PACIS 2022 Proceedings

Paper Number

1534

Abstract

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.

Share

COinS
 

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.