Overview
Most analytics and AI problems are data problems wearing a different hat. Two reports disagree, nobody owns the definition, and when a regulator asks where a number came from, the answer takes a week of manual tracing.
This is the deepest part of our practice. We build the governance operating model, the quality framework and rules, the catalog and lineage, and the master data logic underneath — the foundations reporting and AI both depend on.
Signs you need this
Two teams pull the same metric and get different answers.
A governance framework was delivered by a previous engagement and has not been opened since.
A lineage request takes a week of manual tracing and produces a diagram nobody trusts.
The same customer exists several times under slightly different names.
What we deliver
Data governance
Target operating model, stewardship structures, councils, policies, and critical data element governance that assigns real ownership.
Data quality
Frameworks, profiling, column-level business rules, scorecards, and the issue management workflow that turns a failure into an owned action.
Metadata and lineage
Catalog implementation across Collibra, Atlan, Alation, EDC, or Purview, with business glossary and end-to-end lineage.
Master and reference data
MDM design, matching and survivorship logic, reference data governance, and steward exception workflows.
Data platforms
Lake and warehouse architecture, pipeline engineering in SQL, Python, and PySpark, and migration across Snowflake, Databricks, Azure, and AWS.
Regulatory data
BCBS 239, Basel, CCAR, and FR Y-14 requirements translated into concrete quality, lineage, and control obligations.
How this work runs.
Current state
Data landscape, ownership, tooling, and maturity mapped against a capability model, with the gaps named plainly.
Critical data elements
We agree which data actually matters — usually a fraction of what gets proposed — and what fit-for-purpose means for each.
Model and architecture
Governance operating model, quality framework, and technical architecture, approved before anything is built.
Rules, catalog, lineage
Hands-on delivery with your stewards in the work from the first iteration rather than trained at the end.
Business as usual
Monitoring, reporting, runbooks, and the cadence that keeps the program alive after handover.
What you are left with.
Documented and transferred, so your team owns it.
- Data governance target operating model
- Critical data element register
- Deployed data quality rules and scorecards
- Configured catalog with business glossary
- End-to-end lineage for critical flows
- Master data matching and survivorship design
- Issue management workflow
- Steward runbooks and training