought to its potential monetary value—and
those corporations are making significant profits as a
result, because their cost of materials is essentially zero.
The concept of fair value exchange no longer exists, at
least not in any conventional sense. Facebook users, for
example, provide it with data that have the potential to
generate immense long-term value for the company; in
return they receive a “free” service, but the transaction
is wholly asymmetrical. As the computer scientist Jaron
Lanier has observed, “[T]he dominant principle of the
new economy, the information economy, has lately been
to conceal the value of information…. We’ve decided
not to pay most people for performing the new roles
that are valuable in relation to the latest technologies.
Ordinary people ‘share,’ while elite network presences
generate unprecedented fortunes.” And if an individual’s
information is not valued in economic terms, Lanier
adds, “a massive disenfranchisement will take place.”14
Box 1: Potential approaches for estimating the
value of personal data
The following methods for valuing personal data have been
identified, but each has important drawbacks. Possible
approaches include:
• determining the market capitalizations of firms with
business models predicated on personal data;
• ascertaining the revenues or net income per data
record;
• establishing the market prices at which personal data
are offered or sold;
• establishing the economic cost of a data breach;
• determining prices for personal data in illegal markets;
• reviewing economic experiments and surveys that
attempt to establish the price companies would need
to pay for individuals to give up some of their personal
information; and
• ascertaining how much individuals would be willing to
pay to protect their data.
Source: OECD 2013.

In other words, under the current model, the greater
the role that data play in the global economy, the less
the majority of individuals will be worth. This could mean
that a data-driven economy may become a contracting
economy. Like Lanier, we believe that if a truly
sustainable data-driven economy is to be established,
the way in which data are traded between individuals
and corporations will require a major reset. For a data-
driven economy to thrive, individuals would have to
receive fair/appropriate monetary compensation for each
specific datum they provide, perhaps with additional
payments whenever that datum produces incremental
profits for the entity to which it has been given (a
concept popularized by Lanier). Such an arrangement
would be complex: a specific datum might gain value
only when commingled with other data, for example, and
any payment/micropayment system would have to be
capable of keeping track of such complexities (assuming
the individual has given permission for this to happen).
And a sustainable data-driven economy might also entail
individuals paying fees (likely modest) for services they
now consider (erroneously) to be “free.”
Such systems, or similar approaches that address
these concerns, will be essential to establish the
concept of fair value exchange in the world of big data.
The importance to our economic future—to the entire
concept of a data-driven economy—of undergoing
this evolution cannot be overstated. Without it, the
consumers who today are the engine of economic
growth will increasingly lose their ability to participate in
the economy. Without fair value exchange for data along
with inherent trust in the data ecosystem, everyone will
ultimately lose—consumers, corporations, and countries
alike. Establishing a system of fair value exchange will
require new thinking on how technology and policy can
work in parallel.
DESIGNING A TRUSTWORTHY—AND
ECONOMICALLY VIABLE—DATA ECOSYSTEM
We believe that an essential element of the foundation
that can enable user trust and fair value exchange
is an interoperable metadata-based architecture. In
such an architecture, data are logically accompanied
by a “metadata tag” that contains references to the
permissions and policies associated with the data,
along with related provenance information, specified
in an extensible and interoperable markup language.
The metadata is logically bound to the data and
cannot legally be unbound or modified for the entire
data lifecycle by any parties other than the user or as
specified by, for example, a related policy or rules of a
“trust framework.” More comprehensive consideration of
these issues can be found in Realizing the Full Potential
of Health Information Technology to Improve Healthcare
for Americans: The Path Forward and the Digital
Enlightenment Yearbook 2013.15
But the use of metadata does not stop at enabling
the enforcement of user permissions and related
policies. It can also be utilized to track and capture the
monetary value produced by personal data, over time,
in a decentralized data ecosystem—and consequently
provides a foundation for both trustworthy data and
fair value exchange. Consider: metadata enables
individuals to change their personal data preferences
and permissions over time, prevent undesirable use of
previously collected data, address unanticipated uses,
and adjust to changing norms. Thus, if we consider
personal data to be the product of an individual’s
online “labors,” and if we further consider that, in order
to introduce the concept of fair value exchange (and
sustainability) into a data-driven economy, those data
must be assigned monetary value, then metadata is the
mechanism that will enable individuals to “direct” their
labors and reap the related benefits for the duration of
its existence in the data ecosystem—enabling a more
enlightened society in the digital space. How such
an approach would incorporate machine-to-machine
generation and use of data remains unanswered,
however, and requires considerably more research.
Such an approach is technologically non-trivial. A
primary challenge is security: although metadata can be
logically bound to data, it can also be unbound by bad
actors (a situation similar to the vulnerability of today’s
financial systems to hackers). Thus a strong legal and
policy framework will be required to ensure that criminals
are discouraged from doing this (again analogous to
the laws governing today’s financial systems). Another
challenge lies in specifying the user permissions and
policies that would govern how data can be used
within—and shared across—trust boundaries, and how
those permissions and policies would be negotiated
among the multiple parties with claims on the data
or claims to its monetary value.16
Yet another, highly
significant challenge is developing the appropriate
interfaces that will enable individuals to specify their
permissions, either directly or through other means (such
as recommender systems or data intermediaries).
Achieving all this will require the specification of
an interoperable metadata-based architecture that can
function at Internet scale. The development of such
an architecture needs to be a collaboration between
multiple data stakeholders to ensure its feasibility
and inherent security, as well as its ability to enable
alternative policy frameworks.
A metadata-based architecture offers value to all
stakeholders in the data ecosystem, not only users. Data
controllers and processors can more easily understand
and comply with permissions and policies defined
for specific data. They can also establish a dynamic,
economically viable and sustainable “marketplace” in
data that would ideally mirror the way in which fair value
exchange is established in the physical world. Solution
providers can create applications and services that

produce new business value and track the associated
value chain, yet still use data in privacy-preserving
ways. Companies can develop metadata schemas that
fully describe data use, codes of conduct, and relevant
policies to meet industry and regulatory requirements.
And regulators can take advantage of greatly improved
auditability of data, along with a stronger and better-
defined connection between the data and those policies
that govern its use.
Although metadata can help facilitate a data-driven
economy, it cannot guarantee that entities handling the
data will honor the permissions and policies associated
with them. However, when implemented as part of
a principles-based policy framework that provides
guidance on trustworthy data practices—supplemented
by voluntary but enforceable codes of conduct and
underpinned by legal redress—this is a flexible approach
that holds the promise of satisfying the interests of
regulators, individuals, and industry. In addition, as
noted above, the authors believe that metadata could
also be a key to establishing a viable and sustainable
economic ecosystem in a data-driven economy, enabling
the monetary value generated by data to be tracked,
captured, and realized as payments to and from the
ecosystem’s participants.
CONCLUSION AND WAYS FORWARD
There are many challenges here, and today we have
more questions than answers. But what is clear is that,
in order to create a sustainable data-driven ecosystem,
technology and policy must work symbiotically. For
that to happen, governments and their regulatory
representatives need to partner closely with industry,
academic researchers, and consumer groups to gain a
better understanding of the issues and to jointly develop
innovative and evidence-based approaches to policy
frameworks that address the above needs. Similar to
evolution of new technologies, such approaches will
need to be iterative.
Indeed, our view is that governments are the only
entities with the ability to convene the broad societal
coalition that will be required if the promise of a data-
driven knowledge economy is to be fully realized. Such
dialogues will, of course, need to be conducted on a
global level.
NOTES
1 Machlup 1962.
2 Drucker 1969.
3 Ahmad et al. 2004.
4 Astrotic et al. 2002. Data are for value-added labor productivity.
5 Bughin and Manyika 2013.
6 Gens 2011.
7 Cisco 2013.
8 Ericsson 2011.
9 United Nations, Department of Economic and Social Affairs 2013.
10 Cisco 2013.
11 International Institute of Communications 2012.
12 OECD 2013.
13 Manyika et al. 2011.
14 Lanier 2013, p. 15.
15 See PCAST 2010 and Nguyen et al. 2013. When two or more
entities agree to abide by a common set of legal rules, codes
of conduct, other business and technical rules, and operational
rules, they are generally referred to as belonging to the same trust
framework.
16 In some respects, this is no more complicated than
the agreements that have existed for years among
telecommunications carriers—the scale and scope are different,
the principles similar.
REFERENCES
Ahmad, N., P. Schreyer, and A. Wölfl. 2004. “ICT Investment in OECD
Countries and its Economic Impacts.” In The Economic Impact
of ICT: Measurement, Evidence and Implications. Paris: OECD
Publishing. 61–83.
Astrotic, B. K., P. Boegh-Nielsen, K. Motohashi, and S. Nguyen. 2002.
IT, Productivity and Growth in Enterprises: New Results from
International Micro Data. Paper presented at the OECD Workshop
on ICT and Business Performance, OECD, Paris, December 9.
Available at http://www.rieti.go.jp/it/productivity/docs/DJUS_
OECD_.pdf.
Bughin, J. and J. Manyika. 2013. “Measuring the Full Impact of Digital
Capital.” McKinsey Quarterly, July. Available at http://www.
mckinsey.com/insights/high_tech_telecoms_internet/measuring_
the_full_impact_of_digital_capital.
Cisco. 2013. Cisco Visual Networking Index: Global Mobile Data Traffic
Forecast Update, 2012–2017, February 6. Cisco. Available at
http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/
ns537/ns705/ns827/white_paper_c11-520862.pdf.
Drucker, P. F. 1969. The Age of Discontinuity: Guidelines to Our
Changing Society. New York: Harper and Row.
Ericsson. 2011. More Than 50 Billion Connected Devices. Ericsson
White Paper 284 23-3149 Uen, February. Available at http://www.
ericsson.com/res/docs/whitepapers/wp-50-billions.pdf.
Gens, F. 2011. IDC Predictions 2012: Competing for 2020. IDC
#231720, Volume 1. Framingham, MA: IDC. Available at
http://cdn.idc.com/research/Predictions12/Main/downloads/
IDCTOP10Predictions2012.pdf.
International Institute of Communications. 2012. Personal Data
Management: The User’s Perspective. International Institute of
Communications. Available at http://www.iicom.org/open-access-
resources/doc_details/226-personal-data-management-the-users-
perspective.
Lanier, J. 2013. Who Owns the Future? New York: Simon & Schuster.
Machlup, F. 1962. The Production and Distribution of Knowledge in the
United States. Princeton, NJ: Princeton University Press.
Manyika, J. M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and
A. Hung Byers. 2011. Big Data: The Next Frontier for Innovation,
Competition and Productivity. McKinsey Global Institute Report,
May. Available at http://www.mckinsey.com/insights/business_
technology/big_data_the_next_frontier_for_innovation.
Nguyen, M.-H. C., P. Haynes, S. Maguire, and J. Friedberg. 2013.
“A User-Centred Approach to the Data Dilemma: Context,
Architecture, and Policy.” In Digital Enlightenment Yearbook 2013.
IOS Press Ebooks. 227–42. Available at http://ebooks.iospress.nl/
publication/35146.
2013. “Exploring the Economics of Personal Data: A Survey of
Methodologies for Measuring Monetary Value.” OECD Digital
Economy Papers, No. 220. OECD Publishing. Available at http://
www.oecd-ilibrary.org/science-and-technology/exploring-the-
economics-of-personal-data_5k486qtxldmq-en.

PCAST (President’s Council of Advisors on Science and Technology).
2010. Realizing the Full Potential of Health Information Technology
to Improve Healthcare for Americans: The Path Forward,
December. Available at http://www.whitehouse.gov/sites/default/
files/microsites/ostp/pcast-health-it-report.pdf.
United Nations, Department of Economic and Social Affairs. 2013.
World Population Prospects: The 2012 Revision. Available at http://
esa.un.org/wpp/.

CHAPTER 1.7
Building Trust: The Role of
Regulation in Unlocking the
Value of Big Data
SCOTT BEARDSLEY
LUIS ENRIQUEZ
FERRY GRIJPINK
SERGIO SANDOVAL
STEVEN SPITTAELS
MALIN STRANDELL-JANSSON
McKinsey & Company
“Data is a precious thing. . .” and “…that’s why I’ve
called data the new oil. Because it’s a fuel for innovation,
powering and energizing our economy.”1
These were the
words of Neelie Kroes, Vice-President of the European
Commission responsible for the Digital Agenda, when
speaking about the value of big data earlier in 2013. As
Kroes noted, data comprise a fuel we have only just
begun to tap.
This “new oil” is certainly plentiful. Trillions of bytes
of data are generated by companies that capture
information about their customers, suppliers, and
operations. Networked sensors and software embedded
in devices and appliances are further energy generators,
as are the growing volumes of media content. These
sources of data do not even include the billions of
individuals around the world generating the same fuel
on their smartphones, personal computers, and laptops.
And the volumes of data are exploding. McKinsey
recently estimated that the data collected globally will
grow from some 2,700 exabytes in 2012 to 40,000
exabytes by 2020.2
To put this into context, a single
exabyte of data equals a hundred thousand times all the
printed material of the Library of Congress.
Definitions of big data vary greatly. Rather than put
a number on what qualifies as “big,” McKinsey defines
it as datasets so large that typical database software
tools are unable to capture, store, manage, and analyze
them. Such a definition allows for the fact that the size
of datasets regarded as “big” will also grow with the
advance of technology.3
Whatever the precise definition, big data is widely
acknowledged to create value in four ways. It creates
greater transparency by making more and better
information available more quickly. It helps organizations
create highly specific segmentations, enabling them
to tailor products and services more precisely. It helps
improve decision-making by providing better tools for
analysis. And it supports innovation in the form of new
products and services.
Big data can create significant value for the whole
economy. McKinsey research shows that companies
that use big data can deliver productivity and profit gains
that are 5 to 6 percent higher than those of competitors.
The private sector is not the only beneficiary,
however. Big data can also enhance productivity and
effectiveness of the public sector and create economic
surplus for consumers. For example, the McKinsey
Global Institute estimates that US healthcare expenditure
could be reduced by 8 percent by using big data to drive
efficiency and quality.
No wonder, then, that governments and political
institutions are promoting big data on their agendas
and adopting initiatives such as the European Union’s
open data directive, which aims to give both citizens and
member governments access to a raft of government
Please note: The views expressed in this article are those of the authors
and do not necessarily represent those of McKinsey and Company.

