What is Data Governance?

Explained

Data governance defines who can use which data, how it is classified, retained, and trusted. Strong governance is prerequisite for compliant AI, reliable features, and explainability—it connects privacy, master data, and model risk.

Decision guides that use this term

• Sovereign or on-prem AI vs cloud AI APIs: where should your models run?
• Centralised vs federated AI governance: who should decide what in your organisation?
• Fix the data first or start with AI now: which comes first?
• Synthetic data vs real data: what should you train, tune and test your AI on?

Further reading

• Enterprise AI Strategy: Data & Advanced Analytics

Related terms

• Explainability Ability to explain how a model reaches a conclusion (Explainable AI, XAI).
• Acceptance Criteria Testable conditions that define when a feature or model output is “done” and correct.
• AI Bill of Materials Inventory of model lineage, datasets, dependencies, and licenses for an AI system.
• AI Governance Framework of policies, processes, and controls for responsible and ethical AI use in the enterprise.
• AI Native Architecture and processes designed from the ground up for AI, not as an afterthought.
• CRM System for managing customer relationships; AI augments it with prediction and automation.