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Data Governance Operating Model: Owners, Data Products, Quality KPIs, and a Weekly Cadence

Data governance operating model: owners, data products, quality KPIs, and a weekly cadence
Data governance operating model: owners, data products, quality KPIs, and a weekly cadence

 

 

 

Quick answer

Data governance works when it has named owners and measurable quality targets. Define data products, assign accountable owners, and run a weekly cadence that clears defects and prioritizes fixes. Keep the KPI set small: completeness, accuracy, timeliness, and business usage.

What is a data governance operating model?

A data governance operating model is the practical system that makes governance happen: decision rights, roles, forums, and routines. It answers who owns which data products, who resolves quality defects, how standards are set, and how work gets prioritized each week.

If governance feels like meetings without outcomes, the operating model is missing one of these: named owners, a defect workflow, or KPIs with targets.

Sources: [S2], [S8], External:
Microsoft Purview,
Collibra

Internal support:
digital and technology,
business transformation,
performance improvement.

Common searches: template, example, PDF, framework

Teams often search for: data governance operating model template, data governance operating model example, data governance operating model PDF, and data governance operating model framework. This page includes copy/paste templates and an example you can export to PDF.

Data governance models: centralized, decentralized, federated

There are three common data governance models. Pick one based on how your business is structured, not on tooling. Most organizations end up with a federated data governance model: shared standards with domain ownership.

Model When it fits What it looks like Main risk
Centralized data governance model Small org, single domain, or early-stage standardization A central team defines standards and resolves most issues Slow fixes, low domain ownership
Decentralized data governance model Strong domain autonomy, many business units Each domain runs its own governance and tooling choices Inconsistent definitions and duplicated work
Federated data governance model Most enterprises with shared customers, finance, and risk needs Domains own data products; a council sets shared standards and escalation Unclear decision rights if the council is weak

A simple data governance model example: a customer domain owns the Customer 360 data product, while a council sets shared definitions for customer status, opt-in, and identity resolution rules.

Sources: [S3], [S6], External:
Martin Fowler (data mesh principles),
EDM Council DCAM

Data governance roles and responsibilities

Governance succeeds when roles are simple, named, and tied to outcomes. Start with a council for decisions, owners for accountability, and stewards for day-to-day execution.

Role Accountable for What they do weekly What they decide
Data governance council Standards, priority, escalation Review KPI trends, unblock cross-domain issues Definitions, priority order, exception approvals
Data product owner Value and usability of a data product Prioritize backlog, confirm consumer needs Roadmap, acceptance checks, release readiness
Data owner Policy, risk, and accountability for a data domain Approve changes with risk impact, sponsor fixes Access rules, retention, quality targets
Data steward Definitions and quality execution Triage defects, manage definitions, coordinate fixes Operational calls within standards
Data custodian (IT) Platforms, pipelines, and controls Implement fixes, monitor jobs, enforce controls Technical implementation choices

Sources: [S2], [S8], External:
Microsoft Purview,
Collibra

Data owner vs. steward

Data owner vs. steward is the most common role confusion. A simple rule: the data owner is accountable for the outcome and risk. The data steward is responsible for the day-to-day work that achieves that outcome.

Item Data owner Data steward
Accountability Accountable for quality targets and policy compliance Responsible for execution and coordination
Decisions Approves standards, exceptions, and access rules Maintains definitions and resolves routine issues
Measures Owns KPI targets and escalation thresholds Owns defect queue health and time-to-fix

Sources: [S2], External:
Microsoft Purview roles

Data products and data product ownership

Data governance becomes measurable when you govern data products, not abstract datasets. A data product should have a clear consumer, an owner, acceptance checks, and published definitions.

Data product charter (copy/paste)

Data product name:
Business owner (accountable):
Data product owner (responsible):
Primary consumers:
Use cases supported:
Source systems:
Key entities and definitions:
Quality KPIs and targets (with thresholds):
Access rules and approvals:
Release cadence:
Known risks and dependencies:

Sources: [S3], [S4], External:
Martin Fowler (designing data products),
Martin Fowler (data mesh principles)

Data quality KPIs that actually drive fixes

Keep the KPI set small and action-oriented. If a KPI does not lead to a fix, it becomes reporting noise. Start with completeness, accuracy, timeliness, and usage. Add only when you have owners who can act.