1.7: Building Trust: The Role of Regulation in Unlocking the Value of Big Data
data. Governments understand that big data’s economic
and social potential can grow only alongside continued
innovation in the underlying technologies, platforms, and
analytic capabilities for handling data, as well as the
evolution of behavior among its users. Recent McKinsey
research shows that enabling “open data” or “liquid
data” across seven domains—education, transportation,
consumer products, electricity, oil and gas, healthcare,
and consumer finance—can generate more than US$3
trillion in additional value a year.4
There is no guarantee, however, that this potential
will be fully realized. Several obstacles lie in the way. The
uptake of big data will depend on the adoption of next-
generation telecommunications infrastructure, which is
still in its early development in many parts of the world.
Another prerequisite is a large enough pool of talent with
the advanced analytical skills needed to put the data to
good use. This workforce will need to be trained. Equally,
big data uptake will hinge on whether ways can be
found to protect information technology infrastructures
and the data they carry from cyberattacks. A further
imperative is to build the trust of citizens, who are
growing increasingly suspicious about how information
about them is being used.
Regulation plays a role in tackling all these
obstacles. This chapter focuses only on the need to
build trust. It examines the various broad types of
regulatory frameworks that are emerging to protect
privacy. Furthermore, it identifies the key issues that
regulators will need to consider as their policies evolve if
their aim is to foster trust while not stifling the enormous
potential of big data, and it outlines some actions
companies can take themselves to promote consumer
trust.
CONSUMER TRUST AS AN ENABLER OF BIG DATA
Research reveals that consumers are increasingly
concerned about how their personal data are used
(Figure 1), although the level of concern varies according
to the type of data being considered. Consumers care
more about their financial transactions and health-
related information than about their online habits, for
example. The recent revelations by Edward Snowden
disclosing US government data collection practices and
the extraction of data from a number of large Internet
companies have further raised public awareness about
privacy issues and data protection in the online world.
If big data is to deliver on its promise, companies
will need both to create customer trust in big data
applications and their use and to help customers feel
safe about the protection of their personal data and
privacy. Governments and regulators will need to frame
data protection policies that safeguard the privacy of
both customers and citizens. At the same time, these
policies must not stifle the innovation that big data can
deliver, or its attendant economic and social benefits.
DATA PROTECTION ARCHETYPES ACROSS THE
WORLD
The protection of personal data has long been viewed
as a fundamental right, enabling individuals to be in
control of data about their own person and preventing
unnecessary listings and discriminatory behavior.
Individuals can exercise this control by explicitly giving
or withholding consent before their personal data are
Figure 1: Consumers’ privacy protection concerns
Sources: USC Dornslife/Los Angeles Times 2012; European Commission 2011.
* These data are taken from the Special Eurobarometer poll published in 2011. Respondents were asked to select 4 out of 12 possible responses to the question of what should happen to
companies that breach protection rules. We present the top 3 responses here.
74% of Europeans think that disclosing personal data is
increasingly part of modern life
78% of US citizens think that companies collecting personal
information online are invading consumers’ privacy
72% of Internet users are worried about giving away too
much personal data
88% of Europeans believe that their data would be better
protected in large companies that are obliged to name
a data protection officer
Companies that breach
protection rules should be*
51% fined
40% banned from using
such data in the future
39% compelled to
compensate the victims

used. They have a right to be informed if those data
are to be used, and for what purpose. Companies
and organizations using their data are also required
to protect it from unauthorized use. There are strict
measures in place to protect medical data and credit
information.
But the issue has become more complicated in
the Internet era. Some argue that this right should be
safeguarded more strongly than ever when so many
companies and organizations are seeking access to
personal data and can gain that access more easily. On
the other hand, as we have seen, economic, social, and
personal benefits can arise from sharing data, and many
consumers are perfectly happy to give up some of their
privacy in return for certain goods or services.
Data protection laws are evolving not only in an
attempt to keep pace with technological developments
and new ways of using, collecting, and sharing
personal data, but also to keep pace with attitudes
toward privacy. To better understand the state of play,
McKinsey has conducted extensive research into the
data protection regulatory frameworks of more than 20
countries worldwide, identifying the key principles and
requirements (Figure 2).
From our research we have identified three main
archetypes of the level of regulation imposed around
the world: from the least to the most extensive, these
are regulations with a light touch, those with a minimum
standard, and those with strict ex-ante requirements.
• Light touch/self-regulation. This is the approach
used in the United States, where there is no general
federal data protection law. Instead, different
sectors—such as healthcare, telecommunications,
and finance—are regulated by specific laws applying
only to these sectors. These laws are enforced by
sector-specific authorities. Separate states can also
stipulate their own general regulations. Generally
the onus is on industries and the companies
within them to build trust with their customers,
either by issuing and following codes of conduct
or via contractual arrangements. Companies are
responsible for the privacy statements issued to
their customers and can face judicial sanctions for
non-compliance. Facebook and Google are two
recent cases in point.5
• Minimum standard setting. In Asia, the Asia-
Pacific Economic Cooperation (APEC, a forum of
21 Asia-Pacific economies) has developed a self-
regulatory framework setting out the principles that
economies should implement and companies then
follow to ensure a common, minimum level of data
protection across member economies. The aim is to
enable the easier transfer of data among economies
where the level of data protection regulation varies
greatly. Although some Asian economies (such
as Pakistan) still lack data protection laws entirely
or have recently introduced them (e.g., China and
India), others—such as Japan—have well-developed
laws. Examples of minimum-level principles are the
requirement that individuals (where appropriate)
should be able to exercise choice about the
collection, use, or disclosure of their data, and that
the data collected should be accurate, complete,
and up to date.6
• Strict ex-ante requirements. Ex-ante requirements
apply in Europe, where both the Council of Europe
and the EU Commission have developed extensive
frameworks to protect data and privacy in their
respective member countries.7
These frameworks
not only define what is regarded as personal data
and how such data can and cannot be used, but
they also set organizational and technological
requirements. Companies should, for example,
implement technological and organizational
measures to protect the data gathered. Furthermore,
strict liabilities are in place relating to both
companies and cooperation frameworks for
regulators. The frameworks stipulate that data
from the European Union may be transferred
only to countries that have an appropriate level of
protection.8
All three regulatory archetypes are constantly
evolving. One example of this evolution is that the
European Union is currently updating the existing
data protection directive from 1995 to better meet the
requirements of today’s data-intensive world.9
In the
United States, the Federal Trade Commission (FTC) has
increased its focus on data protection issues and has
published several reports and recommendations on the
topic in the past few years. It has also taken on a stricter
role regarding the enforcement of companies’ own
privacy statements.10
The APEC framework was set up
in 2004 and has evolved over the past 10 years.
Opinions on the best approach to data protection
and privacy regulation differ. Some experts argue
that it is better to adopt a light-touch approach in a
technologically dynamic world because detailed, specific
regulation could quickly become obsolete and even
hinder technological and business development. Others
argue that increasingly powerful technology makes
a stricter regulatory approach necessary to protect
privacy. Whatever approach is taken, we believe data
protection and privacy regulation is becoming more and
more important across the world, and countries and
companies need to embrace it to create competitive
advantages for them in the future.
KEY REGULATORY AREAS FOR BIG DATA UPTAKE
Whatever approach any single government or regulator
chooses to adopt, all will need to pay particular
attention to key areas that require further clarification to
support the kind of innovation and prosperity that big

Figure 2: Variation in data protection regulation across markets
Sources: Council of Europe 2013a, b; European Commission 1995, 2002, 2012; IAPP 2013a, b.
* The convention was initiated and signed by Member States of the Council of Europe in 1981. See Council of Europe, Convention for the Protection of Individuals with regard to Automatic
Processing of Personal Data (ETS No. 108), available at http://conventions.coe.int/Treaty/en/Treaties/Html/108.htm.
Regulation maturity
n High
n Mid
n Low
UNITED STATES SOUTH AMERICA EUROPEAN UNION
No federal law exists to date
Some state- and sector-specific laws have
stricter regulations
Case-by-case enforcement of privacy
statements
Tradition of “habeas data”—the right to
find out if personal data is processed and
the right to file a complaint if it is misused
After amendments, regulation in Argentina
and Uruguay is at the EU level of strictness
No specific data protection law exists in
Brazil, but the country has constitutional
protection and sector-specific data
protection legal provisions
Existing regulation is already the strictest
globally
Regulations cover all industry sectors
Regulation requires “adequate
protection”—that is, the same level
of protection for transmission to third
countries
A safe harbor agreement with the United
States enables free data transfer between
compliant companies in the two regions
RUSSIA AND CENTRAL ASIA MIDDLE EAST AND AFRICA ASIA PACIFIC
Data Protection Acts exist in some countries
(Azerbaijan, Georgia, Russia, Ukraine)
Enforcement is low (relevant mechanisms
and authorities are not always in place)
A few countries (Morocco, Tunisia, the
United Arab Emirates) already have data
protection laws
Morocco signed the Council of Europe
data protection convention in 2013,*
establishing a general data protection
regime
Most countries are unregulated or have
single points in sector laws (e.g., Algeria,
Egypt)
The level of protection ranges from strong
protection in Japan and the Republic of
Korea to weaker protection in Bangladesh,
China, Pakistan, Indonesia, for example
Recent awareness of data protection
issues has resulted in several new laws in
economies such as India, Hong Kong SAR,
the Republic of Korea, and Singapore, as
well as other efforts, such as the APEC
Cross-Border Privacy Rules System,
enhancing protection

data can drive, while maintaining customer trust and
data protection. These areas include: consent before
collection, a definition of personal data, anonymization,
the right to be forgotten, relevant jurisdiction, and liability
issues. Each of these key areas is discussed below.
Consent before data collection. A key principle
in the European regulatory framework is the need to
obtain personal consent before data are gathered.
Anyone wanting to use an individual’s data must
first seek his or her permission. But with so much
information now available and being gathered, seeking
that approval can be a slow, tedious process for
companies and consumers alike and can hinder big
data development. Cookies on the Internet are a simple
example. Surfing the web would be more convenient
without cookie notifications and approvals. The APEC
framework recognizes this, and the framework states
that “where appropriate, individuals should be provided
with . . . mechanisms to exercise choice in relation
to the collection, use and disclosure of their personal
information.”11
However, determining where such choice
is appropriate is open to interpretation.
The definition of personal data. The suggested
EU framework defines personal data as “any data that
can be attributed to an identifiable person either directly
or indirectly.” The APEC framework describes personal
data as “information about an identified or identifiable
individual.” Both these definitions mean that not only
data clearly identifying a person with information such
as a name or address is considered to be personal
data, but also data that can be attributed to a person
indirectly through some other measure, such as via
a mobile phone number or an identity code. In a big
data world where a lot of data are interlinked, it can be
difficult to know exactly when data become “personal.”
Is it only data that identify a person with certainty, or
does it also include data that identify someone with
high probability? How about a person’s actions?
Performance? Or buying behavior? To give a concrete
example, a US retail chain identified new parents as a
very lucrative market segment. The chain analyzed their
customers via characteristics such as their shopping
habits, age, or marital status to spot customers who
were pregnant. They then sent those customers direct
marketing material for their baby products ahead of their
competitors, who sent their material only after the child’s
birth.12
However, information on pregnancies is extremely
sensitive, and such material could risk disclosing a
pregnancy that has not yet been announced. This could
clearly be seen by some as an intrusion of privacy, but
the issue is not entirely clear from a legal perspective.
Anonymization. Closely linked to the dilemma
of how to define which data are personal is the issue
of data anonymization or sanitization. Traditionally,
anonymous data have not been subject to data
protection laws. However, in a big data world where
anonymized data can easily be linked up, it is not very
hard to build a profile of a person without traditional
means of identification such as a name or address.
For example, a team at Harvard was able to identify
individuals from anonymized data in a genetics database
by cross-referencing it with other public databases.
The accuracy rate was 42 percent based on the use
of only three types of information—zip code, date of
birth, and gender—and rose to 97 percent when the
first name or nickname was added.13
Another example
is the use of de-anonymization tools by researchers
from Texas University on 500,000 Netflix users who
had anonymously voted for their preferred movies back
in 2007. In this case, the researchers also managed to
identify users by linking the anonymized ratings with
another public database with movie ratings.14
It can
therefore be argued that the use of anonymous data can
potentially constitute an intrusion of privacy.
Another question related to data anonymization is
the right of companies to use the personal data already
in their possession and turn them into anonymized data
that they sell to others. Some companies are selling their
customer data—such as location and application data
of telecommunications companies—to other companies
in anonymized and aggregated form for marketing
purposes. Companies can target their marketing more
effectively by using these data to learn about their
customers. Internet companies are also matching their
customer data and online habits with data from other
companies to better target their online advertising.15
Several questions arise from a privacy perspective.
When can data be considered anonymized? Does using
a pseudonym make data anonymous? Are companies
allowed to use anonymized data without the customer’s
consent, or must customers give their prior approval?
Should that consent be granted before use, or is it
enough to allow customers to opt out?
The right to be forgotten. The new EU data
protection framework proposes introducing a right
for users to request that data controllers remove their
personal data from their files. Although on paper it
sounds easy to remove personal data relating to an
individual upon request, this may not be so easy
in the real world. The European Union Agency for
Network and Information Security (ENISA) states that
a great deal of data are stored in different places in
the cloud for security reasons, and these data may
have been aggregated or amended into new forms,
such as statistical data. Thus removing some specific
data from all systems upon request may be entwined
with the aggregated data. Clearly this is not such a
straightforward task in a virtual environment, and there is
no single technical method to enable this easily.16
Relevant jurisdiction. Data are increasingly used
and stored across borders, but regulation is still largely
national in its scope and regulators lack jurisdiction
in markets outside their own. The uncertainty about
jurisdictions creates problems for companies and

