Duplicate or Conflicting Entities
The same customer, supplier, product or location exists under multiple identifiers, formats or source-system definitions.
Data Governance · Master Data
Build Trusted Golden Records with Master And Reference Data Management
DataConsultant helps organisations design and implement the governance, data models, matching logic, stewardship workflows, hierarchies, reference-data controls and integration patterns needed to create dependable master records across customers, products, suppliers, locations and other core domains.
Authoritative-source and ownership rules defined
Matching, deduplication and survivorship designed
Reference values and hierarchies governed
Golden records distributed with accountable change control
Scope can cover advisory, operating-model design, platform requirements, implementation support or a phased combination. Timeline and pricing are confirmed after scoping.
Illustrative mastering pattern. The target model, matching approach and platform architecture are tailored to the client environment.
One Governed Identity
Define how records are matched, merged and represented across systems.
Clear Ownership
Assign decision rights, steward actions and approval responsibilities by domain.
Controlled Hierarchies
Manage relationships, classifications and reference values with traceable change.
Reliable Distribution
Publish trusted master and reference data to operational, analytical and AI consumers.
When Fragmented Master Data Starts Creating Business Friction
Master-data problems usually surface as operational exceptions, integration failures, inconsistent reporting, duplicated entities or manual reconciliation. The engagement focuses on the underlying ownership, rules, data model and process causes.
Inconsistent Reference Values
Codes, classifications, statuses and hierarchies differ across applications, making integration and aggregation unreliable.
Unclear Source Authority
Teams disagree about which system or owner may create, change, approve or retire critical attributes and records.
Transformation Dependency
ERP, CRM, cloud, M&A or AI programmes need clean identifiers, crosswalks and governed masters before migration or scale.
Master Data and Reference Data Solve Different Parts of the Same Control Problem
Both require agreed definitions, accountable ownership and controlled change, but the design needs to recognise the difference between durable business entities and shared code sets.
Master Data
Core entities that are reused across business processes and systems, such as customers, products, suppliers, locations, assets, employees, parties and legal entities.
- Entity identifiers and canonical attributes
- Matching, identity resolution and deduplication
- Survivorship and golden-record rules
- Relationships, hierarchies and lifecycle states
Reference Data
Controlled value sets used to classify or describe data consistently, including codes, statuses, categories, units, geographies and other enterprise lookup values.
- Canonical code sets and descriptions
- Crosswalks and source-to-target mappings
- Value hierarchies and effective dates
- Approval, versioning and distribution controls
Identify the Master-Data Decisions Blocking Your Programme
Share the priority domains, source systems and business outcomes. We can help determine whether you need an assessment, target model, platform design or implementation support.
What a Well-Governed Mastering Capability Changes
The goal is not simply a cleaner database. It is a durable operating capability that gives business and technology teams a consistent way to identify, govern and reuse critical entities and reference values.
Consistent Entity Recognition
Applications and teams can reconcile the same real-world entity through governed identifiers, crosswalks and match rules.
Traceable Change
Create, update, merge, retire and hierarchy changes follow documented ownership, approvals and exception paths.
Cleaner Data Exchange
Consumers receive consistent master attributes, codes and relationships through agreed distribution and synchronisation patterns.
More Reliable Aggregation
Reporting, analytics and AI can group and interpret core entities against more consistent business definitions and hierarchies.
From Source Records to a Governed Mastering Model
The target design connects source authority, standardisation, matching, survivorship, stewardship and distribution. The precise pattern depends on whether the organisation needs registry, consolidation, coexistence, central governance or a hybrid approach.
1. Source & Domain Inputs
Establish what each source contributes and which business domain owns the meaning.
Source-system inventory
Attribute authority
Identifiers & crosswalks
Quality profile
2. Mastering & Governance
Create the controls that determine which records belong together, which values survive and who resolves exceptions.
Standardise & validate
Match, merge & identity resolution
Survivorship & golden-record logic
Stewardship, hierarchy & workflow
3. Publish & Operate
Distribute mastered data with controls for version, lineage, exceptions and service ownership.
APIs, events or batch syndication
Reference-value distribution
Downstream reconciliation
Monitoring & issue management
Master And Reference Data Management Scope
The work can begin with strategy and design, extend into platform implementation, or focus on one priority capability. Scope is selected around business risk, value, transformation dependencies and operating maturity.
MDM Strategy & Domain Prioritisation
Define objectives, scope, principles and an adoption sequence.
