Data Mapping and Identifying Data Quality Gap Analysis
AI and automation rely on data that leaders can trust. Leaders must be able to trace data from its source to its use, check whether it is good enough for a specific AI or automation decision, and close important gaps before rollout (UK Government, 2025; NIST, 2023).
Technique Overview
Data Mapping and Identifying Data Quality Gap Analysis Definition
Data mapping means recording where data comes from, what it means, where it goes, how it changes and who uses it. Data quality gap analysis compares the data practitioners have now with the standard needed for a specific purpose. Together, they help practitioners spot missing, inaccurate, duplicated, inconsistent, outdated, unrepresentative or poorly governed data before it is used by AI or automation (ICO, nd; ISO, 2024a).
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Further Reading
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Data Mapping and Identifying Data Quality Gap Analysis references (4 of up to 20) *
- CDEI (Centre for Data Ethics and Innovation) (2019) Interim report: Review into bias in algorithmic decision-making. Available at: www.gov.uk/government/publications/interim-reports-from-the-centre-for-data-ethics-and-innovation/interim-report-review-into-bias-in-algorithmic-decision-making
- CDEI (Centre for Data Ethics and Innovation) (2020) Review into bias in algorithmic decision-making. Available at: www.gov.uk/government/publications/cdei-publishes-review-into-bias-in-algorithmic-decision-making/main-report-cdei-review-into-bias-in-algorithmic-decision-making
- DAMA International (2017) DAMA-DMBOK: Data Management Body of Knowledge. 2nd edn. Basking Ridge, NJ: Technics Publications.
- FCA and PRA (2022) TSB fined £48.65m for operational resilience failings. Available at: www.fca.org.uk/news/press-releases/tsb-fined-48m-operational-resilience-failings
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