consumers alike. Which regulations apply to companies
from another country? Which judicial authority has the
right to intervene in disputes? What happens in cases
where a company breaches laws across many markets?
In its recent proposal on the new EU data protection
regulation, the European Union extends the applicability
of its regulation to companies outside the European
Union that are handling data relating to European Union–
based individuals.
Liability issues. In today’s world, companies
often cooperate to produce big data applications and
solutions. One company orders software from another,
which in turn uses a third company as a contractor,
which stores its data within a cloud service operated by
yet another. If data are leaked, it can be very difficult to
decide which company is liable.
The above remaining gray areas must be considered
and clarified so that both consumers and companies
using big data clearly know what the rules are in order
to ensure a certain environment that is conducive to
investment and market growth. In the next sections we
propose several options for regulators and companies to
make the big data environment more certain.
IMPLICATIONS FOR REGULATORS AND
POLICYMAKERS
Regulators will need to address all the above issues
when shaping their personal data protection policies.
Although not prescribing any single solution, certain
principles will help guide regulators in their deliberations
and ensure the necessary regulatory balance. These
principles include the need to establish regulatory
stability, cooperation with members of industries
and different countries, and promoting industry self-
regulation. Each of these principles is discussed below.
Regulation in any field always works best if it
creates a stable environment in which companies and
other organizations can operate. When it comes to data
protection, companies and other organizations will need
regulatory certainty if innovation is to be encouraged.
Providing that stability is likely to be easier if regulators
focus not on specific regulations that may quickly
become obsolete, but instead on establishing non-
discriminatory technology-neutral high-level regulatory
principles that last.
Regulators should cooperate with companies and
other stakeholders within the industry when revisiting
their regulatory frameworks. This will help to understand
the business issues and allow them to be at the
forefront of developments without hampering industry
development.
Regulators should also cooperate internationally
to establish common international norms and clarity
around applicable legislation. International discussions
are already taking place on specific issues. Regulators in
the European Union and the United States have a safe
harbor framework, for example, that allows US-based
companies to transfer data between the two regions
without further approval from EU-based regulators.
These safe harbor provisions are currently being revisited.
US and Asian regulators are cooperating around the
APEC framework; the United States is the first non-
APEC market to sign the minimum standard framework.
An even a wider take on data protection issues in the big
data environment would be beneficial for all parties.
Whatever their approach to regulation, governments
should promote industry self-regulation. Self-regulation
is the best way to achieve a commonly accepted code
of conduct for a specific industry. This has already
been done in specific areas—for example, the use of
personal data in mobile marketing—but so far efforts
have occurred mainly at the country level, in markets
such as the United States and the United Kingdom. An
international industry standard specifically concerning
the use of personal data protection in big data would
certainly be beneficial to establish a higher level of trust
among consumers and create a clear data protection
standard for companies. The weakness of industry
self-regulation is obviously enforcement, because self-
regulation is not normally legally binding.
By efficiently managing all stakeholders, regulators
can establish a transparent legal framework that helps
promote industry growth rather than hindering it with
unnecessary legal burdens.
IMPLICATIONS FOR COMPANIES
The onus is not just on regulators to build an
environment of trust where citizens feel their privacy
will be properly protected. Companies also have a key
role to play. If they develop an efficient data protection
strategy, companies may also gain competitive
advantage in the form of cost savings, organizational
efficiency, and—importantly—reputational advantage. To
maximize the benefits of big data and to build trust, a
number of actions could be considered.
The first action a company should take is to
assess its regulatory and operational starting point.
Understanding customer concerns and regulatory
issues early will help companies determine the areas
of risk they need to start tackling. It will also outline
the company’s strengths and determine the best way
to leverage those strengths to develop their big data
strategy. For example, a company may wish to build
on its reputation as a reliable company that safeguards
customers’ personal data or position itself as an
innovative company with cool services based on its
users’ behavior and habits or preferences.
A company should also build a privacy-by-design
mentality. It goes without saying that companies will
need to comply with relevant regulations. But gaining
consumers’ trust is a question of mentality, too. Many
companies may find they need to implement changes
across the organization as well as in relevant processes
and technology applications to protect consumer privacy.

Companies should strive to make data protection part of
the company culture. They can avoid costs occurring at
a later stage (when compliance measures are needed)
by implementing data protection in their processes from
the start.
Companies must also cooperate with regulatory
authorities. Privacy and data protection regulation is
constantly evolving. This means that companies will
need to establish a close relationship with national
regulators to ensure compliance and to make certain
that the regulators and policymakers understand the
business issues at hand and the benefits of big data for
society.
Furthermore, companies need to cooperate with
other industry participants. Cooperating to develop
industry-specific norms and standards will help to
create an industry norm that enables consumers to have
greater trust.
Importantly, companies also should empower
customers. Customers’ concerns about privacy are
often alleviated if they are able to make their own
decisions about what data they do or do not share.
Providing transparent privacy policies or simply informing
the customer of the scope of data handling as well as
requesting clear consent declarations from customers
also helps create customer trust without sacrificing big
data business opportunities. Technological tools help, as
they can allow customers to adjust their privacy settings
and choose whether to opt in or out of services. One
example of this is British Telecom’s cookie settings,
which allow the customer to set the level of cookies
allowed and choose the level of privacy they are ready to
sacrifice for better services or service quality.
Companies have a key role to play in creating
consumer trust. Success in this area is not only about
managing regulators and compliance, but also about
creating a reputation as trustworthy and reliable in terms
of both secure operations and fair commercial practices.
As mentioned in the beginning of this chapter, customers
are usually willing to share personal data if the value of
the service is attractive enough and the customers feel
they get more in return than they give up.
CONCLUSION
Big data offers a wide range of opportunities—not just
for individual companies, but also for nations and society
as a whole. Both regulators and companies have large
roles to play to ensure positive development in this
emerging market with such great potential.
Regulators and policymakers should respond swiftly
to regulatory and policy concerns regarding big data
development. They must enable fast network build-out.
They must also ensure the education and training of a
qualified workforce and safeguard Internet safety. And
they must address consumer disquiet about privacy and
the protection of personal data—an area where several
issues are unclear and require further consideration and
clarification, ideally in cooperation with players across
the industry value chain and at an international level.
Above, a number of suggestions about how
companies might respond to these concerns were
outlined. Initially companies should conduct an
assessment of their regulatory and operational status
quo to identify risks and opportunities. They should
consider implementing a privacy-by-design mentality
to avoid unnecessary costs while ensuring compliance.
Companies should also consider cooperating both with
regulators and others within their industry to create
trust of their specific sector. Key for gaining customer
trust will, however, be the empowerment of customers
by clearly communicating their privacy policies to them,
giving them options for their privacy settings, and
requesting consent declarations. Companies need to
ensure that their customers understand what choice
means in terms of service performance and make sure
their services are providing more value to the customer
than the loss of privacy is worth.
It is only by addressing customer concerns at
different levels within the industry that the big data
industry can eventually evolve to its full potential.
NOTES
1 Kroes 2013a, b.
2 Lund et al. 2013.
5 FTC 2010–14; see www.ftc.gov/opa/reporter/privacy/
privacypromises.shtml.
6 APEC 2005.
7 Council of Europe 1981; European Commission 1995, 2002.
10 FTC 2010-2014; see www.ftc.gov/opa/reporter/privacy/
11 APEC 2005.
12 Duhigg 2012.
13 Sweeney et al. 2013.
14 Narayanan and Shmatikov 2008.
15 Steel 2012.
16 ENISA 2011.
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at http://europa.eu/rapid/press-release_SPEECH-13-450_en.htm.
Lund, S., J. Manyika, S. Nyquist, L. Mendonca, and S. Ramaswamy.
2013. “Game Changers: Five Opportunities for US Growth and
Renewal.” McKinsey Global Institute Report, July. Available at
http://www.mckinsey.com/insights/americas/us_game_changers.
Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh,
and A. Hung Byers. 2011. “Big Data: The Next Frontier for
Innovation,Competition and Productivity.” McKinsey Global
Institute Report, May. Available at http://www.mckinsey.com/
insights/business_technology/big_data_the_next_frontier_for_
innovation.
Manyika, J., M. Chui, D. Farrell, S. Van Kuiken, P. Groves, and E. Almasi
Doshi. 2013. “Open Data: Unlocking Innovation and Performance
with Liquid Information.” McKinsey Global Institute, McKinsey
Center for Government, and McKinsey Business Technology
Office Report, October. Available at http://www.mckinsey.com/
insights/business_technology/open_data_unlocking_innovation_
and_performance_with_liquid_information.
Naranyanan, A. and V. Shmatikov. 2008. Robust De-anonymization of
Large Datasets, February 5. The University of Texas at Austin.
Available at http://arxiv.org/PS_cache/cs/pdf/0610/0610105v2.pdf.
Steel, E. 2012.“Datalogix Leads Path in Online Tracking.” The
Financial Times, September 23. Available at http://www.
ft.com/intl/cms/s/2/8b9faecc-0584-11e2-9ebd-00144feabdc0.
html#axzz2idgoMkIT.
Sweeney, L., A. Abu, and J. Winn. 2013. Identifying Participants in the
Personal Genome Project by Name. Harvard University. Data
Privacy Lab. White Paper 1021-1, April 24. Available at http://
dataprivacylab.org/projects/pgp/.
USC Dornsife/Los Angeles Times. 2012. “Voters Across the Political
Spectrum Concerned About Tech Companies Invading Their
Privacy.” Press Release, March 21. Available at http://dornsife.usc.
edu/usc-lat-poll-privacy-march-2012/.

CHAPTER 1.8
From Big Data to Big
Social and Economic
Opportunities: Which
Policies Will Lead to
Leveraging Data-Driven
Innovation’s Potential?
PEDRO LESS ANDRADE
JESS HEMERLY
GABRIEL RECALDE
PATRICK RYAN
Public Policy Division, Google, Inc.
Over the last few years, myriad examples of innovation
in data analysis have emerged, creating new business
models for data-driven innovation. For example,
businesses are developing ways for real-time weather
information to be communicated to devices in the field
that can advise farmers on pest activity, water supply,
and inclement weather.1
The Royal Netherlands
Meteorological Institute has found a way to generate
extremely accurate rainfall information using nothing
more than existing data from cell-tower installations.2
The next phase of the Internet’s evolution has us on a
clear path toward a “revolution of data.”3
Every year,
the costs associated with the production, collection,
storage, and dissemination of data come down,
making those data more readily available. This process
is fomented by the increasing migration of many social
and economic activities to the web.4
More data are
generated today than ever before; this is a positive
trend that will inevitably continue: 90 percent of the
world’s information generated through the history of
mankind has been generated over the last two years,5
while data generated per year is growing at a rate of
40 percent.6
In this chapter we will focus on the social and
economic value of data, but from the point of view of
use and purpose rather than volume. We will therefore
talk about data driven-innovation instead of “big data,”
and will provide case studies from different areas, with
a special consideration of how data-driven innovation
in the public sector could improve policymaking. We
will finish the chapter by describing the main issues
that should be addressed by policymakers, who can
leverage the potential of data-driven innovation in their
communities through forward looking policies.
WHY SPEAK OF DATA-DRIVEN INNOVATION
INSTEAD OF BIG DATA?
It has become axiomatic that more data are produced
every year, and somehow this phenomenon has
driven commentators to call this revolution “the age
of big data.” However, what is commonly known as
big data is not a new concept, as the use of data to
build successful products and services, optimize
business processes, or make more efficient data-based
decisions already has an established history. Innovative
uses of data have been key to developing new products
and making more efficient decisions for quite a long
time, and these activities have become more common
and more efficient with the availability of modern
computing. Crunching data, statistics, and trends in new
ways has always helped change the way that entire
sectors operate. Agriculture is one of the first major
sectors to have benefitted from the aggregation and
analysis of data: in 1793, the Farmer’s Almanac found a
The opinions here are the views of the authors and do not represent the
views and positions of their employer.

niche when it published its first report more to provide
landowners with guidance for what they might expect
in the coming year and to plan their crops accordingly.7
It took 183 years for paper-based, manpower-intensive
analysis to become automated, but in 1950, a team
of meteorologists used the Electronic Numerical
Integrator and Computer (ENIAC) computer to make
the first successful numerical weather prediction.8
Moreover, the term big data is ambiguous, and it
sets up data as a negative because of the implication
that “big” is “bad.” Indeed, many common definitions of
big data usually focus not on size but instead on various
characteristics, including the frequency of production,
speed, volume, variety, and capacities needed to
manage and process information.9
McKinsey, for
example, describes big data as “datasets whose size is
beyond the ability of typical database tools to capture,
store, manage and analyze.”10
The implications of this
definition are that the main features of big data (quantity,
speed, variety) are technical properties that depend
not on the data itself, but instead on the evolution of
computing, storage, and processing technologies.11
What may look like big data today will not likely be as
“big” in the near future.
Thus, what is important about data is not their
volume, but how they may contribute to innovation
and therefore be used to create value. Data alone do
not possess inherent value; instead it is the processing
of data in innovative ways that brings new economic
and social benefits, and this value creates a virtuous
circle to feed into more use of data-based decision-
making and analysis.12
In other words, it is the use of
data that really matters.13
One way to measure this
value is to measure the socioeconomic metrics (or
to estimate the future potential) obtained from the
use of data. The excitement that we are seeing with
new deployments of data to fuel innovation is not just
because of the volume of data, nor is it about the
data themselves. As pointed out by the Software and
Information Industry Association, “transformative data
can be big or small or even the ‘needle’ of data found
in a giant haystack.”14
The truth is that data are data, and that has not
changed for centuries. When “big data” is no longer a
trendy concept, data will continue to drive innovation,
and solutions for new problems will come from new
ways of analyzing and interpreting data, regardless of
volume or our technological capacities to manage it.
In the next section, we will address what we see in the
future for data-driven innovation.
THE BENEFITS OF DATA-DRIVEN INNOVATION
Many sectors benefit from data-driven innovation:
healthcare (e.g., diagnosis and treatment), financial
services (e.g., analyzing market trends and economic
conditions), and transportation and public administration
(e.g., metrics on what citizens want and where
economic development is headed), to name a few. In
one example, a philanthropic research center stores
and analyzes the cancer genome and the sequences
and mutations of more than 10,000 cancer cases to
understand the complexity of the disease.15
In another
recent project, a university-based group of academics
mined data from 60 years of historical weather
records to identify the factors that are most predictive
of hurricane activity.16
In the private sector, PayPal
has developed a system that screens payments and
combines them with IP addresses, browser information,
and other technical data to identify and prevent
fraudulent activity in online payments, bolstering trust
for commercial exchanges on the Internet.17
A startup
firm has developed a no-cost platform for users that
helps travelers predict flight delays using an algorithm
that scours data on every domestic flight for the past 10
years and matches it to real-time conditions.18
Finally,
the United Nations is working with governments around
the world to understand global trends related to hunger,
poverty, disease, and job loss.19
However, because data-driven innovation takes
place across various sectors of the economy and
society, it is sometimes difficult to quantify its full
economic impact. For example, using any traditional
measure of Gross Domestic Product (GDP), Wikipedia
does not contribute any economic value.20
This makes
no sense in today’s world, as economists are now
demonstrating that a fundamental problem exists in
our ability to quantify the value of data, and this gap
misleads policymakers in their drive to maximize
economic surplus.21
As Michael Mandel has observed,
“economists have been systematically trained to think
of the economy as divided into two big categories:
‘goods’ and ‘services.’”22
Data are neither a good nor
a service and so they escape traditional economic
analysis. This highlights the complication of discussing
data: although the value often creates an economic
reward, such measurements are not easy to make.
The Internet itself has been a strong contributor to
economic growth for more than two decades, but
only in the past couple of years have economists
undertaken serious attempts to quantify the Internet’s
impact on the world’s economies.23
One example of innovative data use that has a
difficult-to-quantify economic value proposition is
Google’s Flu Trends, which provides near real-time
estimates of flu activity for a number of countries around
the world. Flu Trends provides its analysis based on
aggregated search queries.24
Some of these estimates
have been compared with official historic influenza data
from relevant countries with surprisingly high levels
of accuracy, and in some cases Flu Trends provided
information weeks ahead of official records. In the case
of H1N1, the world’s citizens were searching online long
before official statistics were available.25
Additionally, the
data from Flu Trends are open, available for everybody
1.8: From Big Data to Big Social and Economic Opportunities