- Business case and success measures
- Domain prioritisation
- Target capability roadmap
Canonical Data Model
Design the entities, attributes, identifiers and relationships required for shared use.
- Domain and entity model
- Attribute definitions
- Relationship and hierarchy rules
Matching & Golden Records
Specify how duplicates are detected and the best available representation is determined.
- Standardisation and match rules
- Survivorship priorities
- Manual review and exceptions
Reference Data & Hierarchies
Control shared value sets, crosswalks and enterprise classification structures.
- Reference code sets
- Mappings and effective dates
- Hierarchy governance
Operating Model & Stewardship
Translate data ownership into practical create, change, approve and resolve workflows.
- Owner and steward roles
- Decision rights and RACI
- Issue and exception workflow
Integration & Syndication
Define how mastered data moves between producers, the MDM layer and consumers.
- Inbound and outbound interfaces
- API, event or batch patterns
- Reconciliation and error handling
Controls, Quality & Auditability
Build quality, access, approval and monitoring requirements around critical master data.
- Quality rules and thresholds
- Access and segregation controls
- Change evidence and monitoring
Migration & Implementation
Prepare the data, configuration, testing and transition needed to operationalise the design.
- Migration and cutover approach
- Configuration and testing
- Handover and knowledge transfer
Common Domains and Use Cases
Most organisations get better control by prioritising a small number of high-value or high-risk domains first, then extending the model after ownership, quality and integration patterns are proven.
Customer & Party
Resolve identities, household or organisation relationships and customer crosswalks.
Product & Material
Align product identifiers, attributes, categories, variants and material structures.
Supplier & Vendor
Create governed supplier identities, onboarding attributes and relationship structures.
Location & Asset
Manage location, facility, equipment or asset hierarchies and persistent identifiers.
Legal Entity & Organisation
Govern organisation relationships, legal entities, business units and reporting structures.
Reference Data
Control enterprise codes, statuses, classifications, crosswalks and value hierarchies.
Choose the Right Domains Before Choosing the Tooling
Define the entities, source authority, matching requirements, workflows and distribution needs first so platform decisions are tied to measurable business requirements.
Typical Deliverables
Deliverables are selected according to whether the engagement is advisory, design-led, implementation-focused or a combined programme. The emphasis is on assets that can be governed, configured, tested and operated.
01
Current-State Assessment
Domain, source, quality, ownership, process and platform findings.
02
Canonical Model
Entities, attributes, identifiers, relationships and domain definitions.
03
Match & Survivorship Rules
Standardisation, duplicate resolution, golden-record and exception logic.
04
Reference & Hierarchy Model
Code sets, mappings, value hierarchies, versions and change rules.
05
Ownership & Stewardship RACI
Data owners, stewards, approvers, platform roles and decision rights.
06
Integration Blueprint
Inbound, outbound, synchronisation, error and reconciliation patterns.
07
Control & Quality Set
Validation, access, approval, auditability, monitoring and issue controls.
08
Implementation Backlog
Prioritised configuration, migration, testing, rollout and adoption work.
09
KPI & Monitoring Framework
Measures for quality, duplicates, workflow, stewardship and distribution health.
10
Phased Roadmap
Domains, dependencies, decisions, owners and mobilisation sequence.
How the Engagement Moves from Evidence to Operational Control
The process follows the decisions that must be made: what to master, which source is authoritative, how identities are resolved, who governs change, how trusted records move and how the capability is sustained.
01 Scope
Prioritise Domains
Agree business outcomes, boundaries, stakeholders and decision criteria.
02 Discover
Profile the Current State
Review sources, duplicates, definitions, ownership, integrations and quality evidence.
03 Model
Design the Canonical View
Define entities, identifiers, attributes, relationships, codes and hierarchies.
04 Master
Set Match & Survivorship
Specify standardisation, identity resolution, merge, golden-record and exception logic.
05 Govern
Assign Decision Rights
Design owners, stewards, approvals, issues, controls and operating cadence.
06 Connect
Plan Migration & Syndication
Map inbound, outbound, cutover, reconciliation and downstream dependencies.
07 Operate
Measure & Improve
Mobilise rollout, monitoring, stewardship measures, handover and improvement backlog.
What We Need From Your Team
Evidence That Shows How Master Data Is Really Created and Used
Good MDM decisions require business and technical evidence, not only a tool inventory. Missing evidence can be recorded as a gap and resolved through discovery rather than assumed.
