A Framework for Current and New Data Quality Dimensions: An Overview

Russell Miller, Harvey Whelan, Michael Chrubasik, David Whittaker, Paul Duncan, João Gregório

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a comprehensive exploration of data quality terminology, revealing a significant lack of standardisation in the field. The goal of this work was to conduct a comparative analysis of data quality terminology across different domains and structure it into a hierarchical data model. We propose a novel approach for 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. We introduce four additional data quality dimensions: governance, usefulness, quantity, and semantics. These dimensions enhance specificity, complementing the framework established by the ISO 25012 standard, as well as contribute to a broad understanding of data quality aspects. The ISO 25012 standard, a general standard for managing the data quality in information systems, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of digital systems across a multitude of domains. In contrast, frameworks such as ALCOA+, which were originally developed for specific regulated industries, can be applied more broadly but may not always be generalisable. Ultimately, the model we propose aggregates and classifies data quality terminology, facilitating seamless communication of the data quality between different domains when collaboration is required to tackle cross-domain projects or challenges. By establishing this hierarchical model, we aim to improve understanding and implementation of data quality practices, thereby addressing critical issues in various domains.

Original languageEnglish
Article number151
JournalData
Volume9
Issue number12
Early online date18 Dec 2024
DOIs
Publication statusPublished - 31 Dec 2024

Data Availability Statement

The original contributions presented in this study are included in the article; further enquiries can be directed to the corresponding authors.

Keywords

  • big data
  • confidence in data
  • data metrology
  • data model
  • data quality
  • data quality dimensions
  • data structures
  • data traceability
  • data uncertainty
  • IoT

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Information Systems and Management

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