Data governance vs AI governance
Data governance addresses how an organisation manages its data assets — the policies, standards, and accountability structures for data throughout its lifecycle. AI governance addresses the governance of AI systems specifically. AI systems are fundamentally data systems — their behaviour is determined by the data they were trained on, receive as inputs, and produce as outputs. Poor data governance propagates directly into AI governance failures: poor quality data produces poor quality AI outputs; biased training data produces biased AI; improperly consented data creates legal exposure.
Core elements and AI-specific requirements
Data ownership: every data asset including training datasets has a defined owner. Data quality: standards ensuring data is accurate, complete, and consistent — for AI, quality issues in training data propagate into model behaviour. Data lineage: knowing what data a model was trained on is increasingly a regulatory expectation. AI-specific requirements: training data provenance and consent (the most frequently unaddressed area); bias monitoring in training datasets; version control for training data knowing which version produced which model; and data lineage through AI pipelines.
