Data Governance & Quality
Data your executives and your AI can trust.
Practical data governance: ownership, quality rules, lineage, catalogs and access policies embedded in your platforms, so trust scales with your data.
Why it matters
Governance that lives in the platform, not in a binder
Traditional data governance programs produce committees and documents. Data still arrives late, metrics disagree and no one knows who can approve access. AI raises the stakes: a model or agent will use whatever data it can reach.
We implement governance as code and as an operating model: named data owners for critical domains, automated quality checks in pipelines, lineage captured by the platform, and access policies enforced consistently across BI, applications and AI.
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
Operating model
Domains, owners, stewards and decision rights that fit how you work.
Data quality engineering
Rules, tests and anomaly detection in pipelines with alerting.
Catalog & lineage
Catalog implementation with automated lineage and business glossary.
Access & privacy
Classification, masking and attribute-based access control.
AI data readiness
Policies for using data in models, RAG and agents.
Master data
Customer, product and supplier mastering where it matters most.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Prioritize
Critical data elements and domains tied to business use.
Design
Operating model, policies and tooling choices.
Implement
Quality, catalog, lineage and access controls on priority domains.
Scale
Expand domains and measure trust and quality KPIs.
Typical starting engagement
Data Governance Accelerator
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- Operating model and decision rights
- Quality rules on 2-3 priority domains
- Catalog and lineage for priority data
- Access and classification policies
- Data trust scorecard
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
Where should data governance start?
With the data behind your most important decisions and AI use cases, not with an enterprise-wide inventory. Start narrow, prove value, then scale.
Which catalog tools do you work with?
Platform-native options such as Unity Catalog, Snowflake Horizon and Microsoft Purview, as well as dedicated catalogs. We recommend based on your estate.
How do you measure success?
Data quality scores, incident reduction, time to access approval, and trust ratings from business users.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.