Identifying Similarities of Big Data Projects–A Use Case Driven Approach

Abstract

Big data is considered as one of the most promising technological advancements in the last decades. Today it is used for a multitude of data intensive projects in various domains and also serves as the technical foundation for other recent trends in the computer science domain. However, the complexity of its implementation and utilization renders its adoption a sophisticated endeavor. For this reason, it is not surprising that potential users are often overwhelmed and tend to rely on existing guidelines and best practices to successfully realize and monitor their projects. A valuable source of knowledge are use case descriptions, of which a multitude exists, each of them with a varying information density. In this design science research endeavor, 43 use cases are identified by conducting a thorough literature review in combination with the application and adaption of a corresponding template for big data projects. By a subsequent categorization, which is performed by identifying and employing a hierarchical clustering algorithm, nine different standard use cases emerge, as the contribution's artifact. This provides decision-makers with an initial entry point, which can be utilized to shape their project ideas, not only by identifying the general meaningfulness of their potential big data project but also in terms of concrete implementation details.

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“…The use of BD as a solid foundation for performance improvement has been established in Coleman et al ( 2016 ); Akter et al ( 2017 ); Mikalef et al ( 2019 ); Almaiah and Nasereddin ( 2020 ), and Dubey et al ( 2020 ) and most of these studies indicated a favorable association between DB success and business performance. However, due to the large size of the database, obtaining important information and insights remains a challenge (Volk et al, 2020 ).…”

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