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Assessing data quality: A managerial call to action

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  • Nagle, Tadhg
  • Redman, Tom
  • Sammon, David

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.

Suggested Citation

  • Nagle, Tadhg & Redman, Tom & Sammon, David, 2020. "Assessing data quality: A managerial call to action," Business Horizons, Elsevier, vol. 63(3), pages 325-337.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:3:p:325-337
    DOI: 10.1016/j.bushor.2020.01.006
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    References listed on IDEAS

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    1. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
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    Cited by:

    1. Yuqi Zang & Jiamei Zhao & Wenchao Jiang & Tong Zhao, 2024. "Advanced Linguistic Complex T-Spherical Fuzzy Dombi-Weighted Power-Partitioned Heronian Mean Operator and Its Application for Emergency Information Quality Assessment," Sustainability, MDPI, vol. 16(7), pages 1-36, April.
    2. Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael, 2022. "The perils of working with big data, and a SMALL checklist you can use to recognize them," Business Horizons, Elsevier, vol. 65(4), pages 481-492.

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