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Developing a business analytics methodology: A case study in the foodbank sector

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  • Hindle, Giles A.
  • Vidgen, Richard

Abstract

The current research seeks to address the following question: how can organizations align their business analytics development projects with their business goals? To pursue this research agenda we adopt an action research framework to develop and apply a business analytics methodology (BAM). The four-stage BAM (problem situation structuring, business model mapping, analytics leverage analysis, and analytics implementation) is not a prescription. Rather, it provides a logical structure and logical precedence of activities that can be used to guide the practice of analytics (i.e., a mental model). The client for the action research project is The Trussell Trust, which is a UK charity with the mission of empowering local communities to combat poverty and exclusion. As part of the action research project the research team created the UK's first dynamic visualization tool for crises related to food poverty. The prototype uses foodbank data to map geographical demand and aligns findings to 2011 Census data to predict where additional foodbanks may be needed. Research findings are that: (1) the analytics methodology provides an umbrella for, and applies equally to, data science and Operational Research (OR); (2) that the practice of business analytics is an entangled and emergent mix of top-down analysis and bottom-up action; and, (3) that, for the third sector in particular, analytics can be usefully approached as a collective and community endeavor.

Suggested Citation

  • Hindle, Giles A. & Vidgen, Richard, 2018. "Developing a business analytics methodology: A case study in the foodbank sector," European Journal of Operational Research, Elsevier, vol. 268(3), pages 836-851.
  • Handle: RePEc:eee:ejores:v:268:y:2018:i:3:p:836-851
    DOI: 10.1016/j.ejor.2017.06.031
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    3. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    4. Johnson, Michael P. & Midgley, Gerald & Chichirau, George, 2018. "Emerging trends and new frontiers in community operational research," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1178-1191.
    5. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    6. Tim, Yenni & Hallikainen, Petri & Pan, Shan L & Tamm, Toomas, 2020. "Actualizing business analytics for organizational transformation: A case study of Rovio Entertainment," European Journal of Operational Research, Elsevier, vol. 281(3), pages 642-655.
    7. Thorsen, Andreas & McGarvey, Ronald G., 2018. "Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1062-1076.
    8. Vidgen, Richard & Hindle, Giles & Randolph, Ian, 2020. "Exploring the ethical implications of business analytics with a business ethics canvas," European Journal of Operational Research, Elsevier, vol. 281(3), pages 491-501.
    9. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.
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    11. Gregory, Amanda J. & Atkins, Jonathan P., 2018. "Community Operational Research and Citizen Science: Two icons in need of each other?," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1111-1124.

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