Mastering Big Data strategies for CFO's

Mastering Big Data:
CFO Strategies to Transform Insight into Opportunity
A FSN & Oracle White Paper

“Mastering Big Data” - A FSN & Oracle White Paper 2

Contents Introduction 3

What exactly is Big Data? 4

Where is the value in Big Data? 4

The role of the CFO in Big Data 7

The technical barriers to Big Data success 7

CFO strategies for Big Data success 8

Summary 9


Introduction So profound is the potential impact of Big Data that the World Economic Forum considers it to be a
new class of economic asset1, akin to human capital and natural resources such as oil and gold. And
like the Klondike gold rush that preceded it, Big Data has the capacity to transform the wealth and
competitiveness of sovereign states, companies and individuals. So it is not surprising that Big Data
has become top of mind among C-levels worldwide; according to a 2012 McKinsey Global Survey of
executives, over a third believe that Big Data – along with mobile, social, and cloud computing - will
boost operating profits by 10 percent in just three years.2

Few organizations today are well positioned to take advantage of the profit opportunities afforded by
Big Data. Through 2015, more than 85% of Fortune 500 organizations will fail to effectively exploit
Big Data for competitive advantage.3 Most organizations are being held back by fractured information
systems, poor data management, insufficient database and hardware performance, as well as limited
access to cutting-edge data discovery and visualization tools.

Clearly, whoever masters the art and science of big data will be poised to master their industries
or markets. That’s why smart CFOs are taking control of big data and business analytics projects,
not just to uncover new ways to drive growth in a slowing global economy, but also to broaden
their influence and impact on the enterprise. Already increasingly responsible for overseeing IT
investments and providing strategic insight to the board, CFOs will be increasingly called upon to take
a crucial leadership role in assessing the value of Big Data initiatives, building on their traditional
skills analyzing and reporting on data to drive decision making. The most recent Gartner study on
CFOs and technology reflects this new mandate, with business intelligence and business analytics
investments ranking first among CFO investment priorities in 2012.4

This white paper will examine why Big Data is becoming increasingly important to CFOs and finance
organizations, from its impact on specific industries and even governments, to the technical barriers
that CFOs face in capitalizing on the promise of big data. It will also examine the strategies and
solutions CFOs can put into place to overcome these obstacles and ensure that investments in big
data projects meet management expectations and deliver real value to the entire organization.

Big Data: What’s All the Fuss About?
“Every two days, we create as much information as we did from the dawn of civilization up until 2003.”
- Eric Schmidt, Executive Chairman, Google

The notion of Big Data is not entirely new. After all, CFOs are accustomed to dealing with mounting
volumes of information. So why all the fuss? The volumes in most core financial applications are
large but certainly not in the realms of the terabytes, petabytes, and even zettabytes being generated
by the billions of connected devices consumers, companies, and governments use daily around the
world. Big Data takes ‘large’ to an entirely new level, not just in the amount of information available,
but in the economic opportunities that data can generate.

But there are other reasons that make the issues of Big Data different and urgent. First, the gap
between the opportunities afforded by Big Data and an organization’s capability to exploit it is
widening by the second. For example, data is expected to grow globally by 40 percent per year but
growth in IT spending is languishing at just 5 percent5.

Second, businesses are being ravaged simultaneously by the twin challenges of rampant economic,
regulatory and market change and unprecedented volatility - all happening at near ‘Twitter-speed’.
As a result, there is intense interest in technologies and techniques that can provide an edge and
shine a light on market trends quickly ahead of competitors.

Third, stirred by Big Data successes reported in the retail, healthcare and financial services sectors,
amongst others, some market observers consider we are at the point of inflection, i.e. that Big Data
really is the catalyst for entirely new growth opportunities, products and services in the private sector,
not to mention cost savings and more effective resource allocation for government organizations.


What exactly is Big Data is typically characterized by the so-called, three “Vs”: volume, velocity and variety. And when
Big Data? it comes to volume, the statistics bandied around by Big Data cognoscenti are truly breathtaking.

