Architecting the Data Foundation for AI
Build an AI data foundation around governed business entities, usable context, permissions, quality, lineage, feedback, and access patterns.
Start with decisions and entities
Identify the decisions AI will support and the customers, products, transactions, documents, and events required. This prevents a generic data program from expanding without a business use design.
Make context trustworthy
Define ownership, identity resolution, freshness, source authority, metadata, and quality expectations. Retrieval is useful only when systems and reviewers can understand where context came from and when it applies.
Embed permission and feedback
Carry access rules, consent, retention, and sensitivity into every retrieval path. Capture outcomes and corrections so the data foundation supports evaluation and improvement rather than one-way consumption.
Common Mistakes
- Centralizing all data before choosing use cases
- Treating embeddings as a governance layer
- Ignoring freshness and lineage
Market Signals
- Teams cannot identify an authoritative source
- Permissions disappear in derived data
- Corrections are not captured as structured feedback