KPI Definition Example target Owner
Completeness Required fields present for the use case 99% complete for priority attributes Data steward
Accuracy Correct values compared to a trusted reference or rule set 98% rule pass rate on sampled records Data owner
Timeliness Data available within the agreed window 95% of runs delivered by 8:00 AM local time Data custodian
Business usage Active consumption by agreed teams and reports Top 5 dashboards use governed fields Data product owner

Sources: [S5], [S7], External:
ISO/IEC 25012,
ISO/IEC 25024

Weekly cadence: what happens every week

A weekly cadence turns governance into outcomes. Keep meetings short and tie every agenda item to either a KPI movement, a defect, or a decision needed.

Weekly cadence agenda (copy/paste)

1) KPI review (10 minutes)
- Completeness, accuracy, timeliness, usage
- What changed since last week and why

2) Defect triage (15 minutes)
- New defects, severity, impacted consumers
- Owner assignment and due dates

3) Fix prioritization (15 minutes)
- Top 5 fixes to ship next
- Dependencies and blockers

4) Decisions and exceptions (10 minutes)
- Definition conflicts
- Access exceptions
- Standards changes

5) Next week plan (5 minutes)
- Owners, dates, and what "done" means

Sources: [S8], External:
Collibra

Operating model template, example, and PDF-ready pack

Use the templates below as your data governance operating model template and framework. If you need a data governance operating model PDF, export this section and the KPI tables as a single pack.

One-page operating model (copy/paste)

Operating model goal:
Priority data products (top 5):
Data governance model (centralized, decentralized, federated):
Decision forums:
- Council (who, how often, what it decides):
- Domain working group (who, how often, what it ships):
Roles (named people):
- Council chair:
- Data owners:
- Data stewards:
- Data product owners:
- Data custodians:
KPIs and targets:
- Completeness:
- Accuracy:
- Timeliness:
- Usage:
Issue workflow:
- Intake channel:
- Severity levels:
- SLA targets:
- Escalation path:
Standards:
- Definitions location:
- Metadata requirements:
- Access approvals:

Data governance operating model example (short)

Example: Customer domain

Data product: Customer 360
Accountable data owner: VP Customer Operations
Data product owner: Customer Analytics Lead
Steward: CRM Data Steward
Custodian: Data Platform Team Lead

KPIs:
- Completeness: 99% for email, phone, consent status
- Accuracy: 98% rule pass rate on identity and address rules
- Timeliness: daily refresh by 8:00 AM local time
- Usage: top 3 sales dashboards use governed customer status

Weekly cadence:
- 45-minute working session to clear defects and ship fixes
Council:
- Monthly 60-minute review for standards, exceptions, and funding decisions

PDF-ready pack outline (copy/paste)

1) One-page operating model summary
2) Data products list (owners, consumers, release cadence)
3) Roles and responsibilities (owner vs. steward RACI)
4) Data quality KPIs (definitions, targets, thresholds)
5) Defect workflow (intake, SLAs, escalation)
6) Council charter (decisions, quorum, exceptions)
7) 30, 60, 90 improvement plan

Sources: [S2], [S3], [S5], External:
Microsoft Purview,
Martin Fowler,
ISO/IEC 25012

FAQ

What is the operating model in data governance?

It is the set of decision rights, roles, and routines that make governance work every week. It defines data products, assigns owners, tracks KPIs, and runs a cadence that clears defects and prioritizes fixes.

What is a governance operating model?

A governance operating model is how decisions are made and executed: who approves standards, who implements changes, and how issues escalate when teams disagree.

What are the 5 C’s of data governance?

Teams define these differently. A practical set is: clarity, controls, consistency, cadence, and compliance. Use the 5 C’s to check whether your operating model can produce outcomes.

What are the 5 pillars of data governance?

A practical set is: ownership, quality, access, standards, and change. If you cannot name the owner and the KPI for a pillar, it will not move.

If you want a data governance operating model tailored to your domains and data products:
contact NMS Consulting.

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