Data Quality Scoring: A Conceptual Model and Prototypical Implementation

Mario Köbis-Riedel, Marcel Altendeitering, Christian Beecks

2025

Abstract

A high level of data quality is crucial for organizations as it supports efficient processes, corporate decision-making, and driving innovation. However, collaborating on data across organizational borders and sharing data with business partners is often impaired by a lack of data quality information and different interpretations of the data quality concept. This information asymmetry of data quality information between data provider and consumer leads to a lower usability of data sets. In this paper, we present the conceptual model and prototypical implementation of a Data Quality Scoring (DQS) solution. Our solution automatically assesses the quality of a data set and allocates a data quality label similar to the Nutri-Score label for food. This way, we can communicate the data quality score in a structured and user-friendly way. For evaluation, we tested our approach using exemplary data sets and assessed the general functionality and runtime complexity. Overall, we found that our proposed DQS system is capable of automatically allocating data quality labels and can support communicating data quality information.

Paper Citation

in Harvard Style

Köbis-Riedel M., Altendeitering M. and Beecks C. (2025). Data Quality Scoring: A Conceptual Model and Prototypical Implementation. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 329-338. DOI: 10.5220/0013461300003967

in Bibtex Style

@conference{data25,
author={Mario Köbis-Riedel and Marcel Altendeitering and Christian Beecks},
title={Data Quality Scoring: A Conceptual Model and Prototypical Implementation},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={329-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013461300003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Data Quality Scoring: A Conceptual Model and Prototypical Implementation
SN - 978-989-758-758-0
AU - Köbis-Riedel M.
AU - Altendeitering M.
AU - Beecks C.
PY - 2025
SP - 329
EP - 338
DO - 10.5220/0013461300003967
PB - SciTePress