Identifying and Mitigating Data Quality Risks
Data quality risks threaten the reliability of analysis by introducing errors, gaps, or inconsistencies into datasets. This technique outlines how analysts can identify and mitigate these risks using structured techniques, effective escalation, and data governance practices (Wang and Strong, 1996; Ilyas and Chu, 2019).
Technique Overview
Identifying and Mitigating Data Quality Risks Definition
Data quality risks refer to flaws that compromise the suitability of data for analysis. These risks affect accuracy, completeness, consistency, or timeliness, and may arise from poor data entry, flawed integration, or outdated systems (Wang and Strong, 1996). Mitigation involves profiling, validation, documentation, and structured resolution or escalation (Ilyas and Chu, 2019; Loshin, 2011).
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Further Reading
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Identifying and Mitigating Data Quality Risks references (4 of up to 20) *
- Majeed, A. and Hwang, S.O. (2025). When Poor-Quality Data Meet Anonymization Models: Threats and Countermeasures. IEEE Access, 13, pp.49457–49475. doi:https://doi.org/10.1109/access.2025.3552412.
- Wang, R.Y. and Strong, D.M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), pp.5–33. doi:https://doi.org/10.1080/07421222.1996.11518099.
- Loshin, D., 2011. Improved Risk Management via Data Quality Improvement. Available at: https://www.biia.com/wp-content/uploads/2011/11/1542_KnowledgeIntegRiskdq.pdf
- Floyd, N.D. and Kirby, Y. (2018). Identifying and Mitigating Data Risk. Routledge eBooks, pp.159–172. https://doi.org/10.4324/9781315171326
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