to download and use. A group of researchers from the
Johns Hopkins University, for example, used these data
to develop a practical influenza forecast model designed
to provide medical centers with advance warning of the
expected number of flu cases, thus allowing sufficient
time to implement interventions before outbreaks.26
This
example illustrates how the openness and accessibility of
data are crucial to keeping the wheel of innovation rolling
by allowing others to access and manipulate the data in
transformative ways.
Similarly, the rapid collection and processing of
information has helped in recent natural disasters. After
a devastating earthquake hit Haiti in 2010, a group of
researchers from the Karolinska Institute and Columbia
University analyzed calling data of over 2 million mobile
phones to detect the pattern of population movements
across the country. This information was then handed
to humanitarian agencies to allocate relief resources in a
more efficient way.27
The findings of the project, called
Flow Minder, suggest that population movements during
disasters may be more predictable than had previously
been understood.28
These examples show that there are ethical
and responsible ways of analyzing big sets of data
and equally ethical and responsible ways of using
them to provide high-value solutions for citizens, whether
or not they have a clear quantifiable economic value
at the outset. More efficient preparation for outbreaks
and better understanding of post-disaster movement
ultimately mean more cost-effective deployment of
public services.
High-value products and services and more efficient
deployment of resources are not the only outcomes of
data-driven innovation. Studies suggest that there is a
direct connection between data-driven decision-making
in business and improved firm performance. Firms that
adopt data-driven decision-making have an output and
productivity that is 5 percent to 6 percent higher than
would be expected, given their other investments and
their information technology (IT) usage.29
Another study
has shown that the use of Internet computing tools can
also help firms reach decisions more efficiently, across a
broad range of industries, as they allow firms of all sizes
to leverage data-driven analysis without needing to make
huge investments in their IT infrastructure.30
As is the case for businesses, policymakers are
entrusted to make decisions for the citizenry with very
little information. Politicians recognize the need to base
their recommendations on objective information, and
they are expected to move quickly, just as business
managers are.
In fact, the public sector is one the most data-
intensive sectors of all. According to McKinsey, the US
government had over 848 petabytes of data stored
in 2009—second only to the manufacturing sector.31
What is usually known as “data-driven policymaking”
involves the collection of information related to how
roads are traveled, to determine trends in utility
consumption and the provision of government services
(Box 1), and to promote creativity and new ideas within
government agencies.32
Statistical agencies inside of
governments, such as census departments, have long
been established to maintain data about the nation.
Thus data-driven policymaking is not new, but the
opportunities brought by the advances on information
and communication technologies make data-driven
policymaking increasingly accessible to government
officials. Further, open government initiatives put
these data into the hands of the public, facilitating a
new kind of transparency and civic engagement for
curious and interested citizens. Data can benefit society
when they are open.33
By providing a way to check assumptions, detect
problems, clarify choices, prioritize resources, and
identify solutions, data-driven policymaking injects data-
based rationality into the policymaking process, all of
which could also create economic benefits.34
According
to the Organisation for Economic Co-operation and
Development (OECD), by fully exploiting public data,
governments in the European Union could reduce
administrative costs by 15 percent to 20 percent,
creating the equivalent of €150 billion to €300 billion.35
In other words, data-driven policymaking moves
policymaking out of the realm of intuition and dogma by
creating a sound evidentiary basis for decisions.
However, studies suggest that the public sector
still does not fully exploit the potential of the data it
generates and collects, nor does it exploit the potential
of data generated elsewhere. The “revolution of data”
still needs to make its way within government agencies.
Although the government is one of the sectors with the
greatest potential to capture value from data-driven
innovation, it also has one of the lowest productivity
growth rates because it lags behind business and
industry in fully embracing data.
Box 1: Hong Kong Efficiency Unit
The Hong Kong Efficiency Unit acts as a single point
of contact for handling public inquiries and complaints
on behalf of many government departments. After
collecting thousands of complaints each year, its staff
recognized the social messages hidden in the complaints
data, which in fact provided important feedback on
public service. Using a platform called the “Complaints
Intelligence System,” they now use the complaints
information collected to gain a better understanding of
daily issues by uncovering trends, patterns, and
relationships inherent in the complaints.
Source: Government of the Hong Kong Special Administrative
Region, Hong Kong Efficiency Unit, 2013.

SETTING THE STAGE FOR A DATA-DRIVEN
ECONOMY
Apart from producing and using data for better
policymaking processes, the public sector can also
play its part by promoting and fostering data-driven
innovation and growth throughout economies. To realize
the potential of data-driven innovation, policymakers
need to develop coherent policies for the use of data.
This could be achieved by: (1) making public data
accessible through open data formats, (2) promoting
balanced legislation, and (3) supporting education that
focuses on data science skills.
Open data initiatives
The use of data across sectors can drive innovation and
economic growth. However, many generators of data—
including governments—do not share their data. As we
have seen, the public sector is one of the main producers
and collectors of data. Open data initiatives that
make data in the public sector accessible to everyone
contribute to data-driven innovation and create value for
governments. For example, aggregate public transport
data may be used by developers to create useful
applications for passengers (see Box 2). This access to
real-time information could result in a greater number
of passengers and, subsequently, to more income for
the transport authorities. In addition, accessible public
data usually lead to better data because data users
can test structure and help to fix mistakes (see Box 3).
Improvements in the quality of data mean better data-
based solutions and, ultimately, better policy.
It is important to note that opening up public data
does not necessarily lead to the disclosure of personal
data. Public data that may contain personal information
of citizens should be shared in an aggregate or fully
de-identified way to protect citizens’ privacy. We will go
into more detail around the discussions on privacy and
personal data in the following section.
How to get the best of data-driven innovation
The increasing ease of linking and analyzing information
usually raises concerns about individual privacy
protection. Personal data are the type that has drawn the
most attention, from a regulatory point of view, in relation
to data-driven innovation. The challenge is to achieve a
reasonable balance between individuals’ right to privacy
and the emerging opportunities in data-driven innovation.
For this reason, in order to capitalize on
opportunities for economic growth via innovation, flexible
and adaptable policies are needed. We need to focus on
using datasets responsibly and ensuring that personally
identifiable information is accessible only by those who
are authorized to do so, without limiting innovation.
In other words, privacy protection frameworks should
support secure and reliable data flows while enhancing
responsible, risk-reducing behavior regarding the use of
personal data.
Legislation should take into account the tension
between data-driven innovation and the principle of
data minimization. This principle essentially states that
the collection of personal data should be limited to
what is relevant and necessary to accomplish a specific
purpose, and for only as long as necessary. This tension
usually materializes in two regulatory discussions: first,
the definition of personal data; and second, the model of
consent by users. These considerations are both critical,
but framing things in this way leads to the inevitable
conclusion that fewer data are better.
A key dividend of data-driven innovation is the
possibility of finding new insights by analyzing existing
data and combining them with other data. This can
sometimes blur the lines between personal and non-
personal data, as well as the uses for which consent
may have been given.36
A practical definition of personal
data should be based on the real possibility of identifying
an individual during the treatment of data.37
This is why
applying existing approaches to personal data may result
in overly broad definitions that can have unintended
negative consequences for data-driven innovation.
For the same reason that combining and correlating
datasets is a key feature of data-driven innovation, the full
potential of data collected may not be clear at the time
of collection. A consent model that is appropriate to the
data-driven economy should provide a path for individuals
to participate in research through informed consent. In
this model, they would become aware of the benefits of
their participation as well as potential privacy risks. For this
reason, the legislative considerations for data collection
should not assume that less is always more and should
take into consideration the data-intensive direction of
some of the economy’s growing sectors.
Building skills for the future
An economy where both the public and private
actors who base their decisions on data analysis will
demand highly skilled workers with backgrounds in
Box 2: Harvard Transparency Project
The Transparency Policy Project at Harvard’s Kennedy
School studied the relationship between transit data
format and accessibility and the number of applications
for that system. Of the five transit agencies they studied,
the TriMet in Portland, Oregon, and the Massachusetts
Bay Transit Authority (MBTA) in Boston have generated the
highest ratio of applications per transit rider (1 to 7,000
and 1 to 27,000, respectively). Meanwhile, the most
reluctant agency to adopt open data, Washington DC’s
Metro, had only 10 applications serving its customers in
2012 (1 to 121,400).
Source: Rojas, 2012.

data analysis, information science, metadata and data
visualization. The demand for engineers who specialize
in technologies such as machine learning and natural
language processing will also increase, and a gap
between the supply and demand for these types of
skills may hinder data-driven innovation’s full potential.
The United States itself will need up to 190,000 more
workers with deep analytical expertise by 2018.38
This
clear demand for skilled workers is further evidence of
data-driven innovation’s potential benefits for economies.
CONCLUSION
We have already begun to see the impact technology
has had on the volume and speed at which data may
be generated, analyzed, and put to use. Thirty years
ago we needed an army of data-entry clerks to feed
an information into a system; today, the information is
already available in a machine-readable format. We
carry devices with sensors that can provide incredible
amounts of information in real time. Every day, the world
adds petabytes of information into social networks and
other Internet platforms.
Talking about this phenomenon as “big data,”
however, misses the true potential of data. Instead, we
should focus our discussion on data-driven innovation,
as this relates to the results and outcomes of data
use—from generating innovative products and service
to improving business and government efficiency. Many
other examples provided earlier have shown that data-
driven solutions have transformative social impact as well.
However, achieving the full potential of data-driven
innovation demands challenging the outdated paradigms
established in a significantly less data-intensive world.
To achieve the maximum benefits from data-driven
innovation, policymakers must take into account the
possibility that regulation could preclude economic
and societal benefits. Decisions that affect data-
driven innovation are usually focused on the problems
of privacy and data protection, but fail to consider
economic and social benefits that regulation could
preclude. It is by looking at the big picture surrounding
big data that we can create the right environment
for data-driven innovation, and that the individuals,
organizations, and economies that may benefit from it
can thrive.
NOTES
1 Gray 2013.
2 The Economist 2013a.
3 Mayer-Schonberger and Cukier 2013.
4 OECD 2013.
5 IBM 2013.
7 The Old Farmer’s Almanac, no date, “History of the Farmer’s
Almanac.”
8 Platzman 1979.
9 Hemerly 2013.
11 OECD 2013.
12 According to Hilbert (2013, p. 4), “the crux of the ‘Big Data’
paradigm is actually not the increasingly large amount of data
itself, but its analysis for intelligent decision-making.”
13 Hemerly 2013.
14 SIIA 2013.
15 Burke 2012.
16 McCormick University 2012. See also Chen 2013.
17 Sims 2011.
18 See www.flightcaster.com.
19 United Nations 2012.
20 Tapscott and Williams 2007.
21 Pélissié du Rausas et al. 2011.
22 Mandel 2012, p. 1.
23 A collection of studies that quantify the contribution of the Internet
to GDP is available at www.valueoftheweb.com.
24 The Economist 2013b.
25 The Economist 2011.
26 Dugas et al. 2013.
27 Lu et al. 2012.
28 Talbot 2013.
29 Brynjolfsson et al. 2011.
30 Cacciola and Gibbons 2012.
32 Esty and Rushing 2007.
33 Rojas 2012.
34 Esty and Rushing 2007.
36 Hemerly 2013.
37 For example, an IP address, by itself, cannot be linked to nor
identify an individual, because it identifies only a device connected
to a network.
Box 3: Can open data lead to better data?
Moscow’s city government published about 170
datasets with geo coordinates at the Moscow opendata
portal. After examining the data, Russian members
of the OpenStreetMap community found many errors
and mistakes, including wrong geo coordinates. After
publishing their research, most of the issues were solved
by Moscow state officials.
Clinicians from the Imperial College London, while
reviewing open statistical data from the United Kingdom’s
National Health Service, found that records said that
20,000 male patients required midwifery services between
2009 and 2010. After this research was published, data
systems were improved.
Source: Open Knowledge Foundation, 2013.

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com/insights/high_tech_telecoms_internet/internet_matters.
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UNGlobalPulseJune2012.pdf.

