Where it matters
The application cannot answer the right questions quickly, a migration carries business risk, or an AI feature needs access to internal data without exposing the wrong records.
What this discipline brings
- Data models and versioned migrations with validation and rollback plans
- Query and retrieval performance measured against real workloads
- Access and retention rules enforced at the right boundaries
- Data lineage and source traceability where decisions depend on it
Data work can support AI Production Readiness or Productionization & Modernization, depending on the decision ahead.