Domain identification
Identify customer, member, supplier, product, asset, location or reference domains that create material risk or value.
- Priority domains are agreed
- Source systems and consumers are known
- Business impact is documented
Consistency across shared data
Master and Reference Data Management
Create consistency across customer, member, supplier, product, asset and reference data reused by systems, processes and reports.
Evidence-led deliveryEvidence into action.
Challenge
When to use this service.
Customer, member, supplier, product or operational information is fragmented across multiple platforms.
Decision-maker insight
Master and Reference Data Management in plain terms.
Master and reference data management creates consistency for the shared business data reused across systems, processes and reports. It reduces duplication and helps teams agree which record, hierarchy or value should be trusted.
Management framework
Master and reference data framework
The work should define priority domains, source systems, ownership, golden-record requirements, hierarchies, matching rules and controls.
Matching and golden records
Define matching rules, survivorship logic and golden-record requirements.
- Duplicate logic is transparent
- Golden-record rules are approved
- Exceptions are governed
Ownership and hierarchy
Assign domain ownership and maintain hierarchies, reference values and change controls.
- Owners approve domain standards
- Hierarchy rules are documented
- Reference data changes have workflow
Synchronisation and quality
Control how master and reference data synchronises across systems, reports and processes.
- Synchronisation requirements are clear
- Quality rules monitor critical attributes
- Controls are reviewed through governance
Lifecycle
Master data improvement lifecycle
Master data work moves from domain selection to source analysis, rule design, controls and ongoing governance.
Select domains
Choose shared data domains that matter most for operations, reporting, service or risk.
Evidence: Domain assessment, business impact view and stakeholder list.Analyse sources
Review source systems, duplicates, values, hierarchies, data flows and consumers.
Evidence: Source-system analysis, profiling output and duplicate findings.Define rules
Document matching, survivorship, golden-record, hierarchy and reference data requirements.
Evidence: Matching rules, golden-record requirements and reference data standards.Design controls
Agree ownership, change workflow, quality controls and synchronisation expectations.
Evidence: Ownership model, control framework and workflow design.Roadmap delivery
Sequence domain improvements, platform changes, data quality remediation and adoption.
Evidence: Implementation roadmap, issue backlog and performance measures.Engagement scope
What we can cover.
Master data maturity assessment
Data domain identification
Source-system analysis
Record matching and duplicate analysis
Golden-record requirements
Master data ownership
Hierarchy management
Reference data standards
Synchronisation requirements
Data quality controls
Master data governance
Implementation roadmap
Deliverables
Outputs your teams can use.
Domain assessment
Golden-record requirements
Matching rules
Ownership model
Control framework
Roadmap
Expected outcomes
What improves.
Consistent shared business data across systems and reports
Reduced duplication, reconciliation effort and conflicting information
Clear ownership and control of master and reference data domains
Decision guide
Test readiness before you invest.
Distinguish embedded capability from disconnected activity.
Leadership questions
- Which shared data domains create the most duplication or reconciliation effort?
- Which system or rule determines the trusted record?
- Who owns hierarchy, reference values and domain-level quality?
- What controls prevent duplicate or inconsistent records entering systems?
- Which changes are required before migration, reporting or AI work can succeed?
Signals of maturity
- Priority domains and source systems are documented.
- Golden-record and matching rules are approved.
- Reference data changes follow workflow and ownership.
- Quality controls monitor shared data attributes.
- Roadmaps sequence domain improvement by value and risk.
Evidence to prepare
- Source-system inventory and data flow diagrams
- Duplicate analysis, matching logic and reconciliation examples
- Current hierarchies, reference values and code lists
- Ownership model, change workflow and control documents
- Migration plans, reporting issues and master data platform requirements
Process
From evidence to implementation.
01
Identify master data domains, source systems and reuse patterns
02
Profile duplicates, hierarchies, reference values and inconsistencies
03
Define ownership, golden-record, matching and synchronisation requirements
04
Design controls for quality, change and reference data management
05
Sequence implementation through a domain roadmap
Related training
Data Quality Practitioner
Build the role capability needed to sustain the change.
View training routeResource
Critical Data Element Template
Prepare the evidence for a productive first conversation.
Browse insightsScope 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.