Agree what the numbers mean
Resolve competing business definitions and unclear ownership. Create a shared vocabulary for priority entities, measures, and decisions.
Data governance that makes use defensible.
Connect ownership, meaning, access, and evidence to the way your organization actually uses data.
Trust → evidence
Governance should reach the work.
A policy becomes useful when people know who owns a dataset, what its measures mean, who can use it, and how to resolve a problem. We translate governance expectations into responsibilities and controls that teams can operate.
Where to start
Which data decision needs a clearer foundation?
Trace data from source to use
Map where important records originate, how they change, and where they are consumed. Identify quality checks and evidence needed for review.
Make permitted use explicit
Connect purpose, access, and review responsibilities. Identify where sensitive data or AI use requires a more deliberate decision and a named owner.
What we build
From intent to
operating practice.
A focused scope, useful deliverables, and evidence your team can review.
Ownership & decision rights
An operating model with data owners, stewards, escalation routes, and review responsibilities. Clarify who approves a change and who resolves an exception.
Definitions & lineage
A practical glossary and source-to-use maps for the agreed scope. Connect important measures to transformations, sources, and accountable teams.
Quality & access controls
Documented quality expectations, validation checks, and role-based access requirements. Test permitted and denied use in the workflows that matter.
Evidence & oversight
A findings register, remediation priorities, and review cadence. Give leaders a view of decisions, outstanding gaps, and the owners responsible for action.
How we work
One bounded engagement.
A clear next step.
Scope depends on your data, existing systems, access, and review capacity. We agree the work and its acceptance criteria before implementation begins.
Choose a decision
Focus on a business process, dataset, or report where unclear data creates friction or risk.
Map the current state
Review sources, definitions, access, quality checks, and the people already doing the work.
Put controls into practice
Agree ownership and implement the priority definitions, checks, and access decisions in scope.
Review and maintain
Establish evidence, issue management, and a manageable review cycle as systems and uses change.
Before we begin
Data Governance:
your questions.
Where should a governance program start?
Start with a specific decision or workflow and the data it depends on. That gives ownership, definitions, and controls a concrete purpose and makes the initial scope easier to assess.
Is this a policy exercise or implementation work?
The scope can include both. Policies and responsibilities need to connect to working definitions, access decisions, validation checks, and a process for resolving exceptions.
Can governance support AI adoption?
Yes. Understanding data purpose, ownership, quality, and access helps define what an AI system may use and how its outputs can be reviewed. AI-specific requirements are assessed within the engagement scope.
How does governance stay current?
Give owners a regular review cadence and a clear process for changes. Revisit definitions, permitted uses, quality checks, and open findings when systems or business needs change.
Start with your use case
What should trusted data
make possible?
Bring a question about data ownership, quality, access, or oversight. Let’s define the next practical step.
Plan an Engagement