Useful principle: start with a small set of priority domains, real source records and real business decisions so matching, ownership and workflow requirements can be validated before scale.
Technology, Standards and Control Considerations
The design should fit the client’s existing landscape and operating model. Platform capability is evaluated against the required domains, match logic, workflow, integration, scale and controls rather than treated as the starting assumption.
MDM Platforms
Existing or planned capabilities may include SAP Master Data Governance, Informatica MDM and 360 Applications, Reltio Multidomain MDM or another fit-for-purpose platform.
Integration
ERP, CRM, PIM, data platforms, APIs, event streams, batch interfaces and downstream reconciliation all influence the mastering pattern.
Access & Change Control
Role separation, approval, sensitive attributes, audit evidence and lifecycle controls are designed around domain risk and policy requirements.
Standards Context
Where characteristic master data is exchanged across systems or organisations, ISO 8000-110:2021 can be considered as a relevant reference for syntax, semantic encoding and conformance to data specifications.
Operational Monitoring
Measure duplicate rates, match exceptions, stewardship queues, quality rules, distribution failures, reference changes and unresolved issues.
Vendor-neutral by default. DataConsultant can work within a mandated technology or support platform evaluation. No software partnership, certification or vendor endorsement is implied by naming technology examples. Third-party licences and cloud consumption are separate from consulting fees unless explicitly included in a scoped proposal.
Turn the Target Model into an Implementable MDM Rollout
Connect data design with stewardship, platform configuration, migration, integration, testing and operating ownership so the capability can survive beyond go-live.
Fit, Boundaries and Scope Decisions
A clear boundary prevents an MDM programme from becoming an open-ended data-cleanup initiative. The service should focus on the master domains, decisions and operating controls that materially affect the target business processes.
A Strong Fit When
- Multiple systems represent the same core entities differently.
- ERP, CRM, M&A or cloud transformation depends on consistent master records.
- Business teams need explicit ownership and approval for critical attributes.
- Duplicate resolution, hierarchy management or reference-data control is recurring.
- Analytics or AI needs dependable entity identities and relationships.
Not Automatically Included
- Third-party software licences, cloud usage or vendor subscription costs.
- Enterprise-wide cleansing of every record when only design or advisory is scoped.
- Legal advice, regulatory certification or statutory audit.
- Full application migration, integration build or long-run managed operations unless commissioned.
- Transactional, event or analytical data redesign unrelated to master/reference scope.
Commercial Model
Custom Scope & Pricing for Master And Reference Data Management
Pricing is confirmed after discovery and scope confirmation. The written estimate reflects the actual domains, source and consuming systems, data condition, matching logic, stewardship workflow, integration, migration and implementation coverage.
Timeline: confirmed after scoping. Complexity is driven by domains, systems, data condition, matching logic, governance approvals, integration, migration and implementation coverage.
Request a scoped proposal
Provide the priority domains, current platforms, transformation context and required outputs. The proposal can separate advisory and implementation work from third-party software or cloud costs.
Why Use DataConsultant for Master and Reference Data Management?
The service is structured to connect governance decisions with architecture, data quality, implementation and ongoing operation without assuming that a new technology purchase is the answer.
Business Ownership First
Mastering rules are tied to accountable domain owners, stewards, business processes and exception decisions—not left solely with a technical team.
Architecture-to-Operation Continuity
The target model connects canonical data, matching, reference values, workflows, integration, controls and monitoring so design assets can be operationalised.
Governance by Design
Ownership, quality, privacy, security, auditability and change control are considered as part of the operating model rather than added after implementation.
Requirements-Led Technology Advice
Existing and planned platforms are assessed against business and operating requirements, with vendor-neutral guidance unless a specific technology is mandated.
Practical Delivery Assets
Outputs are designed for decision-making, configuration, testing, migration, stewardship and rollout rather than remaining as high-level presentation material.
Implementation & Knowledge Transfer
Support can extend through implementation and handover so internal teams understand the rules, controls, ownership and improvement backlog they must sustain.
Need a Practical Path from Duplicate Records to Governed Master Data?
Use a scoped engagement to establish the priority domains, target mastering model, decision rights, platform requirements and implementation sequence without overcommitting the programme upfront.
Master And Reference Data Management FAQs
Answers to common enterprise questions about scope, domains, matching, platforms, governance, timeline, pricing and implementation.
What is master and reference data management?