For example, 15 out of 17 industry sectors in the United States will have more data stored per
company than the U.S. Library of Congress, which itself collected 235 terabytes of data in April
2011.5 Wal-Mart Stores Inc. handles more than 1 million customer transactions every hour, feeding
databases estimated at more than 2.5 petabytes, or the equivalent of 167 times the books in the
Library of Congress6. Thirty billion pieces of content are shared on Facebook, monthly5. Finally, Intel
estimates that there will be 15 billion devices connected to the internet by 2015. Ironically, in many
parts of the world, more people have access to a mobile device than to a toilet or running water7.

But what is driving the explosive growth in data volume when the world’s economy is hardly growing
at all? The explanation lies not in an increase in transaction volumes but a broadening of data sets,
i.e. “Variety” (collecting more analysis about current data) plus the collection of entirely novel types
of data. For example, in the accounting arena Solvency II requires insurers to hold information
about counterparties and IFRS demands more segmental analysis. Furthermore, environmental and
sustainability reporting has forced some organizations to collect entirely new information, such as
electricity meter readings and CO2 emissions. Hence organizations are grappling with variety as well
as volume. Adding to the mix is the explosive growth of ‘unstructured’ data such as commentary and
other text-based information in social media, tweets, emails, blogs and websites. The proliferation
of mobile devices and embedded GPS positioning (location information) has added yet another new
dimension, exacerbating the growth of Big Data as organizations and individuals find themselves able
to interact with each other as well as corporate systems anytime and anywhere.

But what of velocity? Uncertain times create an insatiable appetite for information. Nervous
regulators want to see information more frequently and management teams want to re-forecast
more often and make near real-time decisions. So the speed with which information is ‘pushed’ by
consumers, ‘pulled’ (demanded) by management, delivered and consumed is accelerating.

Where is the value Less often discussed is the unofficial fourth “V” of Big Data – the ‘Value’ of information since it is
the value of the information that should drive the investment in Big Data rather than the collection
in Big Data?
of it, for its own sake. In fact, many market commentators caution that the unfettered pursuit of Big
Data will lead to difficulties in data collection, data transformation, data storage, and data analysis,
potentially undermining established processes such as performance management which may not be
able to cope with the manipulation of such large and unwieldy volumes.

Estimates of the value of Big Data are almost as impressive as its underlying growth. For example, it
is suggested that the US health care sector alone could save $300 billion per year – more than double
the total annual health care spending in Spain5. The same report points to the potential prize of a 60%
improvement in operating margins in retailers who can leverage Big Data effectively. Other research
is equally bullish and consistent.8 93% of companies believe that the opportunity cost of not exploiting
Big Data is on average 14% annually across industry sectors with Life Sciences conceding that the
value could be as high as 20%. But where exactly do the best opportunities lie?

The first thing to note is that Big Data is not a level playing field. While market opportunities for Big
Data are generally large, some industries are inherently data-rich and some companies are, by their
very nature, data-advantaged. Diversified retailers, for example, collect massive amounts of point
of sale information in the normal course of operations, offering them unique insights into customer
behavior, product preferences, sales trends and other market intelligence that can be used to
outsmart the competition.

It is also worth highlighting that Big Data is not a one-way street. For example, if the use of Big
Data leads to greater customer satisfaction, (on-time delivery, better quality, lower prices and novel
services) then there is a direct benefit or ‘economic-surplus’ to the customer in addition to better
margins for the vendor. In fact the estimated5 annual consumer surplus from better use of personal
location data (from mobile phones) is a mouth-watering $600 billion globally.

Putting these tantalizing estimates to one side for the moment the benefits from Big Data are
generally attributable to four principle themes, namely:


• More granular information or micro-segmentation, i.e. the ability to analyze massive amounts of data
and, for example, offer more personalized products and services to customers, or more targeted
treatment to patients, or better re-skilling and development options for unemployed citizens.

• Speed of synthesis, i.e. the ability to deliver very fast, almost real-time information from masses of
data, to accelerate management decision making or perhaps provide offers to consumers based on
real-time location data.

