A Methodology for Constructing Patterns for the Management of Data Science Projects

Christian Haertel, Sarah Schramm, Matthias Pohl, Sascha Bosse, Daniel Staegemann, Christian Daase, Klaus Turowski

2024

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.

Paper Citation

in Harvard Style

Haertel C., Schramm S., Pohl M., Bosse S., Staegemann D., Daase C. and Turowski K. (2024). A Methodology for Constructing Patterns for the Management of Data Science Projects. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 354-365. DOI: 10.5220/0012705300003690

in Bibtex Style

@conference{iceis24,
author={Christian Haertel and Sarah Schramm and Matthias Pohl and Sascha Bosse and Daniel Staegemann and Christian Daase and Klaus Turowski},
title={A Methodology for Constructing Patterns for the Management of Data Science Projects},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={354-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012705300003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Methodology for Constructing Patterns for the Management of Data Science Projects
SN - 978-989-758-692-7
AU - Haertel C.
AU - Schramm S.
AU - Pohl M.
AU - Bosse S.
AU - Staegemann D.
AU - Daase C.
AU - Turowski K.
PY - 2024
SP - 354
EP - 365
DO - 10.5220/0012705300003690
PB - SciTePress