Roles, routines and controls

Data Operating Model

Design how data work gets organised across central teams, business areas, technology, risk and operations.

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A data governance evidence workspace with scorecards and operating-model documents.

Evidence-led deliveryEvidence into action.

Challenge

When to use this service.

Responsibilities are split across teams, but the roles, routines and measures needed for joined-up data management are unclear.

Decision-maker insight

Data Operating Model in plain terms.

A data operating model explains how data work gets done. It defines where authority sits, how central and domain teams collaborate, which routines keep work moving and how controls are embedded into delivery and operations.

Management framework

Operating model framework

The model should clarify structure, responsibilities, routines, controls and measures so people know how to act when data decisions or issues arise.

Model shape

Choose the right centralised, federated or domain-led pattern based on risk, scale, capability and business ownership.

Design choice is linked to business reality
Central and domain responsibilities are clear
The model can scale without bottlenecks

Roles and responsibility matrix

Define accountabilities for owners, stewards, custodians, product owners, governance leads and delivery teams.

Responsibilities cover recurring decisions and processes
Role profiles include authority and time commitment
Skills and capacity gaps are visible

Routines and forums

Establish the meeting cadence, service routes, escalation paths and workflows that make the model operational.

Forums have decision rights and inputs
Issue and change routes are documented
Teams know when to escalate

Controls and measures

Embed quality, privacy, security, metadata, access and policy controls into everyday work.

Controls map to processes and systems
Performance measures show adoption
Assurance evidence is easy to retrieve

Lifecycle

Operating model design lifecycle

An operating model should be designed, piloted and improved, not announced as an organisation chart.

Diagnose

Understand current work patterns, pain points, decision delays and informal ownership.

Evidence: Current-state map, stakeholder interviews, issue examples and process inventory.

Choose model

Select a central, federated or hybrid design and define why it fits the organisation.

Evidence: Option assessment, design principles and decision paper.

Design roles

Create role profiles, responsibility matrix, forums, service routes and escalation paths.

Evidence: Responsibility matrix, role descriptions, forum terms and workflow maps.

Pilot

Test the model with a domain, process or data product before wider rollout.

Evidence: Pilot plan, adoption feedback, decision log and lessons learned.

Scale and improve

Roll out the model with training, measures, controls and periodic review.

Evidence: Implementation plan, training records, scorecard and improvement backlog.

Engagement scope

What we can cover.

Target operating model design

Central, federated or domain-led options

Roles and responsibility mapping

Governance and architecture considerations

Skills and capacity assessment

Performance measures and adoption routines

Deliverables

Outputs your teams can use.

Target operating model

Responsibility matrix

Role profiles

Governance cadence

Implementation and adoption plan

Expected outcomes

What improves.

A practical model for day-to-day data work

Reduced duplication between teams

Clear routes for issue resolution and improvement

Decision guide

Test readiness before you invest.

Distinguish embedded capability from disconnected activity.

Leadership questions

Which data decisions currently stall because roles are unclear?
What should be owned centrally and what should sit with business domains?
Do owners and stewards have enough authority, time and support?
How do technology, risk, privacy and operations participate in data work?
Which measures will show that the operating model is adopted?

Signals of maturity

People understand where to take data questions and issues.
Roles include authority, time commitment and expected outputs.
Forums make decisions and remove delivery blockers.
Controls are embedded into process and system change.
Training and measures support adoption after launch.

Evidence to prepare

Current organisation charts, role descriptions and team mandates
Data governance forum notes, issue logs and escalation examples
Process maps for reporting, data change, access and issue management
Known duplication, bottlenecks or gaps between teams
Training needs, capacity constraints and target domains for a pilot

Process

From evidence to implementation.

Map current ownership, delivery patterns, capability and constraints

Select the right operating-model option for the organisation

Design roles, forums, processes, controls and performance measures

Pilot the model before wider rollout

Embed adoption through training, reporting and review cycles

Related training

Data Governance Practitioner

Build the role capability needed to sustain the change.

View training route

Resource

Data Owner Role Profile

Prepare the evidence for a productive first conversation.

Browse insights

Scope note

Evidence first, claims second.

No claims of certification, approval or compliance without evidence.

Ready to move?

Turn data risk into a clear next step.

Start with a focused discovery call or readiness assessment.

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