• Data combinations, i.e. the capability to straddle multiple data-sets (structured and unstructured
information) to provide new insights, for example, long-term weather patterns and sales of apparel.

• Automation, i.e. highly sophisticated algorithms performed at lightning speed which can, for
example, initiate the adjustment of goods displayed on a supermarket shelf according to estimated
demand for products based on social media feedback on a current advertising campaign.

Although these principles (alone or in combination) have broad applicability, one clear message
arising from early experiences in harnessing Big Data is that the opportunities and benefits are
highly sector specific. And contrary to popular opinion all functional areas of an organization stand to
benefit from Big Data, not just more obvious areas such as marketing and sales. Following are three
examples of how big data is impacting specific sectors of the global economy.

Retail Sector
It is widely acknowledged that the retail sector is one of the most sophisticated and experienced users
of data. The modern retailer is a data-advantaged organization because of its ability to draw on a
wide range of data sources, gleaned from a variety of customer channels (in-store, on-line, catalog,
financial services) and, as such, is at the lower end of lost revenue opportunities – just 10 percent8.

But curiously, retail is both a major beneficiary and a victim of consumer surplus in Big Data. On the
one hand the potential for retail margin improvement is immense, (marketing levers alone could
affect 10% to 40% of operating margin and supply chain levers could have a 5% to 35% impact5. But on
the other hand margins are being squeezed by applications such as RedLaser, a free shopping app for
iPhone, Windows Phones, and Android that provides instant price comparisons between competing
retailers via product barcodes and has been downloaded over 19 million times9. So how are retailers
using Big Data to respond to the challenge?

There are numerous Big Data responses in the retailers’ kit bag. Popular approaches across the
sector include, cross-selling, location-based marketing, in-store behavioral analysis, micro-
segmentation (personalization of offers) and better supply chain management. For example,
the ability to synthesize masses of data about customer preferences, buying history and in-store
behavior (through shopping cart sensors) allows the retailer to keenly manage price negotiations
with suppliers. Similar data allows them target offers with enormous precision, for example, on-line
recommendation engines that say, “people that bought this product also bought…” are estimated
to have delivered 30 percent of Amazon’s sales5. Underscoring the power of micro-segmentation
Amazon CEO Jeff Bezos once said, “If we have 66 million different customers, we want to have 66
million different stores.”10

The retail sector is interesting for two additional reasons. First, it illustrates that Big Data
opportunities reside across all functional areas, including marketing, merchandising, operations, and
the supply chain. Second, retailers must worry that not having a viable Big Data response can put
business at a serious competitive disadvantage.

Healthcare sector
The healthcare sector is at the other end of the spectrum. Historically, perhaps because of immense
complexity, it has not made such aggressive use of Big Data and therefore the opportunities for
revenue growth at 15% are above average.8 Nevertheless 100% of the sector is collecting more
business information today than two years ago and volumes have grown 85% in the last two years.8
And there have been some notable successes.

The Department of Veterans Affairs (VA) in the United States has demonstrated several healthcare
information technology and remote patient monitoring programs. Through its Big Data initiatives the
VA has reduced gender inequalities in 12 out of 14 Healthcare Effectiveness Data and Information Set
(HEDIS) measures since 2008. The VA also consistently scores higher than private sector health care
on both gender-specific and gender-neutral HEDIS measures11.


In the U.K., the National Institute for Health and Clinical Excellence, a key part of the NHS (National
Health Service), uses Big Data to assess, amongst other matters, the cost-effectiveness (value for
money) of medicines. It also uses Big Data to ensure patient access to appropriate treatments
regardless of where they live in the UK.

Sector initiatives around remote patient monitoring offer one of the most exciting uses of Big Data,
both to improve patient health and save money for healthcare providers and insurers in the U.S., it is
estimated that 150 million patients in 2010 were chronically ill with diseases such as diabetes, and
accounted for 80% of health system costs that year5. But a whole variety of remote data collection
devices that can record, blood sugar levels, or heart performance, for example and feed it back in
real-time to clinicians, can promote early intervention and prevent hospitalization with massive
financial savings in healthcare.

