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Four phases, one exit criterion each.
Applied consistently, so you always know which phase you are in and what has to be true before the next one starts.
01 — Assess
We map the current state against a capability model — data landscape, ownership, tooling, and maturity — and interview the people who would have to operate whatever model you end up with.
Exit criterion: A written gap assessment and a prioritised roadmap, with the uncomfortable findings left in.
02 — Design
Operating model, standards, and technical architecture, sequenced against business outcomes. Roles and decision rights get tested against real scenarios before anyone signs off on them.
Exit criterion: An approved target operating model and architecture. Nothing gets built before this is agreed.
03 — Implement
Hands-on delivery of rules, catalogs, lineage, and workflows. Your stewards are in the work from the first iteration rather than trained at the end of it.
Exit criterion: Working controls in production, with your team having operated them alongside us.
04 — Operationalize
Transition to business as usual: documentation, runbooks, KPI reporting, and the meeting cadence that keeps governance live once the engagement closes.
Exit criterion: A program your team runs without us, and the evidence pack for the next examination.
What an engagement usually looks like
Most engagements open with a short assessment phase rather than a full proposal built on assumptions. That keeps the initial commitment small while both sides work out whether the fit is right and what the real problem is.
From there, work runs as a fixed-scope engagement with defined deliverables, or on a time-and-materials basis where the shape of the work is still emerging. Longer governance programs often mix the two — fixed-scope for design, time-and-materials for the implementation that follows.
What we ask of you
Data governance programs succeed or fail on access and sponsorship. Practically: an executive sponsor who can resolve ownership disputes, availability from the business stewards who know what the data actually means, and enough system access to profile real data rather than reason about documentation.
Where those are missing, we will say so early. A program launched without them produces artifacts instead of outcomes, and neither of us benefits from that.