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Management challenges in creating value from business analytics

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  • Vidgen, Richard
  • Shaw, Sarah
  • Grant, David B.

Abstract

The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.

Suggested Citation

  • Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    • Handle: RePEc:eee:ejores:v:261:y:2017:i:2:p:626-639
    DOI: 10.1016/j.ejor.2017.02.023
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Richard Vidgen & Xiaofeng Wang, 2009. "Coevolving Systems and the Organization of Agile Software Development," Information Systems Research, INFORMS, vol. 20(3), pages 355-376, September.
    3. Ranyard, J.C. & Fildes, R. & Hu, Tun-I, 2015. "Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 1-13.
    4. Akkermans, Henk A. & Bogerd, Paul & Yucesan, Enver & van Wassenhove, Luk N., 2003. "The impact of ERP on supply chain management: Exploratory findings from a European Delphi study," European Journal of Operational Research, Elsevier, vol. 146(2), pages 284-301, April.
    5. Stephen J. W. Evans, 2016. "What Is the Plural of a ‘Yellow’ Anecdote?," Drug Safety, Springer, vol. 39(1), pages 1-3, January.
    6. Pape, Tom, 2016. "Prioritising data items for business analytics: Framework and application to human resources," European Journal of Operational Research, Elsevier, vol. 252(2), pages 687-698.
    7. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    8. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    9. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    10. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
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