Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps

Christian Haertel, Daniel Staegemann, Matthias Pohl, Klaus Turowski

2025

Abstract

Data Science (DS) uses advanced analytical methods, such as Machine Learning, to extract value from data to improve organizational performance. However, numerous DS projects fail due to the complexity and difficulty of handling various managerial and technical challenges. Because of shortcomings in existing DS methodologies, new standardized approaches for DS project management are needed that respect both the business and data perspectives. In this paper, the concept for a DS process model to address common problems in DS, including a low level of process maturity and a lack of reproducibility, is outlined. This artifact is developed using the Design Science Research methodology and relies on MLOps principles to support the development and operationalization of the analytical artifacts in DS projects.

Paper Citation

in Harvard Style

Haertel C., Staegemann D., Pohl M. and Turowski K. (2025). Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS; ISBN 978-989-758-769-6, SciTePress, pages 550-557. DOI: 10.5220/0013841000004000

in Bibtex Style

@conference{kmis25,
author={Christian Haertel and Daniel Staegemann and Matthias Pohl and Klaus Turowski},
title={Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS},
year={2025},
pages={550-557},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013841000004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS
TI - Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps
SN - 978-989-758-769-6
AU - Haertel C.
AU - Staegemann D.
AU - Pohl M.
AU - Turowski K.
PY - 2025
SP - 550
EP - 557
DO - 10.5220/0013841000004000
PB - SciTePress