Version 1
: Received: 12 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (10:39:28 CEST)
How to cite:
Miller, R.; Whelan, H.; Chrubasik, M.; Whittaker, D.; Duncan, P.; Gregório, J. An Overview of Current and New Data Quality Dimensions under a Common Framework. Preprints2024, 2024091076. https://doi.org/10.20944/preprints202409.1076.v1
Miller, R.; Whelan, H.; Chrubasik, M.; Whittaker, D.; Duncan, P.; Gregório, J. An Overview of Current and New Data Quality Dimensions under a Common Framework. Preprints 2024, 2024091076. https://doi.org/10.20944/preprints202409.1076.v1
Miller, R.; Whelan, H.; Chrubasik, M.; Whittaker, D.; Duncan, P.; Gregório, J. An Overview of Current and New Data Quality Dimensions under a Common Framework. Preprints2024, 2024091076. https://doi.org/10.20944/preprints202409.1076.v1
APA Style
Miller, R., Whelan, H., Chrubasik, M., Whittaker, D., Duncan, P., & Gregório, J. (2024). An Overview of Current and New Data Quality Dimensions under a Common Framework. Preprints. https://doi.org/10.20944/preprints202409.1076.v1
Chicago/Turabian Style
Miller, R., Paul Duncan and João Gregório. 2024 "An Overview of Current and New Data Quality Dimensions under a Common Framework" Preprints. https://doi.org/10.20944/preprints202409.1076.v1
Abstract
This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.
Keywords
data quality; data model; data quality dimensions; data traceability; confidence in data; data metrology; data uncertainty; data structures; big data; IoT
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
