Enterprise data governance
Governance is what makes a number defensible. We help enterprises define, measure and maintain trust in their data — assessed against DAMA standards, then implemented in phases.
Why governance
Dashboards, models and AI answers inherit the credibility of the data under them. Governance turns trust into something you can measure and evidence.
01
One meaning per business term, one named owner.
02
A quality score published next to the data.
03
Any number walked back to its source in minutes.
04
A council that keeps deciding. Not a one-off project.
The symptoms
Each one is a governance failure showing up as a technology symptom — a cost paid daily, in small amounts, by people who never file a ticket.
Three teams quote three revenue numbers — and all three can defend theirs.
Every important report starts with a reconciliation nobody has time for.
No named owner for a table, so no one can approve a change to it.
Analysts re-verify the same figures before every executive meeting.
An audit request takes weeks of manual tracing across systems.
The AI initiative stalls on one question: can we trust the grounding data?
What governance consists of
Each block answers one question. If a block is missing, the business finds out for you.
Is the number right?
Rules on critical data elements, measured continuously and published as a score.
Where did it come from?
A catalogue of what exists, plus a column-level trace from source to report.
Do we mean the same thing?
One definition and one golden record for customer, product and vendor.
Do we know before they do?
Freshness, volume and schema-drift monitoring that alerts a named owner.
Can we prove who saw what?
Classification, sensitivity labels, masking and auditable access reviews.
Does meaning survive change?
Conformed dimensions, naming standards and a target-state architecture.
Operating model
Council · Charter · Policies · Standards · Owners · Stewards · Issue and exception workflow
Tooling accelerates all six. It replaces none of them.
DAMA-based assessment
The method
11 areas · 0–5
Each DAMA knowledge area is scored per business unit, then rolled up. A score only counts if an artefact backs it.
The target
Level 3–4
Level 3 as the enterprise baseline, Level 4 where business risk sits. Set by risk, not uniformly.
The outcome
A clear plan
Assessed scores per business function
Systematic DAMA scores at business-function level, each with the reasoning and evidence behind it.
Governance gaps per function
Where each function falls short of its target, and what that gap costs.
Detailed plan to higher maturity
A prioritised, sequenced roadmap to close the gaps and reach target maturity.
Our offering
Tooling without an operating model becomes shelfware. An operating model without tooling becomes a policy nobody can follow. We deliver both.
Stage one
Maturity established by evidence at business-unit, technology and enterprise level, with critical gaps sequenced into a fundable roadmap.
Stage two
Track A · Technology
Catalog — scan, classify, describe Lineage — source to report Data quality — rules and scores MDM — golden records Observability — freshness and driftTrack B · Process
Council — decision rights, cadence Policies — access, retention, quality Guidelines — naming and modelling SOPs — issue and exception workflow Roles — owners and stewardsThe assessment roadmap becomes the implementation backlog.
Our approach
Sequencing is the risk control: an enterprise-wide launch on day one is the most common way governance programmes stall.
Stage 1 · How the assessment runs
01
Stakeholders and business units identified; scope of governance agreed.
02
Current state across business functions and the technology landscape, with evidence.
03
Maturity workshops, then findings reviewed back with the people who gave them.
04
Results consolidated; critical gaps sequenced into an implementation plan.
Stage 2 · How implementation is delivered
1
One or two priority business units, for quick wins and credibility.
2
Run the model on real work and refine the processes against reality.
3
Remaining business units, then enterprise-wide functions.
Timelines vary with organisation size, estate complexity and starting maturity.
Our work
Case study · Maturity assessment
Scored against DAMA standards across business functions through evidence-backed stakeholder workshops, separating enterprise gaps from business-unit gaps.
Outcome
A phased three-year roadmap, sequenced and agreed by the sponsors who had to fund it.
Case study · Governance implementation
Charter and policies defined and a council stood up; catalog, lineage and quality enabled; stewardship roles and exception workflows embedded in delivery.
Outcome
Trust and usability of enterprise data measurably improved, tracked by adoption of certified assets.
Data governance assessment
In the first assessment phase, you get an evidence-backed view of your data governance maturity and a roadmap ready to fund: