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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
Master Data Management: Definition and Architecture
Master Data Management (MDM) is the set of processes, governance, policies, standards, and tools that consistently defines and manages the critical data entities of an organisation. The DAMA DMBOK v2 defines master data as "data about the business entities that provide context for business transactions" — typically customers, products, employees, suppliers, locations, and accounts.
The Four MDM Implementation Styles
| Style | Description | Data Flow | Best For |
|---|---|---|---|
| Registry | Cross-reference index only; source systems retain ownership | Read from sources, no write-back | Minimal disruption, federated organisations |
| Consolidation | Golden record created in MDM hub; not pushed to sources | Read from sources, golden record in hub | Analytics and reporting use cases |
| Coexistence | Golden record created and published back to source systems | Bidirectional synchronisation | Organisations needing consistent data across systems |
| Centralised | MDM hub is the system of record; all systems consume from it | Hub is authoritative source | Greenfield implementations, high control environments |
Survivorship Rules
Survivorship rules are the logic that determines which source system's data populates each attribute of the golden record when multiple sources provide conflicting values. Common survivorship strategies include: most recent update wins, most trusted source wins (source system ranking), most complete record wins, and manual review for high-value conflicts.
MDM Governance
Effective MDM requires strong data governance: Data Stewards responsible for each master data domain, clear data ownership policies, defined processes for creating and updating master records, and regular data quality monitoring. MDM without governance reverts to the fragmented state it was designed to solve.
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