CHAPTER 1.9
Making Big Data
Something More than the
“Next Big Thing”
ANANT GUPTA
HCL Technologies
Big data is the business buzzword du jour. But how can
you turn this hot topic into a real source of business
value?
You have certainly heard the breathless spiel: The
world today is being flooded with digital data, in myriad
manifestations and washing over us at such incredible
speed that making sense of it is dauntingly difficult. Yet
this tidal wave of data—when channeled and filtered by
an array of new information technologies—holds untold
value for organizations, whether they are small not-for-
profits or Fortune 500 companies.
Or so we are told. But despite the sometimes
exaggerated hype surrounding “big data,” the
fundamental assertion is true: data—and the decisions
driven by those data—now represent the next frontier of
innovation and productivity.
Estimates of the potential benefits of leveraging big
data are indeed staggering: productivity-led savings
worth US$300 billion a year for the US healthcare
industry and €250 billion for the European public sector,
a 60 percent potential increase in retailers’ operating
margins.1
And technology seems poised to deliver these
benefits. One small example: data storage technology
has advanced to the point that only US$600 is all it takes
to purchase storage space that can accommodate the
entire world’s music!2
Some large companies have indeed used emerging
technologies to extract significant value from big data.
Visa recently announced that increasing from 40 to 200
the number of attributes it analyzes in each credit card
transaction has saved 6 cents in every $100 worth of
transactions.3
Wal-Mart uses a self-teaching semantic
search tool that, honed by the monthly clickstream data
of 45 million online shoppers, tailors offerings to online
shoppers, raising the rate of completed transactions by
more than 10 percent.4
But for most businesses, the promise of big data is
nowhere close to being fulfilled. For one thing, spending
on it is polarized. While the telecommunications, travel,
retail, life sciences, and financial services industries are
making significant strides in big data technologies, other
industries, such as manufacturing and government,5
are
in a wait-and-watch mode.
The lack of major big data initiatives across
industries can be seen in the numbers from service
providers. In 2012, the global top 20 big data players
made less than 1 percent of their total revenues from big
data. The total market for big data hardware, software,
and services in 2012 was US$11.5 billion, whereas the
combined overall revenue of those 20 big data players
was more than US$1.2 trillion.
The disparity between a few success stories and
the lack of action elsewhere has created a high level of
anxiety within firms that have not yet begun to explore
big data. But it is important that they not rush
thoughtlessly into the fray. An organization should make

a big data investment only if it has well-defined and
realizable business objectives.
We offer here nine steps that companies can take
to begin turning big data talk into action, buzz into
business benefits.
WHY IS EXTRACTING VALUE FROM BIG DATA
SO HARD?
First, though, we examine some of the barriers to
realizing big data’s promise.
Volume, velocity, and variety
Big data is often said to be characterized by 3 Vs: its
tremendous volume, the velocity at which it needs
to be processed, and the variety of data types it
encompasses. The first two characteristics are fairly
obvious: technology has made it possible to capture
increasingly large amounts of information and make it
available for analysis in real time.
But mining the value of big data also is difficult
because it requires simultaneously analyzing various
types of information—transactions, log data, mail
documents, social media interactions, machine data,
geospatial data, video and audio data, to name just a
few—much of which is “unstructured.” Traditional types
of business data were available in a format that was
structured and could have been automatically analyzed—
for example, a spreadsheet quantifying customer
returns of different products at different stores over
time. However, much of the value in big data exists in
unstructured information—for example, the transcript of a
chat session between a retail customer and a customer
service representative.
Synthesizing unstructured data from numerous
sources and extracting relevant information from it can
be as much art as science.
Talent scarcity
Much has been said and published about the looming
talent gap. Estimates suggest that the United States
alone faces a shortage of 140,000 to 190,000 people
with deep analytical skills, as well as 1.5 million analysts
and managers to analyze big data and make decisions
based on those findings.6
Another report predicts that
only one-third of 4.4 million big data jobs created by
2015 will be filled.7
Unlike traditional analytics, mining big
data requires an extremely diverse set of skills—deep
business insights, data visualization, statistics, machine
learning, and computer programming. Policy should
work to mitigate this talent shortage through forward-
looking education and immigration policies.
Flawed data governance
Big data is not a substitute for—much less a solution
for—flawed information management practices.
If anything, it requires much more rigorous data
governance structures. Without those improvements,
information technology (IT) systems that have not been
upgraded to handle large volumes of data are likely
to collapse under the sheer weight of the data being
processed. Surveys suggest that business leaders
are often more excited about the potential of big data
Box 1: A user’s glossary of key big data terms
As an organization plans its big data strategy, the following
terms are likely to be used with increasing frequency.
• Hadoop: A batch-oriented programming framework
that supports the processing of large data sets in a
distributed computing environment. Hadoop is written
in the Java programming language and is a top-level
Apache project (Apache is a decentralized community
of developers supporting open-source software).
• HBase: A non-relational, column-oriented distributed
database written in Java. A column-oriented database
stores data tables as sections of columns of data
rather than as rows of data, as in most relational
databases, providing fast aggregation and computation
of large numbers of similar data items.
• HDFS: A distributed, scalable, and portable file system
written in Java for the Hadoop framework.
• Hive: A data warehouse infrastructure built on top
of Hadoop, providing data summarization, query,
and analysis. It permits queries over the data using a
familiar SQL-like syntax.
• Flume: A tool for collecting, aggregating, and moving
large amounts of log data from applications to
Hadoop.
• Mahout: A library of Hadoop implementations of
common analytical computations.
• Oozie: A workflow scheduler system developed to
manage Hadoop jobs.
• Pig: A platform for analyzing large datasets that
consists of a high-level language (Pig Latin) for
expressing data analysis programs, coupled with
infrastructure for evaluating these programs.
• R: R is a free software programming language and
software environment for statistical computing and
graphics. The R language is widely used among
statisticians and data miners for developing statistical
software and data analysis.
• Sqoop: A tool facilitating the transfer of data from
relational databases into Hadoop.
• Zookeeper: A centralized service for maintaining
configuration information, naming, providing distributed
synchronization, and providing group services for
distributed applications.
Source: HCL 2013b.
1.9: Making Big Data Something More than the “Next Big Thing”

than their IT counterparts. That may be because of IT
executives’ understanding of the realities on the ground.
Lack of a data-driven mind-set
Because mind-set can be hard to pin down, its power
is often underestimated. That is a mistake when it
comes to assessing the prerequisites to successful
analytics deployment. It is virtually impossible for big
data investments to deliver value if business leaders do
not have a data-driven mind-set—that is, if they do not
believe that it is important for decisions to be based on
cold, hard numbers rather than gut feel and experience.
But once the right mind-set takes hold, other good
things will follow: data-driven business leaders will have
a tremendous incentive to treat data, and therefore the
IT and analytics professionals who help deliver it in an
understandable form, as a strategic asset. And these
leaders will make it a priority to ease the flow of data
across organizational silos.
Lack of technical know-how
Big data represents a convergence of IT and data science.
Technologies include Hadoop (which enables large-
scale processing of diverse datasets), R (a programming
language for statistics), and in-memory databases
(where data reside on main memory as opposed to disk
storage). Data science includes, among many other areas,
machine learning (systems that learn from data) and data
warehousing. Big data professionals are expected to be
familiar with both disciplines, but this combination is rare,
despite the training courses that are sprouting up globally.
(For descriptions of some of the technologies that enable
the analysis of big data, see Box 1.)
NINE STEPS TO BIG DATA VALUE CREATION
The barriers to extracting business value from big data
can seem daunting. But they can be overcome through
a systematic plan, one that breaks down the challenge
into a series of nine sequential steps that will enable
organizations to take advantage of this valuable and
growing asset. We will consider each of these steps
individually here.
Step 1: Define responsibilities.
Who collects, who analyzes, and who drives value?
The onus of collecting data should be shared by the
IT and analytics teams, but analysis must be the sole
responsibility of analytics professionals. Similarly, only
functional leaders—for example, the Chief Marketing
Officer, the Chief Financial Officer, and the Chief
Procurement Officer—should be responsible for
identifying areas within their respective functions where
big data could drive value. However, getting this level of
support from functional leaders is not easy, especially
if the team—IT and analytics or a dedicated big data
center of excellence—reside outside of the business
function. In order to drive the big data program, the
team may want to appoint a big data program sponsor
for each function and work closely with him or her to
discover and locate the types of information that would
improve business outcomes. Most importantly, however,
the program sponsor would try to get functional buy-in
and identify big data opportunities within the function.
Step 2: Get the business functions to ask the right
questions.
Senior executives will have an easier time winning
buy-in from business functions if they demonstrate how
big data might be valuable to them. Simple questions
such as “What would you really like to know about
your business, and how can data help you with it?”
are a good place to start. Such questions can spur the
functional experts themselves to start asking the more
fundamental questions that can unlock the value of data.
For instance, marketing professionals could ask, “What
is the value of a ‘tweet’ or a ‘like’? Are our investments
in customer service paying off? What is the optimal price
for our product right now?” The ability to ask the right
questions is key to succeeding with big data. It also
pays to keep in mind that big data is not about data
themselves; it is about using data to discover insights
that can lead to valuable outcomes.
Step 3: Take stock of all data “worth analyzing.”
Valuable business insight can come from many sources,
including social media feeds, activity streams, and
“dark data” (data that are currently unused but that
have already been captured), machine instrumentation,
and operational technology feeds. It is important to
explore these sources and to experiment with new
ways of capturing information, such as complex-
event processing, video search, and text analytics.
Organizations’ data typically fit into four buckets:
• Operational data, such as data emanating from
smart grid meters, embedded systems (examples
include microwave sensors and chips inserted in
automobiles), transactions logs (such as payment
transactions), radio-frequency identification chips
(RFID), navigation and location sensors, networks,
and servers.
• Streaming data, such as computer network data,
phone conversations, and so on.
• Documents and content, such as PDFs, web
content, and legal discovery elements (electronic
information exchange in civil litigations).
• Rich media, including audio and video tracks,
electronic images, and so on.

Step 4: Select the business functions best
positioned to lead the way.
It is smart to launch big data initiatives in business
functions that are most ready to collect and analyze
data and for which the potential payback is high.
Functions such as marketing, customer service, supply
chain management, and finance are poised for maximum
growth. If system readiness is not an issue, these are
usually the right places to direct initial investments
(see Figure 1).
Step 5: Match big data initiatives with compatible
business functions.
Some big data programs can be implemented in a
variety of settings, but most are suited to specific
functions. For example:
• Customer functions (such as marketing,
e-commerce, and customer service) can use
big data for targeted advertising that provides
personalized offers to consumers based on their
socio-demographic characteristics, and for loyalty
management that extends channel reach from
point of sale, web, and call center to include mobile
and social capabilities.
• Finance functions (such as finance, risk, and
treasury) can use big data for intraday liquidity
management, providing real-time monitoring of
price movements in relation to positions, to make
trading and rebalancing decisions, and for improved
credit risk assessment, through multiple big data–
supported credit risk assessments that factor in
hundreds or even thousands of indicators.
• Supply chain and procurement can use big data
for dynamic route optimization because big data
technologies that are faster than conventional
systems allow more iterations and faster route
planning in real-time.
Step 6: Determine whether big data will yield
valuable information unavailable through traditional
business analytics.
Making the business case for a big data initiative
clearly will be easier if it can be shown that it creates
new value. For instance, if a marketing department is
currently segmenting customer profiles using standard
demographic indicators, would there be additional
benefit in analyzing attitudes and preferences (at a
granular level) through text and speech analysis?
Similarly, if a traditional business intelligence program
is currently analyzing financial market sentiments
using structured stock information, would it make the
sentiment analysis more refined by including social
media feeds, news sites, and so on?
In comparing views of data from a traditional
business intelligence perspective versus a big data
one, consider the following the questions: What data
are we capturing today? What are the limitations of this
kind of structured data? What extra value will we get by
collecting external, context-specific, and unstructured
data? Where will we find data and how will we collect
them? Would our business act upon the insights
0 5 10 15 20 25 30 35 40 45 50
0
10
20
30
40
50
Figure 1: Potential payback of big data initiatives
Source: Gartner, 2013.
Datasystemsmostfitforpurpose
Easy pickings
Overeager
Invest here
Not ready but who cares?
Most opportunity for analysis
Least opportunity for analysis
Datasystemsleastfitforpurpose
Customer service
Operations & production
Sales
Finance
Information & research
Risk/security
R&D
Procurement
Supply chain management
IT
Operations, real estate planning
Regulatory compliance
Legal
Human resources
Marketing
Strategy & business development
General management

gained? Is the extra business value worth the additional
investment of time, energy, and money?
Step 7: Assess complexities and prioritize
accordingly.
All else being equal, an organization should begin its
big data experimentation with an initiative that is not too
demanding. In assessing possibilities, it is helpful to keep
in mind the complexity of both the type of data and the
type of analysis the data will require.
As we mentioned above, much of what is meant
by “big data” is unstructured information—data that
traditionally have been impossible to break down and
categorize as they are collected. Such data are not only
difficult to analyze but can also be easily misinterpreted
when taken out of context. Thus it makes sense to
experiment in the beginning with data that are relatively
easy to analyze.
Different types of analysis also present varying
degrees of complexity. Generally speaking, descriptive
analytics (which answers “what happened?”—for
example, an analysis of social media sentiment
analysis) are relatively easy to do. However, diagnostic
analytics (which answers “why did it happen?”—for
example, an analysis of customer defection at the
shopping cart stage of the online purchase process);
predictive analytics (which answers “what will
happen?”—for example, forecasts of customer churn in
telecommunications); and prescriptive analytics (which
answers “how can we make it happen?”—for example,
determining whether personalized offers to customers
would make sense), are increasingly complex to conduct.
Step 8: Assess your technology architecture.
An organization’s traditional information architecture
may not accommodate massive, high-speed, variable
data flows. Many traditional and even state-of-the-
art technologies were not designed for today’s or
tomorrow’s level of data volume, velocity, and variety.
Even as datasets grow exponentially along those
dimensions, the investments required for scaling
technologies (such as processors, storage, database
management systems, and analytics) to perform
efficiently grow even faster. To counter these intractable
economics, organizations need to consider a variety of
methods to upgrade their infrastructure in support of or
in anticipation of big data.
In fact, the idea that big data involves negligible
cost because it is analyzed using open-source tools and
platforms is a myth. “Free” open-source technologies
such as Hadoop (which enables large-scale processing
of diverse datasets) are typically not immediately
usable. You need either to hire and train data scientists
and analysts in Hadoop programming, or to buy an
enterprise-ready version of Hadoop.
If the outcome of big data analysis is mission-critical
for your business, it probably makes sense to use only
purpose-built hardware. Generic servers may be fine for
smaller projects and proofs of concept, but specifically
designed, enterprise-grade servers, storage, and
networking products are best for large-scale-production
solutions.
Creating or upgrading to big data–ready technology
architecture is no small feat. Building everything from
scratch takes time, and buying everything is expensive.
Therefore, finding the right combination of insourcing and
outsourcing requires careful consideration.
Step 9: Start building a team.
Big data initiatives require multidisciplinary teams of
business and technology experts. Every team member—
business analyst, programmer, data scientist, and data
visualizer—will need to have cross-functional familiarity.
Building this team is a five-step process:
• Break down your talent needs into four distinct
areas: business analysis, analytics, database
technology, and data visualization.
• Scan your internal landscape for the aforementioned
skills. Although they may not be in the target
department, every organization probably already
includes people who know the business, possess
data-crunching capabilities, and make data-driven
decisions.
• Hire people with needed skills if they are not
available or cannot be acquired by cross-training
existing employees.
• Hire people with related skills if the needed skills
are unavailable within your organization or difficult
to acquire through external hires. For instance,
consider substituting statisticians for the much less
common data scientists.
• Start small and scale up. In the beginning, your
needs will be modest. A few hires may be adequate
to get started.
Some are even predicting that big data analytics will
lead to the emergence of an entirely new set of CXO roles
within enterprises—Chief Data Officer, Chief Digital Officer,
Chief Analytics Officer, and so on. That said, the structure
of most organizations would make it difficult for someone
owning the big data portfolio to succeed. Without clear
line responsibilities, a CDO (whichever flavor, Data or
Digital) or a CAO would have little leverage to execute the
important tasks needed to increase the organization’s big
data capabilities and optimize its initiatives.
Instead, big data and business analytics expertise
should fall within existing functions—for example,
finance, human resources, and marketing—with the aim
of furthering the strategic initiatives of those functions.
The efforts of the big data teams in these areas could
be overseen and coordinated by a big data manager,
reporting to the Chief Information Officer, who would

