COMPEL Glossary / data-governance
Data Governance
Data governance encompasses the organizational processes, policies, standards, and accountability structures that ensure data is accurate, consistent, secure, and used appropriately across the enterprise.
What this means in practice
For AI, data governance must address requirements beyond traditional data management: training data provenance (where data came from and how it was transformed), representativeness assessment (whether data fairly represents all populations the AI will serve), consent management for ML (ensuring data use aligns with the basis under which it was collected), synthetic data governance, and monitoring for data drift. Data governance is the foundation upon which AI governance stands -- every AI risk traced to its root cause terminates in data. In the COMPEL maturity model, Data Management and Quality (Domain 6) is assessed separately from Data Infrastructure (Domain 10) because excellent technology with poor governance is a common and dangerous pattern.
Why it matters
Data governance is the foundation upon which AI governance stands. Every AI risk, when traced to its root cause, terminates in data. Organizations with weak data governance produce AI systems that learn from inaccurate, biased, or improperly sourced data, creating legal liability, reputational risk, and unreliable business decisions. Strong data governance enables confident, compliant, and trustworthy AI deployment.
How COMPEL uses it
COMPEL assesses Data Management and Quality (Domain 6) separately from Data Infrastructure (Domain 10) because excellent technology with poor governance is a common and dangerous pattern. During Calibrate, data governance maturity is baselined. The Model stage designs governance frameworks including quality standards, access controls, and lineage tracking. The Evaluate stage audits governance compliance across all AI systems.
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Other glossary terms mentioned in this entry's definition and context.