AI-Ready Data Estate
Is our data ready for the AI we want to run?
AI amplifies data problems at scale. Readiness is proven use case by use case, not across the whole estate at once.
Maturity
1Exploring
Data siloed by application. Access negotiated per project.
2Establishing
A central platform for analytics. Quality managed reactively.
3ScalingExample
Data products with owners, contracts and service levels.
4Compounding
A unified, governed estate. New use cases start in weeks.
Key decisions
- Consolidate or federateOne platform versus respecting existing investments
- Copy or virtualizePerformance versus duplication and cost
- Domain ownership or central teamScale versus consistency
Anti-patterns
- Boiling the ocean before the first use case
- A data lake nobody owns
- Leaving unstructured data out of scope
Measure progress
- Time to provision data for a new use case
- Share of critical data with a named owner
- Quality incidents reaching production AI
Builds on
Enables
- A portfolio value model needs trusted baseline data.Needed for AI Value Realization at Scaling
- Semantic models sit on a central data platform.Needed for Enterprise Context Layer at Establishing
- Shared definitions need owned data products underneath.Needed for Enterprise Context Layer at Scaling
- End-to-end agents need reliable data products.Needed for AI in the Process at Scaling
- Audit-readiness depends on lineage and ownership in the data.Needed for Governance & Trust at Compounding
Plays
Microsoft-specific guidance, as of September 2026.
Ratings are illustrative and pending author review.
Capabilities
| Capability | Role in this pillar | Rating |
|---|---|---|
| Microsoft Fabric | Unified data and analytics platform | Adopt |
| OneLake shortcuts and mirroring | Use data where it lives, including SAP, Databricks and Snowflake | Trial |
| Microsoft Purview Unified Catalog | Data products, ownership and quality | Trial |
Adopt
Proven. Use it by default for this purpose.
Trial
Production-ready. Use it with a clear scope.
Assess
Promising. Explore with a small team.
Hold
Not recommended for new work.
Known gaps
Mirroring and shortcut coverage differs by source system. Validate your specific sources early.