ensure that best practices were adopted and that
initiatives were coordinated.
Following the nine steps described above will help
the IT function to assume such responsibilities.
CASE STUDIES
Many global organizations have already begun
embarking on deriving value out of big data initiatives.
Almost all of them have defined step-by-step frameworks
somewhat similar to the one outlined above. The sheer
variety of value creation evident—from clinical trials
and marketing to risk management and audits, from
analyzing crop and seed production to fan listening
posts—is also staggering (see Box 2).
RECOMMENDATIONS FOR GOVERNMENT ACTIONS
AND POLICIES
In order to take full advantage of the potential of big data
in both the public and private sectors, we recommend
that governments create a vision and platform for public-
sector open data. We believe that open data will be
an essential characteristic of future public policy. It is
important that such a vision percolate down from the top
to garner support from ministries and civil servants alike
so that open data initiatives function effectively.
Communicating from the very top that open data
is an essential characteristic of public policy is crucial.
Furthermore, governments should create an easy-to-
use platform for the public to access the data in a form
that is easily digestible and ready for analysis. It is also
advisable to develop rules and regulations for taxing the
commercial use of open data.
Governments should spearhead the effort to
ensure the privacy and security of personal data. The
appropriate agency should take a leading role in working
with all relevant private- and public-sector entities
to develop and implement policies for safeguarding
personal data and means for enforcement.
Box 2: Organizations already using big data initiatives
A few organizations that have followed frameworks for using
big data include:
• A US-based mid- to upscale chain of department stores
is gaining new insights from analyzing and combining data
on Hadoop with data from traditional databases to turn its
marketing staff from “Mad Men” to “Math Men.”
• A US-based provider of business outsourcing solutions
has set up an innovation lab where subject experts from
different industries and backgrounds work together to
tackle big data analytics.
• An Indianapolis-based global pharmaceutical company
is using big data to develop an integrated approach to
optimizing how clinical trials are conducted and eliminate
inefficiencies.
• A US-based document management corporation is
applying its decades of expertise in imaging technologies
to transportation systems that can benefit from real-time
analysis of data.
• Australia-based telecommunications companies use big
data to determine which of their customers are less likely
to pay their bills, allowing them to focus collection efforts
on that group rather than across the whole customer
base.
• A global corporation offering computer-assisted legal
research services uses a big data technology platform
it has developed in house both for its risk management
business and for gathering data it sells to its clients. It
now also sells this big data platform through its newly
established subsidiary.
• A US-based multinational consumer goods company has
developed a decision-support environment used by more
than 60,000 employees worldwide to see what is
happening in the business, to understand why it is
happening, and to determine how to respond to changing
market conditions. The decision cockpit is focused
on forward-looking projections rather than historical
reporting, with data visualization showing the relative
revenue and profit contribution of each region, country,
territory, brand, and product. The company’s performance
is also tracked against that of competing brands and
products. The company has established about 50
collaborative conference rooms, called Business Spheres,
in offices around the globe. The rooms are surrounded
by projection screens for displaying the dashboards as
well as live video-conferencing sessions, allowing remote
executives to attend weekly review meetings in person.
• A California-based multinational energy corporation is
using big data to transform the audit function. It runs audit
tests on all of its accounts payable transactions instead
of only on the small sample it used to analyze before.
This enables the finance department to better understand
various business risks and adjust audit coverage to the
areas that pose the greatest risks. At the same time, it
reduces the time spent on auditing by about 15 percent.
• A British multinational music recording and publishing
company has created the Million Interview Dataset over
the last few years, asking consumers in 24 countries
and across 15 languages about their music listening and
consumption habits. The Dataset provides rich insights
into the interests, attitudes, behaviors, familiarity, and
appreciation of music as expressed by music fans.
• A Minnesota-based member-owned agricultural
cooperative combines 20 years of satellite imagery with
local seed and crop protection data from its test sites
to provide a service that generates field performance
information for every acre and matches crop inputs and
decisions to the potential of each field and each zone.
Source: HCL Technologies.

Moreover, it is essential to develop and execute a
big data plan for all government services and activities.
The plan should identify all government data worth
analyzing, define data collection responsibilities, outline
steps to ensure data quality, and determine where big
data technologies and analysis capabilities should be
first deployed.
Finally, each government should establish a big
data center of excellence (BDCOE). The BDCOE should
be the focal point of expertise, long-range thinking and
policy formulation, and training and development. It
should also be the repository of best practices. It should
not only serve as a resource for all government agencies
but should also act as the government’s leading
authority on all matters related to data management.
CONCLUSION
Big data analytics is not a passing fad. It will be a
central means of creating value for the organization of
tomorrow—and that is “tomorrow” almost literally. It
represents a major change in the way that businesses
and other organizations will operate and will require a
new mind-set and new capabilities. Given that, many
organizations are struggling to know where to start in
becoming competent in the realm of big data. A step-
by-step approach can make the transition seem less
daunting and minimize the stumbles that are bound to
occur along the way.
NOTES
3 Laney 2012.
4 Laney 2012.
5 HCL Technologies 2013a.
REFERENCES
Gartner. 2013. What’s the Big Deal with Big Data for Customer Service?
Webinar with Gareth Herschel, Research Director, Gartner and
Michael Maoz, VP Distinguished Analyst, Gartner. September 17.
Available at http://my.gartner.com/portal/server.pt?open=512&objI
D=202&mode=2&PageID=5553&ref=webinar-rss&resId=2569818.
HCL Technologies. 2013a. Strategic Intelligence Wing Research on Big
Data (the research arm of HCL Technologies).
———. 2013b. CIO Straight Talk Issue 3. Quincy, Mass, US and
Noida, India: HCL Technologies. Available at http://magazine.
straighttalkonline.com/issue3/.
Laney, D. 2012. Big Data Strategy Components: IT Essentials. October
15, ID G00238944. Chicago: Gartner.
Manyika, J., M. Chui, B. Brown, J. Bughin, R, Dobbs, C. Roxburgh, and
A. Hung Byers. 2011. “Big Data: The Next Frontier for Innovation,
Competition, and Productivity.” McKinsey Global Institute Report.

Part 2
Country/Economy Profiles

How to Read the Country/Economy Profiles
The Country/Economy Profiles section presents a profile
for each of the 148 economies covered in The Global
Information Technology Report 2014. Each profile sum-
marizes an economy’s performance in the various di-
mensions of the Networked Readiness Index (NRI).
PERFORMANCE HIGHLIGHTS
The first section of the profile presents the economy’s
overall performance in the NRI, along with its perfor-
mance in the NRI’s four components and 10 pillars. The
economy’s rank (out of 148 economies) and score (on a
1-to-7 scale) are reported.
On the radar chart to the right of the table, a blue
line plots the economy’s score on each of the 10 pillars.
The black line represents the average score of all econo-
mies in the income group to which the economy under
review belongs. The country classification by income
group is defined by the World Bank and reflects the
situation as of December 2013. Note that the two high-
income groups in this classification, High income: OECD
and High income: non-OECD, were merged into a single
group for the purpose of the analysis.
THE NETWORKED READINESS INDEX IN DETAIL
This section presents an economy’s performance in
each of the 54 indicators composing the NRI. The in-
dicators are organized by pillar. The numbering of the
variables matches that of the data tables in the next sec-
tion of the Report, which provide descriptions, rankings,
and scores for all the indicators. The indicators derived
from the 2012 and 2013 editions of the World Economic
Forum’s Executive Opinion Survey are identified by an
asterisk (*). These indicators are always measured on a
1-to-7 scale (where 1 and 7 correspond to the worst and
best possible outcomes, respectively). For more infor-
mation on the Executive Opinion Survey and a detailed
explanation of how scores are computed, please refer to
Chapter 1.3 of The Global Competitiveness Report 2013–
2014, available for free on the World Economic Forum
website at www.weforum.org/gcr.
For those indicators not derived from the World
Economic Forum’s Executive Opinion Survey, the scale
is reported next to the title. The Technical Notes and
Sources at the end of this Report provides further de-
tails on each indicator, including its definition, method of
computation, and sources.
Note that for the sake of readability, the years
were omitted. However, the year of each data point is
indicated in the corresponding data table. For more in-
formation on the framework and computation of the NRI,
refer to Chapter 1.1.
ONLINE DATA PORTAL
In complement to the analysis presented in this
Report, an online data portal can be accessed via www.
weforum.org/gitr. The platform offers a number of analyt-
ical tools and visualizations, including sortable rankings,
scatter plots, bar charts, and maps, as well as the pos-
sibility of downloading portions of the NRI dataset.
3: Country/Economy Profiles
Note: Indicators followed by an asterisk (*) are measured on a 1-to-7 (best) scale. For
further details and explanation, please refer to the section “How to Read the
Country/Economy Profiles” on page 97
1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
The Networked Readiness Index in detail
INDICATOR RANK/148 VALUE
1.01 Effectiveness of law-making bodies* ................105 ......... 3.1
1.02 Laws relating to ICTs* ........................................85 ......... 3.7
1.03 Judicial independence* ....................................134 ......... 2.3
1.04 Efficiency of legal system in settling disputes*..120 ......... 3.0
1.05 Efficiency of legal system in challenging regs*..113 ......... 2.9
1.06 Intellectual property protection*........................114 ......... 2.9
1.07 Software piracy rate, % software installed..........77 .......... 75
1.08 No. procedures to enforce a contract ................91 .......... 39
1.09 No. days to enforce a contract ..........................69 ........ 525
2.01 Availability of latest technologies*.....................119 ......... 4.1
2.02 Venture capital availability* ...............................136 ......... 1.9
2.03 Total tax rate, % profits ......................................48 ....... 31.7
2.04 No. days to start a business ..............................12 ............ 5
2.05 No. procedures to start a business....................34 ............ 5
2.06 Intensity of local competition*...........................144 ......... 3.4
2.07 Tertiary education gross enrollment rate, %........50 ....... 54.9
2.08 Quality of management schools*........................84 ......... 4.1
2.09 Gov’t procurement of advanced tech* ...............52 ......... 3.7
3.01 Electricity production, kWh/capita......................93 ... 1318.7
3.02 Mobile network coverage, % pop. .....................58 ....... 99.0
3.03 Int’l Internet bandwidth, kb/s per user................76 ....... 17.4
3.04 Secure Internet servers/million pop. ...................85 ....... 19.0
3.05 Accessibility of digital content*.........................116 ......... 4.1
4th pillar: Affordability
4.01 Mobile cellular tariffs, PPP $/min......................120 ....... 0.44
4.02 Fixed broadband Internet tariffs, PPP $/month ..48 ..... 26.05
4.03 Internet & telephony competition, 0–2 (best) ......92 ....... 1.64
5th pillar: Skills
5.01 Quality of educational system* ...........................52 ......... 4.0
5.02 Quality of math & science education*.................54 ......... 4.3
5.03 Secondary education gross enrollment rate, %..90 ....... 82.4
5.04 Adult literacy rate, %..........................................56 ....... 96.8
6.01 Mobile phone subscriptions/100 pop.................70 ..... 110.7
6.02 Individuals using Internet, %...............................58 ....... 54.7
6.03 Households w/ personal computer, % ...............95 ....... 20.0
6.04 Households w/ Internet access, % ....................87 ....... 20.5
6.05 Fixed broadband Internet subs./100 pop...........78 ......... 5.1
6.06 Mobile broadband subscriptions/100 pop..........71 ....... 18.8
6.07 Use of virtual social networks* .........................110 ......... 5.1
7.01 Firm-level technology absorption* ....................108 ......... 4.2
7.02 Capacity for innovation* ...................................121 ......... 3.0
7.03 PCT patents, applications/million pop. ..............85 ......... 0.3
7.04 Business-to-business Internet use*..................135 ......... 3.8
7.05 Business-to-consumer Internet use* ................131 ......... 3.4
7.06 Extent of staff training*.......................................36 ......... 4.4
8.01 Importance of ICTs to gov’t vision*.....................78 ......... 3.9
8.02 Government Online Service Index, 0–1 (best).....88 ....... 0.42
8.03 Gov’t success in ICT promotion*......................106 ......... 3.9
9.01 Impact of ICTs on new services & products*....121 ......... 3.6
9.02 ICT PCT patents, applications/million pop. .......93 ......... 0.0
9.03 Impact of ICTs on new organizational models*.124 ......... 3.4
9.04 Knowledge-intensive jobs, % workforce.............99 ....... 13.7
10.01 Impact of ICTs on access to basic services* ....126 ......... 3.3
10.02 Internet access in schools* ................................73 ......... 4.2
10.03 ICT use & gov’t efficiency* .................................82 ......... 4.0
10.04 E-Participation Index, 0–1 (best).........................92 ....... 0.11
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2014..............95..3.7
Networked Readiness Index 2013 (out of 144).................... 83.....3.8
A. Environment subindex.................................................. 95.....3.7
1st pillar: Political and regulatory environment............... 117.....3.1
2nd pillar: Business and innovation environment ............. 65.....4.3
B. Readiness subindex .................................................... 86.....4.6
3rd pillar: Infrastructure and digital content...................... 90.....3.5
4th pillar: Affordability ...................................................... 87.....5.1
5th pillar: Skills ................................................................ 62.....5.2
C. Usage subindex............................................................ 95.....3.4
6th pillar: Individual usage ............................................... 82.....3.2
7th pillar: Business usage ............................................. 107.....3.2
8th pillar: Government usage .......................................... 93.....3.8
D. Impact subindex......................................................... 108.....2.9
9th pillar: Economic impacts ......................................... 125.....2.6
10th pillar: Social impacts ............................................... 97.....3.3
Albania
Albania Lower-middle-income group average