“Chip-on-a-Pill” technology, which can provide data on how and when medicines are ingested,
illustrates how some sectors are using new data sources to create completely new service and
product possibilities.

Financial Services
This sector’s use of data is in the middle of the range, with an estimated 12% of revenue lost through the
under-utilization of Big Data8. But like retail, it is also one of the sectors experiencing tension between
its own capacity to leverage data and price transparency in the hands of the consumer.

As a result, the traditional role of the financial services intermediary (broker) is changing. Price
comparison web sites give consumers a near real-time perspective of financial savings, loans and
insurance products. In fact, brokers in all industries whose strength was bound up in access to
privileged information are now under pressure in the face of consumers armed with access to Big Data.

Big Data does provide plenty of opportunity for gaining market share through better cross-selling
and up-selling, tighter financial management, and more comprehensive regulatory compliance8.
The ability to automatically sense key controls across millions of transactions through Continuous
Controls Monitoring (CCM) helps businesses comply with regulation. Auditors can also draw
greater reliance from systems which monitor the entire transaction universe, rather than relying on
traditional statistical sampling techniques.

Big Data projects are spawning new products and services as well in the financial services sector.
Remote data capture and location-specific information is being used in imaginative car insurance
products in which the insurer is able to monitor car usage (speed, time of day) remotely - so called
“Telematics” and adjust premiums accordingly.12

For example, Co-operative Insurance in the U.K. analyzed the driving habits of 10,000 telematics
insurance customers aged 17 to 25 across the UK, and found that they were 20% less likely to have a
crash than those with standard insurance. Telematics customers have less serious accidents, with a
typical claim 30% less than ordinary customers.12

The Co-operative Insurance case study is a good example of economic surplus to the consumer
(personalized and affordable premiums), and lower administrative costs and competitive advantage to
the insurer.

The role of the CFO While business information has always been considered a key corporate asset, Big Data is likely to
in Big Data set a new benchmark in that area because of the breadth and depth of management competencies
and skills needed to exploit fully its potential. There is already a massive identified shortfall in “deep
analytical skills” and “data-savvy managers”, which is why organizations are likely to draw heavily on
the capabilities of the finance function.5

Classifying Big Data as a new category of asset lends further credence to the idea that the finance
function is the natural ‘port of call’ for ensuring that Big Data initiatives are strategically aligned
and have a demonstrable ROI (Return on Investment). Furthermore, the need to analyze and model
business opportunities, define leading edge applications, and interpret results sits well with the
capabilities of many modern finance functions.


From an organizational perspective, the demands of Big Data also fit well with the growing oversight
role CFOs are assuming over IT. According to the latest research by Gartner and Financial Executives
International, the number of IT organizations reporting into the CFO’s office in 2012 is at a record high,
as CFOs look to maximize the value of IT investments in areas like Big Data.

But it won’t all be ‘smooth sailing’. Modern finance functions are lean operations and CFOs do not
have a surfeit of resources at their disposal. They are constantly balancing the needs of the business
for strategic insight and decision support with the traditional need for ‘back office’ transaction,
accounting, and compliance support. This represents a formidable challenge, especially at a time
when organizations already complain of information ‘overload’ and overbearing business complexity.

Big Data will also take most organizations well out of their comfort zone. To date, an organization’s
experience of data requirements has largely been closely associated with established business
processes; for example, the data needed to run a ‘purchase to pay’ cycle or to fulfill a statutory
reporting requirement. But Big Data initiatives will increasingly rely on specially developed industry
applications and novel uses of unfamiliar structured and unstructured data, such as location
specific data and social media feeds. So, traditional methodologies around information management
design are likely to be stretched to breaking point. Also, the disruption of existing business models
caused by the significance and speed of Big Data initiatives suggests that this will give rise to new
partnerships and interactions across global enterprises, which will in turn have new tax, risk and
regulatory implications6. Factors such as these, combined with the constant need to re-prioritize
between IT investments, suggest a prominent role for the CFO in helping to create and manage the
value of Big Data initiatives inside the enterprise.

