"SMEs in data-driven era: the role of data to firm performance"

Ioannis KopanakisIoannis Kopanakis
Konstantinos VassakisKonstantinos Vassakis
George MastorakisGeorge Mastorakis
Department of Business AdministrationDepartment of Business Administration
Technological Educational Institute of CreteTechnological Educational Institute of Crete
Agios Nikolaos, Crete, GreeceAgios Nikolaos, Crete, Greece
SMEs in Data-driven era: the roleSMEs in Data-driven era: the role
of data to firm performanceof data to firm performance
9th
ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ)
CONFERENCE
“Innovation, Entrepreneurship and Digital Ecosystems”

Introduction
Knowledge- Innovation - Performance
2
• The changing customer needs, technological evolutions
and the globalized competitive market pressure have
transformed the socio-economic environment of
enterprises.
• Enterprises have to adopt strategies innovation-oriented
in order to build and sustain competitive advantage in the
globalized “knowledge – based” economies they operate.
• In that extremely changing era, innovation depends on
the combination of technologies and exploitation of
knowledge is a vital determinant of enterprises’ success
(Daud, 2012).
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CONFERENCE

Data to knowledge
3
• In the era of Industry 4.0, Data has major impact on businesses,
since the revolution of networks, platforms, people and digital
technology changed the determinants of firms’ innovation and
competitiveness (OECD, 2015).
• The capacity of enterprises to access information and create
valuable knowledge provides them competitive advantage
against rivals in the innovation race (Sarvan et al., 2011).
Source: https://goo.gl/6ciVPo
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Data – Driven Innovation (DDI)
Knowledge to Innovation
4
• “Data-Driven Innovation” (DDI) -> techniques and
technologies for processing and analysing “big data” as the
method to innovate using data-based decision process.
• DDI’s economic value expecting to be enormous in the
following years and it has the capacity to introduce:
new improved products & services,new improved products & services,
new improved production processesnew improved production processes
better organizational managementbetter organizational management
more efficient R&Dmore efficient R&D
better supply chain managementbetter supply chain management
more efficient marketingmore efficient marketing
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Big Data
5
• “Big data” -> datasets with large volume that cannot be
captured, stored, managed and analysed by typical
database software tools (Manyika et al., 2011)
• Knowledge arising from the information given from data
analysis processes -> major resource for enterprises to
obtain new knowledge, present added value, foster new
products, processes and markets. Hence, the ability to
manage, analyse and act on data is significant to
enterprises.
• An asset for enterprises’ indicating the significance of
data-driven approach within enterprises (Microsoft
Europe, 2016)
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Characteristics of Big Data
The 4Vs of Big data
6
1. Volume: the sizes of data that are
extremely huge- measured in
exabytes
2. Variety: heterogeneity of data types,
3. Velocity: ratio of data generation and
the speed needed for their analysis.
4. Veracity: data uncertainty and the
level of reliability correlated with
some type of data
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CONFERENCE

Significance of Data
7
• Through DDI, almost all the sectors are more service-centred,
adopting the term “servicification” (Lodefalk, 2013)
• The exploitation of big data provides enterprises in several
industry sectors, not only ICT firms (Tambe, 2014) -> added
value through the improvement in resources (physical &
human) supervision and allocation, reduction of waste,
greater transparency and facilitation of new insights.
• Εconomic benefits of big data in UK private and public sector
businesses: increase from £25.1 billion in 2011 to £216 billion
in 2017, while data-driven innovationdata-driven innovation will lead to £24,1 billion
contribution to UK economy during 2012-2017 (Cebr, 2012) .
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CONFERENCE