Index of Country/Economy Profiles
Country/Economy Page
Albania 100
Algeria 101
Angola 102
Argentina 103
Armenia 104
Australia 105
Austria 106
Azerbaijan 107
Bahrain 108
Bangladesh 109
Barbados 110
Belgium 111
Benin 112
Bhutan 113
Bolivia 114
Bosnia and Herzegovina 115
Botswana 116
Brazil 117
Brunei Darussalam 118
Bulgaria 119
Burkina Faso 120
Burundi 121
Cambodia 122
Cameroon 123
Canada 124
Cape Verde 125
Chad 126
Chile 127
China 128
Colombia 129
Costa Rica 130
Côte d'Ivoire 131
Croatia 132
Cyprus 133
Czech Republic 134
Denmark 135
Dominican Republic 136
Ecuador 137
Egypt 138
El Salvador 139
Estonia 140
Ethiopia 141
Finland 142
France 143
Gabon 144
Gambia, The 145
Georgia 146
Germany 147
Ghana 148
Greece 149
Guatemala 150
Guinea 151
Guyana 152
Haiti 153
Honduras 154
Hong Kong SAR 155
Hungary 156
Iceland 157
India 158
Indonesia 159
Iran, Islamic Rep. 160
Ireland 161
Israel 162
Italy 163
Jamaica 164
Japan 165
Jordan 166
Kazakhstan 167
Kenya 168
Korea, Rep. 169
Kuwait 170
Kyrgyz Republic 171
Lao PDR 172
Latvia 173
Lebanon 174
Lesotho 175
Liberia 176
Libya 177
Lithuania 178
Luxembourg 179
Macedonia, FYR 180
Madagascar 181
Malawi 182
Malaysia 183
Mali 184
Malta 185
Mauritania 186
Mauritius 187
Mexico 188
Moldova 189
Mongolia 190
Montenegro 191
Morocco 192
Mozambique 193
Myanmar 194
Namibia 195
Nepal 196
Netherlands 197
New Zealand 198
Nicaragua 199
Nigeria 200
Norway 201
Oman 202
Pakistan 203
Panama 204
Paraguay 205
Peru 206
Philippines 207
Poland 208
Portugal 209
Puerto Rico 210
Qatar 211
Romania 212
Russian Federation 213
Rwanda 214
Saudi Arabia 215
Seychelles 216
Senegal 217
Serbia 218
Sierra Leone 219
Singapore 220
Slovak Republic 221
Slovenia 222
South Africa 223
Spain 224
Sri Lanka 225
Suriname 226
Swaziland 227
Sweden 228
Switzerland 229
Taiwan, China 230
Tanzania 231
Thailand 232
Timor-Leste 233
Trinidad and Tobago 234
Tunisia 235
Turkey 236
Uganda 237
Ukraine 238
United Arab Emirates 239
United Kingdom 240
United States 241
Uruguay 242
Venezuela 243
Vietnam 244
Yemen 245
Zambia 246
Zimbabwe 247

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.01 Electricity production, kWh/capita......................93 .. 1,318.7
4.01 Prepaid mobile cellular tariffs, PPP $/min.........120 ....... 0.44
5th pillar: Skills
Rank Value
(out of 148) (1–7)
B. Readiness subindex .................................................... 86.....4.6
5th pillar: Skills ................................................................ 62.....5.2
C. Usage subindex............................................................ 95.....3.4
Albania
Albania Lower-middle-income group average

