Governance that stops the same argument happening twice

Most governance programmes stall because they start with a policy document. Nobody reads it, nothing is enforced, and the next dashboard re-litigates the same definition fight. We start with the definitions people already argue about, encode them, and assign an owner.

In plain terms

What Data Governance Consulting means in practice

Brainforge delivers data governance consulting that makes definitions, ownership, and quality enforceable. We agree what each metric means, assign owners, test the data, and document the lineage so trust stops depending on one person.

Where teams get stuck

Metric definitions, ownership, quality testing, and lineage that make the numbers defensible.

Two teams present two different numbers for the same metric and both believe they are right.

Nobody can trace a dashboard figure back to its source without asking one specific person.

Data quality issues are found by an executive during a meeting rather than by a test.

We have a governance policy, and nothing in the stack enforces any of it.

Access is granted ad hoc, so nobody knows who can see what.

What we deliver

Core Data Governance Consulting

Governance assessment

A review of how definitions, ownership, quality, and access work today, and where trust actually breaks.

Metric definitions and ownership

One agreed definition per metric, encoded in a semantic layer, with a named owner and a review path.

Data quality and testing

Tests that catch bad data before it reaches a decision, rather than after someone presents it.

Lineage and documentation

Making it possible to trace any number back to its source without asking the person who built the query.

How deep it goes

Capabilities behind the work

The same delivery primitives (context, controls, and review) show up across every engagement.

Governance assessment

Finding where trust actually breaks before writing any policy.

Definitions and metric auditOwnership and access reviewQuality incident retrospectiveTooling and enforcement gap analysis

Metric definitions and semantic layer

Turning agreed definitions into something the stack enforces.

Definition workshops with stakeholdersSemantic layer designOwner assignment and review cadenceReconciliation against source systems

Data quality and testing

Catching problems in the pipeline rather than in the board meeting.

dbt and warehouse test coverageFreshness and volume monitoringAnomaly alertingIncident response process

Lineage and documentation

Making the estate legible to people who did not build it.

Column-level lineageData dictionary and catalogModel documentationOnboarding material for new analysts

Access and ownership model

Knowing who can see what, and who is accountable for each domain.

Role and permission designDomain ownership modelSensitive data classificationAccess review cadence

The stack we build with

Tooling our team runs in production

Frameworks, platforms, and infrastructure we implement, integrate, and operate end to end.

Warehouses

SnowflakeBigQueryDatabricks

Transformation and testing

dbt

Semantic layer and catalogue

OmniLookerCube

Quality and observability

ElementaryMonte CarloSoda

What changes

Outcomes you can point to

One governed definition per metric with a named owner.
Tests that fail loudly before bad data reaches a decision.
Traceable lineage from dashboard figure back to source system.
A documented ownership and access model that survives staff turnover.
Governance that lives in the stack rather than in a document nobody opens.

Proof in production

See where this work shows up.

Clean data for smarter shipping ops

Stella Source consolidated quoting, CRM, and operational data into a single governed source of truth with role-based dashboards, reaching 95% cost-estimation accuracy.

Read the single source of truth case study →

How we saved a retailer hundreds of thousands

We consolidated four carrier and shipment data sources into one governed warehouse model and analysed 100% of historical shipments.

Read the governed warehouse model case study →

Common questions

Data Governance Consulting, straight answers

What does data governance consulting cost?

Engagements start with a scoped assessment that produces a definitions audit, an ownership model, and a prioritised enforcement plan. Most governance programmes fail on scope, so we deliberately start narrow and sequence from the disputes that cost the most.

Do we need a governance platform or a catalogue tool?

Usually not first. A catalogue makes an existing agreement visible, it does not create one. We agree definitions and ownership first, then recommend tooling only where something needs enforcing and cannot be enforced in the stack you already run.

How is this different from writing a data governance policy?

A policy is a document. Governance that works is encoded: definitions in a semantic layer, quality rules as tests, ownership in roles, lineage in metadata. If it cannot fail loudly, it will not change behaviour.

Can you work with our existing dbt project?

Yes. dbt is the most common place we encode governance, because tests, documentation, and model contracts give you enforcement without buying another platform.

How do you handle disagreements between departments over definitions?

We run the definition workshop with the people who actually disagree, force one decision per metric, and record the rationale. Where two teams genuinely need different measures, we name them differently rather than letting one word mean two things.

Is this only worth doing at enterprise scale?

No. Smaller teams usually feel the pain sooner, because there is no analyst layer absorbing the reconciliation work. Governance is often cheaper to fix at 30 people than at 300.

How we work