Towards a Low-Code Tool for Developing Data Quality Rules

Timon Klann, Marcel Altendeitering, Falk Howar

2023

Abstract

High-quality data sets are vital for organizations as they promote business innovation and the creation of data-driven products and services. Data quality rules are a common approach to assess and enforce compliance with business domain knowledge and ensure the correctness of data sets. These data sets are usually subject to numerous data quality rules to allow for varying requirements and represent the needs of different stakeholders. However, established data quality tools have a rather technical user interface and lack support for inexperienced users, thus hindering them from specifying their data quality requirements. In this study, we present a tool for the user-friendly and collaborative development of data quality rules. Conceptually, our tool realizes a domain-specific language, which enables the graphical creation of rules using common data quality constraints. For implementation, we relied on CINCO, a tool for creating domain-specific visual modeling solutions, and Great Expectations, an open-source data validation framework. The evaluation of our prototype was two-fold, comprising expert interviews and a focus group discussion. Overall, our solution was well-received and can contribute to lowering the accessibility of data quality tools.

Paper Citation

in Harvard Style

Klann T., Altendeitering M. and Howar F. (2023). Towards a Low-Code Tool for Developing Data Quality Rules. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 22-29. DOI: 10.5220/0012050400003541

in Bibtex Style

@conference{data23,
author={Timon Klann and Marcel Altendeitering and Falk Howar},
title={Towards a Low-Code Tool for Developing Data Quality Rules},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={22-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012050400003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Towards a Low-Code Tool for Developing Data Quality Rules
SN - 978-989-758-664-4
AU - Klann T.
AU - Altendeitering M.
AU - Howar F.
PY - 2023
SP - 22
EP - 29
DO - 10.5220/0012050400003541
PB - SciTePress