Country/Economy Profiles” on page 97.
1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.02 Laws relating to ICTs* ......................................146 ......... 2.1
1.03 Judicial independence* ......................................95 ......... 3.2
1.08 No. procedures to enforce a contract ..............131 .......... 45
1.09 No. days to enforce a contract ........................101 ........ 630
2.03 Total tax rate, % profits ....................................142 ....... 71.9
2.04 No. days to start a business ............................103 .......... 25
2.05 No. procedures to start a business..................142 .......... 14
2.08 Quality of management schools*......................135 ......... 3.0
2.09 Gov’t procurement of advanced tech* .............127 ......... 2.7
3.02 Mobile network coverage, % pop. ...................124 ....... 81.5
3.03 Int’l Internet bandwidth, kb/s per user................99 ......... 7.7
3.04 Secure Internet servers/million pop. .................128 ......... 1.3
4.01 Prepaid mobile cellular tariffs, PPP $/min...........49 ....... 0.18
4.03 Internet & telephony competition, 0–2 (best) ....104 ....... 1.33
5th pillar: Skills
5.01 Quality of educational system* .........................133 ......... 2.7
5.02 Quality of math & science education*...............132 ......... 2.7
5.04 Adult literacy rate, %........................................117 ....... 72.6
6.01 Mobile phone subscriptions/100 pop.................94 ....... 97.9
6.02 Individuals using Internet, %.............................113 ....... 15.2
6.06 Mobile broadband subscriptions/100 pop........140 ......... 0.0
7.06 Extent of staff training*.....................................139 ......... 3.0
8.01 Importance of ICTs to gov’t vision*...................122 ......... 3.2
8.02 Government Online Service Index, 0–1 (best)...123 ....... 0.25
10.02 Internet access in schools* ..............................138 ......... 2.2
10.03 ICT use & gov’t efficiency* ...............................141 ......... 2.9
10.04 E-Participation Index, 0–1 (best).......................107 ....... 0.05
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2014............129..3.0
Networked Readiness Index 2013 (out of 144).................. 131.....2.8
A. Environment subindex................................................ 143.....2.8
2nd pillar: Business and innovation environment ........... 145.....2.9
B. Readiness subindex ................................................... 101.....4.1
3rd pillar: Infrastructure and digital content.................... 127.....2.4
5th pillar: Skills .............................................................. 102.....4.0
C. Usage subindex.......................................................... 134.....2.7
6th pillar: Individual usage ............................................. 104.....2.5
8th pillar: Government usage ........................................ 134.....3.0
10th pillar: Social impacts ............................................. 140.....2.3
Algeria
Algeria Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.01 Effectiveness of law-making bodies* ..................97 ......... 3.2
1.07 Software piracy rate, % software installed.........n/a ......... n/a
1.09 No. days to enforce a contract ........................140 ..... 1,296
2.07 Tertiary education gross enrollment rate, %......128 ......... 7.5
3.01 Electricity production, kWh/capita....................120 ..... 280.0
3.03 Int’l Internet bandwidth, kb/s per user..............142 ......... 0.6
4.02 Fixed broadband Internet tariffs, PPP $/month 115 ..... 57.22
5th pillar: Skills
5.03 Secondary education gross enrollment rate, %140 ....... 31.5
6.01 Mobile phone subscriptions/100 pop...............138 ....... 47.1
6.03 Households w/ personal computer, % .............118 ......... 8.5
6.04 Households w/ Internet access, % ..................115 ......... 7.2
6.05 Fixed broadband Internet subs./100 pop.........124 ......... 0.2
7.03 PCT patents, applications/million pop. ............118 ......... 0.0
9.04 Knowledge-intensive jobs, % workforce............n/a ......... n/a
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2013 (out of 144)................... n/a.....n/a
4th pillar: Affordability .................................................... 110.....4.1
5th pillar: Skills .............................................................. 141.....2.3
C. Usage subindex.......................................................... 142.....2.5
Angola
Angola Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.03 Total tax rate, % profits ....................................147 ..... 107.8
3.05 Accessibility of digital content*...........................88 ......... 4.7
4.03 Internet & telephony competition, 0–2 (best) ........1 ....... 2.00
5th pillar: Skills
6.05 Fixed broadband Internet subs./100 pop...........56 ....... 10.9
6.07 Use of virtual social networks* ...........................45 ......... 6.0
7.02 Capacity for innovation* .....................................91 ......... 3.3
7.05 Business-to-consumer Internet use* ..................68 ......... 4.6
9.03 Impact of ICTs on new organizational models*...88 ......... 3.9
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 79.....4.9
C. Usage subindex............................................................ 77.....3.6
7th pillar: Business usage ............................................... 99.....3.3
D. Impact subindex........................................................... 96.....3.2
9th pillar: Economic impacts ........................................... 87.....3.1
Argentina
Argentina Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.04 Efficiency of legal system in settling disputes*....74 ......... 3.7
1.05 Efficiency of legal system in challenging regs*....76 ......... 3.4
1.06 Intellectual property protection*..........................75 ......... 3.6
1.07 Software piracy rate, % software installed........101 .......... 88
2.01 Availability of latest technologies*.......................97 ......... 4.5
2.02 Venture capital availability* .................................93 ......... 2.4
2.04 No. days to start a business ................................9 ............ 4
2.05 No. procedures to start a business......................3 ............ 2
5th pillar: Skills
7.01 Firm-level technology absorption* ......................98 ......... 4.4
7.04 Business-to-business Internet use*....................48 ......... 5.2
8.03 Gov’t success in ICT promotion*........................46 ......... 4.6
9.01 Impact of ICTs on new services & products*......46 ......... 4.7
10.01 Impact of ICTs on access to basic services* ......64 ......... 4.3
Rank Value
(out of 148) (1–7)
B. Readiness subindex ..................................................... 51.....5.1
5th pillar: Skills ................................................................ 47.....5.4
C. Usage subindex............................................................ 73.....3.7
Armenia
Armenia Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.07 Software piracy rate, % software installed............5 .......... 23
2.06 Intensity of local competition*.............................13 ......... 5.8
2.07 Tertiary education gross enrollment rate, %..........8 ....... 83.2
3.01 Electricity production, kWh/capita........................9 11,120.8
3.04 Secure Internet servers/million pop. .....................8 .. 1,724.5
5th pillar: Skills
5.03 Secondary education gross enrollment rate, %....1 ..... 133.0
6.06 Mobile broadband subscriptions/100 pop............7 ....... 96.2
7.03 PCT patents, applications/million pop. ..............21 ....... 76.2
8.02 Government Online Service Index, 0–1 (best).......9 ....... 0.86
9.02 ICT PCT patents, applications/million pop. .......20 ....... 20.1
10.04 E-Participation Index, 0–1 (best)...........................8 ....... 0.76
Rank Value
(out of 148) (1–7)
1st pillar: Political and regulatory environment................. 15.....5.2
B. Readiness subindex ....................................................... 9.....6.2
3rd pillar: Infrastructure and digital content........................ 8.....6.8
5th pillar: Skills ................................................................ 20.....5.8
C. Usage subindex............................................................ 19.....5.3
Australia
Australia High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.08 No. procedures to enforce a contract ..................4 .......... 25
3.03 Int’l Internet bandwidth, kb/s per user................17 ..... 108.1
3.04 Secure Internet servers/million pop. ...................16 .. 1,134.7
5th pillar: Skills
7.03 PCT patents, applications/million pop. ..............10 ..... 159.6
7.04 Business-to-business Internet use*......................9 ......... 5.9
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 22.....5.8
C. Usage subindex............................................................ 20.....5.3
Austria
Austria High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.09 No. days to enforce a contract ............................6 ........ 237
3.02 Mobile network coverage, % pop. .......................1 ..... 100.0
5th pillar: Skills
5.04 Adult literacy rate, %............................................3 ....... 99.8
8.01 Importance of ICTs to gov’t vision*.......................6 ......... 5.5
8.03 Gov’t success in ICT promotion*..........................8 ......... 5.6
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 66.....5.1
C. Usage subindex............................................................ 44.....4.2
Azerbaijan
Azerbaijan Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.03 Total tax rate, % profits ........................................5 ....... 13.5
3.01 Electricity production, kWh/capita......................12 10,694.9
3.04 Secure Internet servers/million pop. ...................45 ..... 135.8
5th pillar: Skills
6.03 Households w/ personal computer, % .................3 ....... 92.7
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 58.....5.2
C. Usage subindex............................................................ 25.....5.1
8th pillar: Government usage ............................................ 5.....5.6
Bahrain
Bahrain High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.04 No. days to start a business ..............................57 .......... 11
2.07 Tertiary education gross enrollment rate, %......109 ....... 13.2
4.01 Prepaid mobile cellular tariffs, PPP $/min.............5 ....... 0.04
4.02 Fixed broadband Internet tariffs, PPP $/month ....3 ..... 10.37
5th pillar: Skills
6.02 Individuals using Internet, %.............................128 ......... 6.3
9.04 Knowledge-intensive jobs, % workforce...........109 ......... 7.3
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 128.....2.8
C. Usage subindex.......................................................... 120.....2.9
Bangladesh
Bangladesh Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
5.01 Quality of educational system* .............................6 ......... 5.3
5.02 Quality of math & science education*...................9 ......... 5.5
5.03 Secondary education gross enrollment rate, %..23 ..... 104.7
5.04 Adult literacy rate, %.........................................n/a ......... n/a
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 15.....5.9
C. Usage subindex............................................................ 43.....4.3
Barbados
Barbados High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.06 Intensity of local competition*...............................6 ......... 6.0
2.08 Quality of management schools*..........................2 ......... 6.0
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .................................................................. 4.....6.3
C. Usage subindex............................................................ 27.....5.0
Belgium
Belgium High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.01 Electricity production, kWh/capita....................146 ....... 15.7
4.02 Fixed broadband Internet tariffs, PPP $/month 132 ... 101.41
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 130.....2.8
C. Usage subindex.......................................................... 137.....2.6
Benin
Benin Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
6.03 Households w/ personal computer, % .............101 ....... 16.4
6.04 Households w/ Internet access, % ..................102 ....... 11.6
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 114.....3.6
C. Usage subindex.......................................................... 110.....3.1
Bhutan
Bhutan Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.04 Secure Internet servers/million pop. ...................94 ......... 9.7
5th pillar: Skills
6.06 Mobile broadband subscriptions/100 pop..........98 ......... 6.6
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 93.....4.5
C. Usage subindex.......................................................... 116.....3.0
Bolivia
Bolivia Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 57.....5.3
C. Usage subindex............................................................ 79.....3.6
Bosnia and Herzegovina
Bosnia and Herzegovina Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.05 No. procedures to start a business..................107 ............ 9
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 92.....4.6
C. Usage subindex............................................................ 88.....3.5
Botswana
Botswana Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.04 No. days to start a business ............................146 ........ 108
3.02 Mobile network coverage, % pop. .....................28 ..... 100.0
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 91.....4.6
C. Usage subindex............................................................ 47.....4.1
Brazil
Brazil Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.02 Mobile network coverage, % pop. ....................n/a ......... n/a
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 30.....5.7
C. Usage subindex............................................................ 41.....4.4
Brunei Darussalam
Brunei Darussalam High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 56.....5.3
C. Usage subindex............................................................ 65.....3.9
Bulgaria
Bulgaria Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 144.....2.1
C. Usage subindex.......................................................... 127.....2.7
Burkina Faso
Burkina Faso Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
4.01 Prepaid mobile cellular tariffs, PPP $/min..........n/a ......... n/a
4.02 Fixed broadband Internet tariffs, PPP $/month .n/a ......... n/a
5th pillar: Skills
Rank Value
(out of 148) (1–7)
4th pillar: Affordability ..................................................... n/a.....n/a
5th pillar: Skills .............................................................. 137.....2.4
C. Usage subindex.......................................................... 148.....2.1
Burundi
Burundi Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 119.....3.3
C. Usage subindex.......................................................... 107.....3.2
Cambodia
Cambodia Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 116.....3.6
C. Usage subindex.......................................................... 119.....2.9
Cameroon
Cameroon Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
2nd pillar: Business and innovation environment ............... 3.....5.5
5th pillar: Skills .................................................................. 8.....6.1
C. Usage subindex............................................................ 26.....5.0
Canada
Canada High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 82.....4.8
C. Usage subindex............................................................ 92.....3.4
Cape Verde
Cape Verde Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.01 Electricity production, kWh/capita....................148 ......... 8.4
3.04 Secure Internet servers/million pop. ..................n/a ......... n/a
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 148.....1.9
C. Usage subindex.......................................................... 147.....2.2
Chad
Chad Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 71.....5.0
C. Usage subindex............................................................ 39.....4.4
Chile
Chile Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 59.....5.2
C. Usage subindex............................................................ 61.....3.9
China
China Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 76.....4.9
C. Usage subindex............................................................ 62.....3.9
Colombia
Colombia Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 24.....5.8
C. Usage subindex............................................................ 57.....4.0
Costa Rica
Costa Rica Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 133.....2.7
C. Usage subindex.......................................................... 115.....3.0
Côte d’Ivoire
Côte d’Ivoire Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 36.....5.6
C. Usage subindex............................................................ 46.....4.2
Croatia
Croatia High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 11.....6.1
C. Usage subindex............................................................ 48.....4.1
Cyprus
Cyprus High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
7.05 Business-to-consumer Internet use* ....................9 ......... 5.8
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 49.....5.3
C. Usage subindex............................................................ 40.....4.4
Czech Republic
Czech Republic High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.03 Judicial independence* ........................................8 ......... 6.2
5th pillar: Skills
6.02 Individuals using Internet, %.................................4 ....... 93.0
6.04 Households w/ Internet access, % ......................6 ....... 92.0
6.05 Fixed broadband Internet subs./100 pop.............3 ....... 38.8
7.03 PCT patents, applications/million pop. ................7 ..... 208.5
9.04 Knowledge-intensive jobs, % workforce...............9 ....... 45.2
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2013 (out of 144)...................... 8.....5.6
5th pillar: Skills ................................................................ 18.....5.9
C. Usage subindex.............................................................. 7.....5.7
6th pillar: Individual usage ................................................. 3.....6.6
7th pillar: Business usage ................................................. 7.....5.7
Denmark
Denmark High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 106.....3.9
C. Usage subindex............................................................ 87.....3.5
Dominican Republic
Dominican Republic Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 75.....4.9
C. Usage subindex............................................................ 74.....3.6
Ecuador
Ecuador Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 120.....3.3
C. Usage subindex............................................................ 89.....3.5
Egypt
Egypt Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.01 Electricity production, kWh/capita......................99 ..... 927.9
5th pillar: Skills
9.04 Knowledge-intensive jobs, % workforce...........102 ....... 11.9
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 107.....3.8
C. Usage subindex............................................................ 93.....3.4
El Salvador
El Salvador Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.02 Laws relating to ICTs* ..........................................3 ......... 5.8
3.05 Accessibility of digital content*.............................6 ......... 6.4
5th pillar: Skills
6.07 Use of virtual social networks* .............................8 ......... 6.4
9.01 Impact of ICTs on new services & products*........9 ......... 5.5
9.03 Impact of ICTs on new organizational models*.....3 ......... 5.5
10.01 Impact of ICTs on access to basic services* ........7 ......... 5.8
10.02 Internet access in schools* ..................................3 ......... 6.5
10.03 ICT use & gov’t efficiency* ...................................5 ......... 5.6
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 25.....5.8
C. Usage subindex............................................................ 22.....5.2
10th pillar: Social impacts ................................................. 4.....5.9
Estonia
Estonia High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 140.....2.3
C. Usage subindex.......................................................... 130.....2.7
Ethiopia
Ethiopia Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.01 Effectiveness of law-making bodies* ....................2 ......... 5.7
1.04 Efficiency of legal system in settling disputes*......2 ......... 6.1
1.05 Efficiency of legal system in challenging regs*......1 ......... 5.9
1.06 Intellectual property protection*............................1 ......... 6.2
2.01 Availability of latest technologies*.........................1 ......... 6.5
5th pillar: Skills
6.01 Mobile phone subscriptions/100 pop...................8 ..... 172.3
6.06 Mobile broadband subscriptions/100 pop............3 ..... 106.6
7.01 Firm-level technology absorption* ........................7 ......... 6.0
7.02 Capacity for innovation* .......................................2 ......... 5.7
7.06 Extent of staff training*.........................................2 ......... 5.5
9.02 ICT PCT patents, applications/million pop. .........2 ..... 110.1
9.03 Impact of ICTs on new organizational models*.....1 ......... 5.7
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2014................1..6.0
Networked Readiness Index 2013 (out of 144)...................... 1.....6.0
A. Environment subindex.................................................... 3.....5.6
1st pillar: Political and regulatory environment................... 3.....5.9
5th pillar: Skills .................................................................. 1.....6.5
C. Usage subindex.............................................................. 2.....6.0
D. Impact subindex............................................................. 2.....5.9
9th pillar: Economic impacts ............................................. 1.....6.0
Finland
Finland High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 19.....5.9
C. Usage subindex............................................................ 23.....5.2
France
France High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 113.....3.7
C. Usage subindex.......................................................... 122.....2.9
Gabon
Gabon Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.03 Total tax rate, % profits ....................................148 ..... 283.2
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 124.....3.2
C. Usage subindex............................................................ 99.....3.4
Gambia, The
Gambia, The Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
4th pillar: Affordability ........................................................ 4.....6.8
5th pillar: Skills ................................................................ 78.....4.9
C. Usage subindex............................................................ 75.....3.6
Georgia
Georgia Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 12.....6.0
C. Usage subindex............................................................ 13.....5.5
Germany
Germany High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 110.....3.7
C. Usage subindex.......................................................... 100.....3.3
Ghana
Ghana Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 53.....5.3
C. Usage subindex............................................................ 67.....3.8
Greece
Greece High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 122.....3.3
C. Usage subindex............................................................ 96.....3.4
Guatemala
Guatemala Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
4.02 Fixed broadband Internet tariffs, PPP $/month 145 2,049.86
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 142.....2.2
C. Usage subindex.......................................................... 144.....2.4
Guinea
Guinea Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 60.....5.2
C. Usage subindex.......................................................... 102.....3.3
Guyana
Guyana Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.07 Tertiary education gross enrollment rate, %.......n/a ......... n/a
3.02 Mobile network coverage, % pop. ....................n/a ......... n/a
5th pillar: Skills
5.03 Secondary education gross enrollment rate, %.n/a ......... n/a
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 143.....2.1
C. Usage subindex.......................................................... 145.....2.3
Haiti
Haiti Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 112.....3.7
C. Usage subindex.......................................................... 117.....3.0
Honduras
Honduras Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.04 Efficiency of legal system in settling disputes*......3 ......... 5.8
2.02 Venture capital availability* ...................................1 ......... 4.6
3.03 Int’l Internet bandwidth, kb/s per user..................2 .. 1,426.6
5th pillar: Skills
7.03 PCT patents, applications/million pop. .............n/a ......... n/a
8.02 Government Online Service Index, 0–1 (best)....n/a ......... n/a
9.02 ICT PCT patents, applications/million pop. ......n/a ......... n/a
10.04 E-Participation Index, 0–1 (best)........................n/a ......... n/a
Rank Value
(out of 148) (1–7)
Networked Readiness Index 2014................8..5.6
A. Environment subindex.................................................... 4.....5.6
5th pillar: Skills ................................................................ 10.....6.1
C. Usage subindex............................................................ 15.....5.4
Hong Kong SAR
Hong Kong SAR High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 41.....5.5
C. Usage subindex............................................................ 45.....4.2
Hungary
Hungary High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.01 Availability of latest technologies*.........................7 ......... 6.4
3.03 Int’l Internet bandwidth, kb/s per user..................5 ..... 319.0
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 13.....6.0
C. Usage subindex............................................................ 24.....5.2
Iceland
Iceland High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 101.....4.0
C. Usage subindex............................................................ 91.....3.4
India
India Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 61.....5.2
C. Usage subindex............................................................ 69.....3.7
Indonesia
Indonesia Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 85.....4.7
C. Usage subindex.......................................................... 113.....3.0
Iran, Islamic Rep.
Iran, Islamic Rep. Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .................................................................. 9.....6.1
C. Usage subindex............................................................ 29.....4.9
Ireland
Ireland High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.09 Gov’t procurement of advanced tech* .................9 ......... 4.5
4.02 Fixed broadband Internet tariffs, PPP $/month ....2 ....... 8.39
5th pillar: Skills
9.02 ICT PCT patents, applications/million pop. .........5 ....... 79.4
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 39.....5.5
C. Usage subindex............................................................ 14.....5.5
Israel
Israel High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 43.....5.5
C. Usage subindex............................................................ 51.....4.1
Italy
Italy High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 87.....4.7
C. Usage subindex............................................................ 94.....3.4
Jamaica
Jamaica Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
9.02 ICT PCT patents, applications/million pop. .........1 ..... 118.9
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 29.....5.7
C. Usage subindex.............................................................. 9.....5.7
Japan
Japan High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 38.....5.5
C. Usage subindex............................................................ 59.....4.0
Jordan
Jordan Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 51.....5.3
C. Usage subindex............................................................ 38.....4.4
Kazakhstan
Kazakhstan Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 98.....4.3
C. Usage subindex............................................................ 86.....3.5
Kenya
Kenya Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.07 Tertiary education gross enrollment rate, %..........1 ..... 100.8
3.01 Electricity production, kWh/capita......................13 10,567.3
5th pillar: Skills
9.02 ICT PCT patents, applications/million pop. .........4 ....... 87.8
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 31.....5.7
C. Usage subindex.............................................................. 3.....5.9
Korea, Rep.
Korea, Rep. High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
6.06 Mobile broadband subscriptions/100 pop.........n/a ......... n/a
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 70.....5.0
C. Usage subindex............................................................ 58.....4.0
Kuwait
Kuwait High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 89.....4.7
C. Usage subindex.......................................................... 126.....2.8
Kyrgyz Republic
Kyrgyz Republic Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 118.....3.5
C. Usage subindex.......................................................... 112.....3.1
Lao PDR
Lao PDR Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 35.....5.6
C. Usage subindex............................................................ 42.....4.3
Latvia
Latvia Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 45.....5.4
C. Usage subindex............................................................ 90.....3.4
Lebanon
Lebanon Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 103.....4.0
C. Usage subindex.......................................................... 138.....2.6
Lesotho
Lesotho Lower-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
4.02 Fixed broadband Internet tariffs, PPP $/month .n/a ......... n/a
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 131.....2.8
C. Usage subindex.......................................................... 132.....2.7
Liberia
Liberia Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
4.01 Prepaid mobile cellular tariffs, PPP $/min..........n/a ......... n/a
5th pillar: Skills
6.06 Mobile broadband subscriptions/100 pop.........n/a ......... n/a
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 96.....4.4
C. Usage subindex.......................................................... 139.....2.6
Libya
Libya Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 23.....5.8
C. Usage subindex............................................................ 34.....4.5
Lithuania
Lithuania Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
1.01 Effectiveness of law-making bodies* ....................7 ......... 5.4
1.06 Intellectual property protection*............................6 ......... 5.9
3.03 Int’l Internet bandwidth, kb/s per user..................1 .. 4,088.5
5th pillar: Skills
10.01 Impact of ICTs on access to basic services* ........4 ......... 5.9
Rank Value
(out of 148) (1–7)
1st pillar: Political and regulatory environment................... 4.....5.7
5th pillar: Skills ................................................................ 27.....5.7
C. Usage subindex.............................................................. 8.....5.7
Luxembourg
Luxembourg High-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.03 Total tax rate, % profits ........................................1 ......... 8.2
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 68.....5.1
C. Usage subindex............................................................ 56.....4.0
Macedonia, FYR
Macedonia, FYR Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 126.....2.9
C. Usage subindex.......................................................... 129.....2.7
Madagascar
Madagascar Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 125.....3.0
C. Usage subindex.......................................................... 136.....2.6
Malawi
Malawi Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
2.09 Gov’t procurement of advanced tech* .................4 ......... 4.8
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills ................................................................ 67.....5.1
C. Usage subindex............................................................ 30.....4.8
Malaysia
Malaysia Upper-middle-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
5th pillar: Skills
Rank Value
(out of 148) (1–7)
5th pillar: Skills .............................................................. 139.....2.4
C. Usage subindex.......................................................... 111.....3.1
Mali
Mali Low-income group average

1. Political and
6. Individual usage
usage
usage
digital content
9. Economic
impacts
environment
10. Social
impacts
1
2
3
4
5
6
7
3.03 Int’l Internet bandwidth, kb/s per user..................3 ..... 625.8
5th pillar: Skills

Wef global informationtechnology_report_2014

Wef global informationtechnology_report_2014

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Wef global informationtechnology_report_2014