Abstract

Data Science (DS) leverages Data Analytics to help organizations extract value from large datasets and enhance performance. A significant focus in DS is on data collection, cleaning, and transformation to create high-quality datasets for analysis. However, traditional manual data preparation methods are often inefficient and error-prone, particularly in Big Data environments. DataOps seeks to automate data lifecycle stages by integrating DevOps practices, enhancing the quality and reliability of data pipelines. This paper proposes a framework for implementing DataOps, demonstrated through a case study on urban mobility analytics.

Recommended Citation

Haertel, C., Sagavakar, K.S., Staegemann, D., Pohl, M., Volk, M. & Turowski, K. (2025). Toward a DataOps Framework for Enhancing Data Quality in Data ScienceIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.80

Paper Type

Poster

DOI

10.62036/ISD.2025.80

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Toward a DataOps Framework for Enhancing Data Quality in Data Science

Data Science (DS) leverages Data Analytics to help organizations extract value from large datasets and enhance performance. A significant focus in DS is on data collection, cleaning, and transformation to create high-quality datasets for analysis. However, traditional manual data preparation methods are often inefficient and error-prone, particularly in Big Data environments. DataOps seeks to automate data lifecycle stages by integrating DevOps practices, enhancing the quality and reliability of data pipelines. This paper proposes a framework for implementing DataOps, demonstrated through a case study on urban mobility analytics.