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DG · Data Governance

Governance that people actually operate

We design data governance operating models that fit how your organisation really makes decisions — with clear accountability, proportionate controls and metrics that show progress quarter by quarter.

The challenge

Most organisations already have a data governance framework on paper. The problem is that it rarely survives contact with the business.

  • Data owners are named but cannot say which data they own or what they are accountable for.
  • Councils meet, minute and disband because there is no decision for them to make.
  • Critical data elements are undefined, so effort is spread thinly across thousands of attributes.
  • Leadership cannot see whether governance is improving anything, so funding becomes fragile.

What we deliver

Data Governance

Operating model design

Federated, centralised or hybrid — we design the model around your decision rights, budget lines and existing risk committees, not a template.

Roles and RACI

Role definitions for owners, stewards, custodians and the CDO office, with a RACI mapped to real processes such as onboarding a new data source or changing a report.

Councils and forums

Terms of reference, escalation paths and agenda templates for a data council that makes decisions, and working groups that resolve issues.

DAMA-DMBOK maturity assessment

A structured baseline across the eleven DMBOK knowledge areas, scored on evidence rather than self-assessment, with a prioritised gap analysis.

Critical data elements

A method to identify and agree the 50–300 data elements that drive regulatory reporting, customer outcomes and financial risk — and govern those first.

Governance KPIs

A small set of leading and lagging indicators — ownership coverage, issue ageing, policy attestation, quality trends — reported to the executive.

Outcomes you can measure

Outcomes you can measure

90 days

to a ratified operating model and named owners for every critical data element

+1.5

typical uplift in DMBOK maturity score across priority knowledge areas within 12 months

<30 days

median ageing of data issues once escalation paths are in place

Related case study · Illustrative engagement

Evidencing BCBS 239 through end-to-end lineage

A systemically important European bank · Europe

92%

lineage coverage for risk reports

Supervisory findings on risk data aggregation were closed by tracing 240 critical data elements from source to the liquidity and credit risk reports.

Read the case study

FAQ

Frequently asked questions

How long does it take to stand up a data governance operating model?

A workable operating model — roles, RACI, council terms of reference and a first set of critical data elements — typically takes 10 to 14 weeks. Embedding it across business lines usually runs for a further 6 to 12 months.

Should data governance sit in IT, risk or the business?

Accountability should sit with the business, coordination with a CDO office, and execution shared with technology. We help you choose where the CDO office reports based on your regulatory exposure and decision culture.

Do we need a tool before we start governance?

No. Tools accelerate a working model; they do not create one. We usually define the model and critical data elements first, then select or configure tooling to support them.

How do you measure governance maturity?

We use a DAMA-DMBOK-aligned assessment scored on evidence — policies, artefacts, controls in operation — rather than interviews alone, so scores are repeatable year on year.

What are critical data elements and why do they matter?

Critical data elements are the subset of data that materially drives regulatory reports, financial statements or customer outcomes. Governing them first focuses scarce steward time where risk is highest.

Discuss your priorities with a practitioner

A 45-minute working session, no sales deck. You will leave with at least three actionable recommendations.

Book a consultation Email us: info@crestqa.co