Data governance has always mattered. But in the AI era, it matters in new ways. The data that trains and grounds AI systems directly determines their quality, fairness, and reliability.
From Documentation to Active Quality
Traditional data governance often focuses on documentation: data dictionaries, lineage diagrams, policy manuals. AI demands more.
Active data quality monitoring. AI systems amplify data quality issues. Governance must shift from periodic audits to continuous monitoring.
Drift detection. Data distributions change over time. Governance frameworks need automated detection of when data characteristics shift.
Ground truth management. For AI systems, you need curated datasets for evaluation. Governing these datasets is critical.
New Governance Domains
AI introduces governance concerns that didn't exist in traditional data management:
Model governance. Models need versioning, access controls, and lifecycle management.
Prompt governance. Who can create prompts? How are they reviewed?
Output governance. What can AI systems produce? How do you enforce content policies?
Governance as Enabler
The mindset shift is crucial: governance should enable AI, not just constrain it. When done well, governance:
- Accelerates development by providing curated, trusted datasets
- Reduces risk by catching issues before production
- Builds trust with stakeholders
- Ensures compliance without blocking legitimate use cases
Data governance in the AI era is harder than ever. But organizations that get it right will build AI systems that are trustworthy.