Authors:
Christian Haertel
;
Sarah Schramm
;
Matthias Pohl
;
Sascha Bosse
;
Daniel Staegemann
;
Christian Daase
and
Klaus Turowski
Affiliation:
Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany
Keyword(s):
Data Science, Project Management, Pattern, Design Science Research.
Abstract:
In the era of Big Data, the successful completion of Data Science (DS) projects is crucial. However, DS project management is quite challenging due to its interdisciplinary nature. Existing DS process models, such as CRISP-DM, have limitations, resulting in low success rates for these undertakings. To address this issue, a novel methodology for the construction of patterns in DS project management has been proposed, using the Design Science Research methodology. The design draws inspiration from existing pattern concepts to address common problems in DS project execution. The methodology is demonstrated through the creation of patterns for best practices in DS project management, synthesized from scientific literature. The goal of this approach is to provide a platform for exchanging and standardizing best practices in DS project management. While initial demonstrations show the general applicability of the methodology, further evaluations and case studies are necessary to assess its
effectiveness and areas for improvement. The study identifies potential ambiguities in certain activities within the process, suggesting opportunities for refinement. Overall, this research contributes to the field of DS project management by offering a structured method to encapsulate and disseminate effective practices, supporting the successful execution of data projects in organizations.
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