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A Systematic Approach for Business Data Analytics with a Real Case Study

Author

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  • Kaibo Liu

    (Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA)

  • Jianjun Shi

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

Abstract

Business data analytics is a process of utilizing analytic techniques for resolving business issues based on business performance data. While the avalanche of business data creates unprecedented opportunity, it also poses three fundamental challenges for analytics: (1) Business data often encounters quality issues and needs substantial cleaning efforts; (2) Business data is large in overall size but cannot be fully shared due to the concern of data security; and (3) Business data often needs to be cross-referenced with public databases to reveal more information and knowledge. Due to these challenges, the leading obstacle at many organizations is the lack of a systematic approach to understanding how to leverage the business data analytics techniques to transfer from data-rich into decision-smart. To answer this question, this article proposes a systematic step-by-step procedure for business data analytics. This proposed framework is illustrated and validated by a real case study that involves choosing an optimal location for opening of a new retail site.

Suggested Citation

  • Kaibo Liu & Jianjun Shi, 2015. "A Systematic Approach for Business Data Analytics with a Real Case Study," International Journal of Business Analytics (IJBAN), IGI Global, vol. 2(4), pages 23-44, October.
    • Handle: RePEc:igg:jban00:v:2:y:2015:i:4:p:23-44
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    Cited by:

    1. Kayabay, Kerem & Gökalp, Mert Onuralp & Gökalp, Ebru & Erhan Eren, P. & Koçyiğit, Altan, 2022. "Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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