 Assessing data quality: A managerial call to actionTadhg Nagle, Tom Redman and David Sammon Business Horizons, 2020, vol. 63, issue 3, 325-337 Abstract: While awareness of data quality has increased in recent years, there have been very few studies on the actual level of data quality within organizations. This article outlines the analysis of 75 data quality assessments collected over the course of 2 years from a wide range of organizations, data sets, and business processes. The results reveal that data is in far worse shape than most managers realize. On average, 47% of recently created data records have at least one critical error. High-quality data is the exception, with only 3% of the DQ scores rated acceptable (≥97%). Indeed, the results suggest an unhealthy organizational tolerance of bad data and underscore the magnitude of improvement organizations need to make in order to be truly effective in the knowledge economy. By providing a unique insight and benchmark for data quality practitioners, this article serves as a call-to-action for all organizations—regardless of size and type—to determine their level of data quality. Finally, we set out a typology that presents a categorical scheme to promote preemptive actions against the most frequent types of data error. Keywords: Data quality; Friday afternoon measurement (FAM); Bad data; Data quality assessment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:bushor:v:63:y:2020:i:3:p:325-337 DOI: 10.1016/j.bushor.2020.01.006 Access Statistics for this article Business Horizons is currently edited by C. M. Dalton More articles in Business Horizons from Elsevier |
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