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Data Quality Skills
Data quality work determines whether data is fit for a defined purpose. Core skills include profiling, requirement gathering, rule design, completeness, accuracy, consistency, timeliness, validity, uniqueness, monitoring, root-cause analysis, issue management, and stewardship.
Quality is contextual. A dataset can be complete yet unsuitable for an AI use because it omits an affected population or reflects outdated processes. Prove competence with a quality scorecard that defines each rule, threshold, owner, monitoring frequency, exception, business impact, and remediation path. Connect the rules to the decisions the data supports.
Related: Data Governance Lead, AI Model Validator, Data Lineage, AI Evaluation and Testing.
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