Applying Data Analytics for Innovation and Sustainable Enterprise Excellence

  • Stavros SindakisEmail author
Part of the Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth book series (DIG)


There is an irreversible trend toward the criticality of big data analytics’ capability and exercise thereof so that rather than exclusive use of traditional ‘data-driven decision-making’ approaches, sustainable—organizational—excellence will often demand focus on more computationally intensive data and information generation, collection, extraction, and interpretive procedures that—when added to traditional data-driven methods—yield the area of sustainable enterprise excellence referred to as enterprise intelligence and analytics.


Innovation Strategy Business Analytic Ambidextrous Organization Explorative Innovation Sustainable Business Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Benner, M.J., and M.L. Tushman. 2003. Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review 28(2): 238–256.Google Scholar
  2. Ceri, S., G. Gottlob, and L. Tanca. 2012. Logic programming and databases. Heidelberg: Springer Science & Business Media.Google Scholar
  3. Chen, Hsinchun, Roger H.L. Chiang, and Veda C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36(4): 1165–1188.Google Scholar
  4. Galbraith, J.K. 2012. Inequality and instability: A study of the world economy just before the great crisis. New York: Oxford University Press.CrossRefGoogle Scholar
  5. Gibson, C.B., and J. Birkinshaw. 2004. The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of Management Journal 47(2): 209–226.CrossRefGoogle Scholar
  6. Gunday, G., G. Ulusoy, K. Kilic, and L. Alpkan. 2011. Effects of innovation types on firm performance. International Journal of Production Economics 133(2): 662–676.CrossRefGoogle Scholar
  7. He, Z.L., and P.K. Wong. 2004. Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science 15(4): 481–494.CrossRefGoogle Scholar
  8. Joshi, K.D., L. Chi, A. Datta, and S. Han. 2010. Changing the competitive landscape: Continuous innovation through IT-enabled knowledge capabilities. Information Systems Research 21(3): 472–495.CrossRefGoogle Scholar
  9. LaValle, S., E. Lesser, R. Shockley, M.S. Hopkins, and N. Kruschwitz. 2011. Big data, analytics and the path from insights to value. MIT Sloan Management Review 52(2): 21.Google Scholar
  10. Nickerson, J.A., and T.R. Zenger. 2004. A knowledge-based theory of the firm—The problem-solving perspective. Organization Science 15(6): 617–632.CrossRefGoogle Scholar
  11. Provost, F., and T. Fawcett. 2013. Data science and its relationship to big data and data-driven decision making. Big Data 1(1): 51–59.CrossRefGoogle Scholar
  12. Reinmoeller, P., and N. Van Baardwijk. 2005. The link between diversity and resilience. MIT Sloan Management Review 46(4): 61.Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.American University in Dubai, School of BusinessDubaiUAE

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