The capability model describes what an enterprise must be able to do to manage data and deliver intelligence — independent of organization structure or technology. It can be used to assess current maturity, identify gaps, assign ownership, and map technology to capabilities.
| Capability area | Capabilities | Typical owner |
|---|---|---|
| Strategy and architecture | Data and AI strategy · capability mapping · reference architecture · principles and standards · roadmap · architecture governance | Enterprise architecture |
| Information architecture | Information domains · conceptual and logical modeling · canonical concepts · master and reference data · identity resolution | Data architecture with domains |
| Data platform | Storage · compute · lakehouse and warehouse services · streaming · orchestration · environment management · FinOps | Platform engineering |
| Integration | APIs · change data capture · events and streaming · batch and file · data contracts · schema management | Integration and platform teams |
| Data engineering | Ingestion · transformation · testing · deployment automation · reliability engineering | Domain and platform engineering |
| Data products | Product design · contracts · publication · discovery · lifecycle · consumer support | Domain product owners |
| Metadata and semantics | Catalog · business glossary · lineage · semantic models · ontologies · knowledge graphs | Data governance and architecture |
| Quality and trust | Quality rules · observability · service objectives · incident management · reconciliation | Domains with platform support |
| Security and privacy | Identity and access · classification · encryption and key management · masking and tokenization · privacy engineering · retention | Security and privacy functions |
| Analytics | Reporting · self-service analytics · metrics and semantic layers · data science | Analytics teams |
| AI and intelligence | Model access and gateway · retrieval and RAG · evaluation · prompt and context management · agents and tools · decision services · AI observability | AI platform and product teams |
| Governance and risk | Policies · decision rights · stewardship · AI risk assessment · audit and evidence · exception management | Governance and risk functions |
| Operating model | Roles and skills · funding · communities of practice · performance measurement · change management | Leadership and architecture |