The Single Source of Truth Where MDM Provides Authoritative Version of Critical Business Entities

The Data Governance Market integrates with master data management to provide governed, authoritative versions of critical business entities. Without MDM, each operational system maintains its own version of customer, product, or supplier data, leading to inconsistency and disputes about which version is correct. MDM consolidates data from multiple source systems, applies matching and survivorship rules to create golden record, and publishes authoritative version back to consuming systems. Data governance defines the matching rules, survivorship logic, and quality thresholds that MDM applies. By 2028, MDM will be standard for enterprises with 5+ source systems sharing customer or product data, reducing data conflicts by 40-60%.

How Matching Rules and Survivorship Logic Are Defined Through Governance Processes

Data governance council defines how MDM identifies matching records across systems and which attribute values become authoritative. Matching rule definition includes deterministic matching (tax ID, email exact match) and probabilistic matching (name similarity, address standardization) with thresholds governed by business tolerance for false positives vs false negatives. Survivorship rule definition for conflict resolution: source system priority (ERP over CRM for financial data), last update recency (most recent non-null value), or human review for unresolved conflicts. Exception handling for matching confidence below threshold but above random, routing to data steward for review. Trust framework for source system quality scores based on historical data accuracy, used to weight survivorship decisions. By 2029, MDM survivorship will be governed by documented rules approved by stewards for each attribute.

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The Data Steward Workbench for Manual Review of Unresolved Matches and Conflicts

MDM systems provide steward interface for manual review of records that cannot be automatically resolved. Match review for potential duplicates where matching confidence score falls in uncertain range, steward decides whether records refer to same entity. Conflict review for attributes where survivorship rules produce tie or conflict for record already matched, steward selects correct value. Hierarchy management for parent-child relationships (corporate groups, product families) that cannot be algorithmically determined. Exception queue aging showing oldest unresolved cases requiring steward attention. Bulk operations for similar cases where steward can apply same decision to group of records (e.g., all records from same source with same issue). By 2030, steward workbench will include AI assistance suggesting resolutions based on historical decisions, reducing manual review time by 40-60%.

The Integration with Data Governance Where MDM Quality Metrics Feed Governance Scorecards

MDM quality metrics provide measurable evidence of data governance effectiveness for customer, product, and supplier domains. Match rate by source system showing percentage of records that matched to existing golden records versus created new records. Coverage of critical attributes in golden record versus source systems, identifying data capture gaps. Duplicate rate in source systems before MDM consolidation and after survivorship, tracking improvement. Cross-system agreement for overlapping records (e.g., address match between ERP and CRM) measuring data consistency. Steward workload metrics for exceptions requiring manual review, trending over time as matching rules improve. By 2030, MDM quality metrics will be integrated into governance dashboards for regular council review.

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