{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:55:16Z","timestamp":1774547716698,"version":"3.50.1"},"reference-count":137,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Government Department for Science, Innovation, and Technology through the UK\u2019s National Measurement System"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This study reviews various data quality frameworks that have some form of regulatory backing. The aim is to identify how these frameworks define, measure, and apply data quality dimensions. This review identified generalisable frameworks, such as TDQM, ISO 8000, and ISO 25012, and specialised frameworks, such as IMF\u2019s DQAF, BCBS 239, WHO\u2019s DQA, and ALCOA+. A standardised data quality model was employed to map the dimensions of the data from each framework to a common vocabulary. This mapping enabled a gap analysis that highlights the presence or absence of specific data quality dimensions across the examined frameworks. The analysis revealed that core data quality dimensions such as \u201caccuracy\u201d, \u201ccompleteness\u201d, \u201cconsistency\u201d, and \u201ctimeliness\u201d are equally and well represented across all frameworks. In contrast, dimensions such as \u201csemantics\u201d and \u201cquantity\u201d were found to be overlooked by most frameworks, despite their growing impact for data practitioners as tools such as knowledge graphs become more common. Frameworks tailored to specific domains were also found to include fewer overall data quality dimensions but contained dimensions that were absent from more general frameworks, highlighting the need for a standardised approach that incorporates both established and emerging data quality dimensions. This work condenses information on commonly used and regulation-backed data quality frameworks, allowing practitioners to develop tools and applications to apply these frameworks that are compliant with standards and regulations. The bibliometric analysis from this review emphasises the importance of adopting a comprehensive quality framework to enhance governance, ensure regulatory compliance, and improve decision-making processes in data-rich environments.<\/jats:p>","DOI":"10.3390\/bdcc9040093","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T11:26:41Z","timestamp":1744284401000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Comparison of Data Quality Frameworks: A Review"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9947-9258","authenticated-orcid":false,"given":"Russell","family":"Miller","sequence":"first","affiliation":[{"name":"National Physical Laboratory, Informatics, Data Science Department, Glasgow G1 1RD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8820-5306","authenticated-orcid":false,"given":"Sai Hin Matthew","family":"Chan","sequence":"additional","affiliation":[{"name":"National Physical Laboratory, Informatics, Data Science Department, Teddington TW11 0LW, UK"},{"name":"Department of Mathematics, University of Bath, Bath BA2 7AY, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4174-3535","authenticated-orcid":false,"given":"Harvey","family":"Whelan","sequence":"additional","affiliation":[{"name":"National Physical Laboratory, Informatics, Data Science Department, Teddington TW11 0LW, UK"},{"name":"Department of Natural Sciences, University of Bath, Bath BA2 7AX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0976-1343","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Greg\u00f3rio","sequence":"additional","affiliation":[{"name":"National Physical Laboratory, Informatics, Data Science Department, Glasgow G1 1RD, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","unstructured":"(2022). 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