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

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Ioannis Kopanakis, Konstantinos Vassakis & George Mastorakis. "Big Data in Data-driven innovation: the impact in enterprises’ performance". Presentation at 9th Annual EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
"Innovation, Entrepreneurship and Digital Ecosystems", 14-16 September 2016, Warsaw, Poland.

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

  1. 1. 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”
  2. 2. 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). 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  3. 3. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  4. 4. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  5. 5. 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) 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  6. 6. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  7. 7. 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) . 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  8. 8. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  9. 9. 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 ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  10. 10. Data – driven decisions 10  2 phases of Processes leveraging big data:  Data ManagementData Management  Data AnalysisData Analysis (Gandomi & Haider, 2014) 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  11. 11. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  12. 12. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE “Innovation, Entrepreneurship and Digital Ecosystems”
  13. 13. Data sample 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE “Innovation, Entrepreneurship and Digital Ecosystems”
  14. 14. Empirical Results Enter your footer text here14
  15. 15. 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 Enter your footer text here15 R squared 0,740795 Equation: Turnover increase from innovation = 0,453398*Data exploitation + 0,270974 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE T u r n o v e r I n c r e a s e Data – driven decision makingLow Very High
  16. 16. 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. 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  17. 17. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  18. 18. 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 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE
  19. 19. Thank you for your attention 9th ANNUAL EUROMED ACADEMY OF BUSINESS (ΕΜΑΒ) CONFERENCE

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