The technical Just like the Klondike gold rush, the earth does not give up its riches easily. Extracting economic
value from Big Data presents formidable challenges for even the most data-advantaged and IT-savvy
barriers to Big
organizations. The potential risks and rewards also vary considerably by industry sector. This time
Data success
around, the most sought after resources will not be picks and shovels (although raw computing power
and software discovery tools will have a critical role) but skills and knowledge. A shortage of 140,000
to 190,000 deep analytical positions has already been identified, and 1.5 million more data-savvy
managers will be needed to take full advantage of big data in the United States alone5.

Despite a record of success and innovation in most sectors, the ability to launch successful Big
Data initiatives is hampered by many of the same factors that have generally beset information
management over the last three decades. Setting aside organizational factors such as appropriate
project sponsorship, skills and governance, the principle difficulties relate to much more prosaic
considerations, such as:
• the difficulty of data capture from distributed systems architectures
• the ability to leverage diverse source systems
• a lack of systems integration
• the prevalence of multi-vendor platforms
• the poor management of metadata

All of this needs to be set against a period of increasing business complexity, modest IT budgets, and
the competing demands of other technology-driven opportunities such as cloud, mobile, and social
computing, and further advances in e-commerce. Some businesses are struggling to standardize on
platforms and streamline core processes to capitalize on these new advances. For example, 93% of
executives believe their organization is losing revenue as a result of not being able to fully leverage
the information they collect8.

Big Data introduces its own set of technical challenges, such as the need to collect and store
unprecedented volumes of data: the requirement to assimilate a variety of new structured and
unstructured data sources: and, the need to process and analyze it that data rapidly. And as
organizations store more personal information, they bump up against more onerous obligations
around data privacy and security.


In many organizations, existing database technologies, information retrieval, and discovery and
visualization tools are not sufficiently powerful or scalable to cope with the demand of Big Data.
Overall, 60% of executives rate their companies unprepared for a “data deluge”, although their state
of preparedness varies significantly between sectors8. Furthermore, as seen in the earlier examples,
Big Data projects are more likely to leverage industry-specific data and applications which may need
to be developed if they are not available ‘off the shelf’.

CFO strategies for First and foremost, CFOs must be able to provide guidance on the problem they want Big Data to
solve, whether it’s trying to speed up existing processes (like fraud detection), or identifying new
Big Data success
products or services from insights gleaned from customer sentiment data. If the goal of a Big Data
effort cannot be clearly defined, then the initiative shouldn’t be pursued.

Second, it is vital to ensure that the organization has the right foundation in place to treat data as a
corporate asset. Talent is critical to this equation: finding data scientists with the business experience
or insights needed to frame the right hypotheses, validate them, and then apply those findings to
address specific business challenges or opportunities, will be key to success.

Technology is also critical to creating the right infrastructure to
capitalize on Big Data. A recent Oracle survey on Big Data “Before you build massive
business challenges discovered that 97% of executives pieces of technology to
acknowledge that their organization needs to make changes to
capture, store, and report
improve information optimization over the next two years8. The
on big data, you need to
first step here is to create strong data governance, to deal with
be clear on the business
the sheer volume of structured and unstructured data that must
be combined and analyzed from a wide variety of sources. purpose. Is it for internal
Strong data governance is essential to having confidence in the efficiency? For new
data, and this prerequisite really drives a lot of process and products? Or is it to
organizational design and definition, including ownership, underscore the efficacy of
validation, change management, retention policies and so on. existing products? That’s
where the involvement of
There is a definite need for standardization of architectures and the CFO and the senior
technology. Certainly the underlying hardware platforms and management team is
their ability to meet the performance goals are important, but
really critical.”
businesses should also focus on the data sources (whether they
are internal or external sources), the data and application Dallas Clement, EVP and
integration components, and, of course, the analytics CFO, AutoTrader Group
components (the tools the business and operational resources
will use to make decisions). Increasingly, there is an opportunity
to automate some of the analysis and decision making via event processing and real-time decision
tools, such as those provided by Oracle.

