Gregorio, J; Duncan, P; Alsuleman, M; Chrubasik, M; Cieszynski, B; Desai, V; Hippolyte, J-L; Krishnadas, P; Lines, K; Miller, R; Peric, N; Whelan, H (2026) Data Quality Good Practice Guide. NPL Report. MS 63

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Abstract

Data quality underpins effective decision-making across multiple domains of application. As organisations increasingly rely on digital systems, from analytical pipelines and digital twins to regulatory reporting platforms, the consequences of poor-quality data become more pronounced. This guide provides good practice guidance for understanding and assessing data quality, grounded in established international standards and informed by practical applications across diverse domains.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	MS 63
Keywords:	data quality; data model; data quality dimensions; data traceability; confidence in data; data metrology; data uncertainty; data structures; big data; IoT; data quality frameworks; data management; data regulations; data governance; TDQM; ISO 8000; ISO 25012; DAMA DMBoK; ALCOA+
Subjects:	Mathematics and Scientific Computing > Modelling
Divisions:	Data Science
Identification number/DOI:	10.47120/npl.MS63
Last Modified:	20 Mar 2026 13:19
URI:	https://eprintspublications.npl.co.uk/id/eprint/10337