Abstract
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.
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References
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.
Ceri, S., G. Gottlob, and L. Tanca. 2012. Logic programming and databases. Heidelberg: Springer Science & Business Media.
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.
Galbraith, J.K. 2012. Inequality and instability: A study of the world economy just before the great crisis. New York: Oxford University Press.
Gibson, C.B., and J. Birkinshaw. 2004. The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of Management Journal 47(2): 209–226.
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.
He, Z.L., and P.K. Wong. 2004. Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science 15(4): 481–494.
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.
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.
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.
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.
Reinmoeller, P., and N. Van Baardwijk. 2005. The link between diversity and resilience. MIT Sloan Management Review 46(4): 61.
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Sindakis, S. (2017). Applying Data Analytics for Innovation and Sustainable Enterprise Excellence. In: Carayannis, E., Sindakis, S. (eds) Analytics, Innovation, and Excellence-Driven Enterprise Sustainability. Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-137-37879-8_11
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DOI: https://doi.org/10.1057/978-1-137-37879-8_11
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