For example, Oracle Endeca Information Discovery is an enterprise data discovery platform for
advanced exploration and analysis of complex and varied data. One of its notable strengths is that it
enables an interactive “model-as-you-go” approach which frees IT from the burdens of traditional
data modeling and supports the broad exploration and analysis needs of business users. Its ability to
explore and analyze structured, semi-structured and unstructured data is a major advantage in a Big
Data setting.

But there is no getting away from the fact that Big Data, also requires industrial strength hardware and
systems software solutions to process, churn and synthesize massive quantities of data. Oracle offers
a choice of products for organizing Big Data including, the ‘Oracle Big Data Appliance’ for managing
and transforming the data: ‘Oracle Exadata Database Machine’, which provides extreme performance
for both data warehousing and OLTP applications; and, the ‘Oracle Exalytics In-Memory Machine’, for
enabling the rapid performance of Business Intelligence and Performance Management applications
working with extreme levels of data.

The Oracle Big Data Appliance is a carefully engineered combination of hardware and software
designed to simplify implementation and management of Big Data projects. It can handle massive
volumes of data in batch and in parallel—filtering, transforming and sorting it before loading it into
an enterprise data warehouse. Oracle Big Data Appliance is a formidable workhorse, comprising a


powerful battery of 18 Oracle Sun servers with a total of 864 GB main memory; 216 CPU cores; 648 TB
of raw disk storage; 40 Gb/s InfiniBand connectivity between nodes; and,10 Gb/s Ethernet data center
connectivity.

The Oracle ‘Exadata Database Machine’ is also an engineered combination of hardware and software
designed to provide extreme performance for both data warehousing and OLTP applications, making
it the ideal platform for consolidating on private clouds. It is a complete package of servers, storage,
networking, and software that is massively scalable, secure, and redundant. With Oracle Exadata,
customers can reduce IT costs through consolidation, store up to ten times more data, and improve
performance of all applications, enabling faster business decisions.

Oracle Exalytics In-Memory Machine is engineered to handle analytics and calculations ‘on the fly’
and to drive the rapid performance needed by large-scale Business Intelligence and Enterprise
Performance Management applications. It features powerful compute capacity, abundant memory
and fast networking options optimally configured for in-memory analytics of business intelligence
workloads.

Summary For the companies that grasp the opportunity, the Big Data phenomenon holds out the possibility
of transforming organizational competitiveness and disrupting established markets in a way that
arguably has not been seen since the “dot com” days.

In particular, the ability to combine existing and novel forms of industry-specific data - both structured
and unstructured - and render it quickly in a way that can be readily synthesized, opens up not only the
possibility of near real-time decision making but also compelling new products and services.

And across all sectors there is no shortage of data with which to amplify an organization’s
understanding of its business, and the stakeholders with which it interacts. New data, gleaned
from point-of-sale systems, multiple e-commerce channels, mobile computing, social media, and
advanced communications can offer instant information about the behavior, whereabouts and desires
of retail customers: the health and condition of hospital patients; and the driving habits of young car
owners. With micro-segmented information like this, enlightened retailers, healthcare professionals,
and insurers are already offering and developing highly specialized services tailored exactly to the
needs of their customers and patients.

Despite many notable successes, few organizations are well positioned to take advantage of the
opportunities afforded by Big Data. Through 2015, more than 85% of Fortune 500 organizations will fail
to effectively exploit Big Data for competitive advantage3. Most organizations are being held back by
fractured information systems, poor data management, insufficient database and hardware performance,
limited access to suitable data discovery and visualization tools. Fortunately, innovative technologies such
as Oracle Endeca Information Discovery, Oracle Big Data Appliance, Exadata, and Exalytics machines are
rapidly coming on stream to manage the demands of the Big Data era.

