Data Science projects aim to methodologically extract knowledge and value from data to help organizations to improve performance. Dedicated process models are applied to support the management of these endeavors. However, high failure rates in the execution highlight the need for improvements in Data Science project management. Therefore, in this paper, stages and activities, functional roles, and artifacts of 28 Data Science process models are analyzed in a literature review. Based on the findings, a Data Science Lifecycle, consisting of six phases, is derived. Additionally, a corresponding Data Science process map provides an overview regarding the involved team roles and required deliverables of the individual activities in a Data Science undertaking. Accordingly, this artifact aims to mitigate the current project management issues in Data Science. For future research, the results of this study can serve as the foundation for a holistic process model for Data Science project management.
Haertel, Christian; Pohl, Matthias; Nahhas, Abdulrahman; Staegemann, Daniel; and Turowski, Klaus, "Toward A Lifecycle for Data Science: A Literature Review of Data Science Process Models" (2022). PACIS 2022 Proceedings. 242.
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