Data Strategy & Governance
"Trustworthy data is the unfair advantage in enterprise AI"
A coherent strategy for collecting, cataloguing, governing, and providing data — so AI workloads have the trusted foundation they need to graduate from PoC to production.
Six governance surfaces
Data domains & ownership
Map of domains, owners, stewards, and SLAs across the enterprise.
Catalog & lineage
Single discoverable catalog with column-level lineage and metadata.
Quality controls
Contracts, tests, and anomaly detection on critical data flows.
Access control
RBAC, ABAC, row/column-level security, just-in-time access workflows.
Privacy by design
PII discovery, classification, masking, and lawful-basis tracking.
Regulatory alignment
GDPR, DPDP Act, sector-specific regulations baked into policies.
A staged rollout
Baseline
Current state: catalog, quality, access, classification, regulatory exposure.
Design
Target operating model, control framework, and tooling selection.
Implement
Wave-by-wave rollout starting with highest-value, highest-risk domains.
Operate
Continuous improvement, evidence collection, and quarterly governance reviews.
Related sub-services
Talk to us about data strategy
Tell us about your data estate. We will return with a baseline plan and rollout proposal.