Contribution of study
8
• Big data, similar to IT, has the ability to bring significant cost
reductions, delivery time, enhanced R&D and
new/improved products or services.
• However, little evidence exists on ROI for big data
applications in enterprises, showing promising issues
(Davenport & Dyché, 2013)
• Empirical evidence related to the impact of data-driven
approach and its impact to enterprises performance are
scarce and limited mainly in research for large-multinational
companies
• In addition, no research exists in the impact of big data-
driven approach in innovation performance of SMEs
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Significance of study
9
• It is observed growing attention to big data and data-driven
approach from academics and professionals, since the
analysis of “big data” leads on valuable knowledge,
promotion of innovative activity transforming economy of
countries (OECD, 2015).
• There is evidence that data-driven approach has a positive
impact in enterprises’ performance (Brynjolfsson, 2011;
Davenport & Harris, 2007; Lavalle, 2010; Bakhshi et al.,
2014).
• The scope of this study is to examine the impact of data
exploitation to SMEs innovation performance.
9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
Innovation, Entrepreneurship and Digital Ecosystems
9th
CONFERENCE

Data – driven decisions
10
 2 phases of Processes leveraging big data:
 Data ManagementData Management
 Data AnalysisData Analysis
(Gandomi & Haider, 2014)
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Authors Data set Scope Results
Brynjolfsson et al. (2011) 179 large publicly traded
firms
Adoption of data-driven
decision making approach
provide in firm
performance
-5-6% higher productivity
- better performance in
asset utilisation, ROE and
market value
McAfee & Brynjolfsson
(2012)
330 North American
companies
the performance of data-
driven companies and
whether big data
intelligently improves
business performance
5% higher production and
6% higher profitability
than their rivals
Bakhshi & Mateos-Garcia
(2012)
500 UK businesses from
various industries
operating on-line
2 categories: data-driven
decision making
enterprises and
experience-driven
enterprises
Data-driven companies
present higher level of
innovativeness launching
new products and
services and making
disruptive changes to
their business processes
Data-driven performance
Impact in enterprises’ performance
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Case Study: Crete, Greece
Enter your footer text here12
Why Crete?
•Crete is a Moderate Innovator with relative strengths compared to EU28:
Non-R&D innovation exp., Public R&D exp., SMEs marketing or organizational
innovations (Regional Innovation Scoreboard, 2016)
• 1st
in R&D expenditures as a percentage of its GDP /1,35% as National is
0,8% (National Documentation Center, 2015)
Data / Methodology:
•80 Small and Medium Enterprises (SMEs)
•Personal interviews with C-suite
•Period covered: 2011-2014
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Data sample
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Empirical Results

Coefficients
Term Value StdErr t-value p-value
Data
exploitation
0,453398 0,0893987 5,07164 0,0006704
intercept 0,270974 0,302566 0,895588 0,093795
R squared 0,740795
Equation:
Turnover increase from
innovation = 0,453398*Data
exploitation + 0,270974
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T
u
r
n
o
v
e
r
I
n
c
r
e
a
s
e
Data – driven decision makingLow Very High

Findings
16
• Data exploitation by SMEs contributes to their
innovation performance and to their performance as a
whole.
• SMEs with data driven approach in their decision
making are found to be more innovative and present
higher performance.
• All types of SMEs with higher data-driven orientation
present to be have higher innovation performance
contributes positively to their survival and growth.
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CONFERENCE

Conclusions
17
• In the data-driven era, transformed by the rapidly
large streams of data generated through ICT and IoT,
knowledge originates from big data processes
provides the decision makers the capability to
innovate and increase their performance gaining a
competitive advantage against rivals.
• The insights by leveraging big data in innovation
prospects provide a competitive advantage in
enterprise through new ways of growth and consumer
surplus
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CONFERENCE

Further research
• SMEs in national and international level
• What happened with the ROI in data-driven processes?
• What type of innovation is more possible through data
– driven approach by SMEs?
• What are the boundaries of SMEs to adopt data-driven
approach?
• Initiatives for developing data-driven approach
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Thank you for your
attention
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CONFERENCE

"SMEs in data-driven era: the role of data to firm performance"

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