Of more concern is the global shortage of deep analytical skills and, although not articulated in
studies so far, perhaps the visionary skills necessary to tease out Big Data opportunities. The office of
the CFO is uniquely placed to assist in the quest to turn Big Data to competitive advantage. Collecting,
synthesizing, and interpreting data is second nature to the finance function, as is strategic planning
and business modeling. Over recent years, the CFO has also played an increasingly influential role in
strategy setting and deciding priorities for IT spending and delivery.

Big Data presents unique opportunities for growth and competitive advantage for those organizations
that marshal the skills, set appropriate priorities and seize the moment. With Big Data doubling every
two years, those organizations that fail to capitalize on the new information paradigm could find it
impossible to catch up.


Bibliography Note1 Personal Data: The Emergence of a New Asset Class; An Initiative of the World Economic Forum January 2011 in Collaboration
with Bain & Company, Inc.
Note2 McKinsey & Company Global Survey, “Minding Your Digital Business”, 2012
Note3 “Technology Mega-Trends the CFO Must Know”, Carol Ko, CFO Innovation Asia, August 2012
Note4 Van Decker, John E, “Top 10 Findings From Gartner’s Financial Executives International CFO Technology Study”, Gartner Research
Report G00234215, May 16,2012
Note5 Big Data: The next frontier for innovation, competition and productivity, McKinsey Global Institute, June 2011
Note6 “Data + Analytics = Opportunity”, Financial Executive July/August 2012
Note7 Time magazine, The Wireless Issue, August 2012
Note8 From Overload to Impact: An Industry Scorecard on Big Data Business Challenges; Oracle July 17th 2012
Note9 Redlaser web site content as at 08/30/2012
Note10 “Click here for the Upsell”; CNN Money; Erick Schonfeld, July 2007
Note11 VA Press Release: VA Continues to Reduce Gender Disparities in Health Care, August 28, 2012
Note12 “Car insurance: satellite boxes make young drivers safer”, Jill Insley, The Guardian, 5th April 2012

(1) Video: CFO Insights interview with Frank Friedman, Deloitte CFO http://www.oracle.com/us/c-central/cfo-executive-insights/index.html
Other useful
resources (2) Video: “Tectonic Shifts Shaping the Next Wave of Business Computing”, Intel presentation, Oracle CFO Summit, April 2012, Gordon
Graylish, Vice President and General Manager, Enterprise Solutions Group, Intel.
http://www.oracle.com/us/c-central/events/cfo-summit-highlights/business-computing-1636834.html

(3) Video: The Impact of Technology on Business Insight and Competitive Advantage Barry Libenson, CIO of agricultural co-op Land
O’Lakes, discusses IT transformation with Oracle solutions to improve performance and deliver unmatched customer and partner
experiences. https://login.oracle.com/mysso/signon.jsp

FSN FSN Publishing Limited is an independent research, news and publishing organization catering for
the needs of the finance function. This white paper is written by Gary Simon, Group Publisher of
FSN and Managing Editor of FSN Newswire. He is a graduate of London University, a Fellow of the
Institute of Chartered Accountants in England and Wales and a Fellow of the British Computer Society
with more than 27 years experience of implementing management and financial reporting systems.
Formerly a partner in Deloitte for more than 16 years, he has led some of the most complex information
management assignments for global enterprises in the private and public sector.

Gary.simon@fsn.co,.uk

www.fsn.co.uk

Oracle Corporation (NASDAQ: ORCL) is the world’s largest enterprise software company. With
Oracle
the market-leading Hyperion enterprise performance management suite, world class financial
applications, and integrated governance, risk and compliance solutions Oracle helps finance
executives maximize potential and deliver results for their organizations. For more information
about Oracle’s Big Data Solutions, visit us at http://www.oracle.com/us/technologies/big-data/index.
html?sckw=srch:big-data&SC=srch:big-data

Disclaimer of Whilst every attempt has been made to ensure that the information in this document is accurate and complete some typographical
errors or technical inaccuracies may exist. This report is of a general nature and not intended to be specific to a particular set of
Warranty/Limit of circumstances. The publisher and author make no representations or warranties with respect to the accuracy or completeness of the
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No warranty may be created or extended by sales representatives, or written sales materials. The advice and strategies contained
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