Master and reference data management establishes governed ways to create, match, approve, maintain and distribute core business entities and controlled code sets. Master data commonly includes customers, products, suppliers, locations, assets, employees or legal entities. Reference data includes controlled values such as classifications, status codes, country or currency codes, product categories and other shared lookup values.
What is the difference between master data and reference data?
Master data describes durable business entities such as a customer, supplier, product or location. Reference data provides the controlled codes, classifications and value sets used to describe or categorise those entities and transactions. They require related governance but often have different ownership, change, hierarchy and distribution requirements.
What does DataConsultant include in a master and reference data management engagement?
Scope can include current-state assessment, domain and source analysis, authoritative-source decisions, canonical data-model design, matching and deduplication rules, survivorship and golden-record rules, reference-data and hierarchy design, stewardship workflows, governance controls, integration and syndication requirements, migration planning, platform requirements, implementation roadmap and knowledge transfer. Final scope is confirmed during discovery.
Which master-data domains can be covered?
The service can address one or multiple domains such as customer, product, material, supplier, vendor, employee, location, asset, party or legal-entity data, plus enterprise reference-data sets. Domain selection should follow business value, risk, data quality, integration dependencies and sponsorship rather than attempting to master every domain at once.
How are duplicates and golden records handled?
The engagement can define standardisation, match and merge rules, identity-resolution logic, survivorship priorities, exception handling and stewardship review. The appropriate balance between deterministic rules, probabilistic or platform-assisted matching, and manual review depends on the domain, data quality, risk and tolerance for false matches.
Do we need a new MDM platform?
Not necessarily. DataConsultant can assess whether existing ERP, CRM, data-platform or governance capabilities are sufficient before recommending a dedicated MDM or reference-data platform. Where platform selection or implementation is in scope, requirements are evaluated against the data domains, integration pattern, workflow, scale, security, operating model and total lifecycle needs.
Which MDM technologies can be considered?
The service can work with existing and planned master-data technologies, including platforms such as SAP Master Data Governance, Informatica MDM and 360 Applications, Reltio Multidomain MDM, as well as ERP, CRM, PIM, data-quality, metadata, integration and API technologies. Recommendations are requirements-led and vendor-neutral unless a specific platform is already mandated.
How long does a master and reference data management engagement take?
The timeline is confirmed after scoping. It depends on the number of domains and source systems, record volumes, data-quality condition, matching complexity, hierarchy and reference-data scope, stakeholder availability, integration requirements, target platform, migration needs, governance approvals and whether implementation support is included.
How is master and reference data management pricing calculated?
DataConsultant does not publish a fixed fee for this service. A written estimate is prepared after discovery and scope confirmation. Pricing depends on factors such as domains, source and consuming systems, data volumes, match and survivorship complexity, stewardship workflows, hierarchy and reference-data requirements, integrations, migration, platform configuration, controls, workshops, deliverables and implementation support.
Can the service support an ERP, CRM, cloud or M&A transformation?
Yes. Master-data work can be scoped around ERP or CRM transformation, cloud modernisation, mergers and acquisitions, application consolidation, data-platform programmes or AI readiness. The engagement should define source authority, crosswalks, duplicate handling, data-quality gates, migration and cutover rules, ownership and post-go-live operating responsibilities.
How are privacy, security and compliance requirements considered?
The service can incorporate data classification, role-based access, approval controls, change evidence, retention dependencies, sensitive-attribute handling, auditability and accountable ownership. Where regulated or personal data is involved, applicable obligations should be validated with the client’s qualified privacy, legal, security and compliance teams. The service supports governance and compliance readiness but does not replace legal advice or statutory certification.
What information should we prepare before starting?
Useful inputs include priority business processes, domain definitions, source and consuming-system inventories, sample data or profiling results, current ownership, data dictionaries, reference code sets, integration diagrams, duplicate or reconciliation reports, data-quality issues, policies, privacy and security constraints, current platform details, transformation plans and access to accountable business and technology stakeholders.
Can DataConsultant help with implementation after the design?
Yes. Implementation support can be scoped for data-model configuration, matching and survivorship rules, stewardship workflows, reference-data and hierarchy setup, integration and syndication, migration, testing, data-quality controls, governance mobilisation, rollout support, documentation, knowledge transfer and ongoing improvement. Responsibilities and acceptance criteria are agreed before implementation begins.
Master Data Enquiry
Request a Master And Reference Data Scope Review
Share your contact details and requirement. DataConsultant can review the likely domains, evidence, stakeholder involvement, technical dependencies and appropriate next step.