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

    Hindle, Giles and Vidgen, Richard (2018) Developing a business analytics methodology: a case study in the foodbank sector. European Journal of Operational Research 268 (3), pp. 836-851. ISSN 0377-2217.

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    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 visualisation 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 endeavour.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): analytics, OR for community development, data mining, problem structuring methods, business modelling, soft systems methodology, business analytics methodology
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Richard Vidgen
    Date Deposited: 25 Sep 2017 09:36
    Last Modified: 30 Jul 2019 07:20
    URI: http://eprints.bbk.ac.uk/id/eprint/19801

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