Data-driven transformation has become a necessary step towards sustainable change and a major accelerator of companies' progress. In this journey, one of the early challenges companies are confronted to prioritizing the initiatives in an effective fashion. Many conventional techniques have been used to prioritize IT projects, and some were slightly adapted to handle data analytics use cases specifically, but decision makers mostly rely on their expertise and understanding of the company's objectives and constraints to put the use cases in order. This paper digs further in this topic by reviewing the prioritization techniques available today, discussing how these techniques and their criteria fit in the context of data analytics use cases. It then, proposes a comprehensive criteria hierarchy that can be used by a scoring model or a hierarchical multi-criteria method such as AHP to provide better guidance to decision makers for the prioritization of data analytics use cases.
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Iraqi et al. (2024) studied this question.
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