Master Data Management
Master Data Management (MDM) is the discipline of creating and maintaining a single, consistent, authoritative version of an organization's core business entities — such as customers, products, or suppliers — across all the systems that…
Definition
Master Data Management (MDM) is the discipline of creating and maintaining a single, consistent, authoritative version of an organization's core business entities — such as customers, products, or suppliers — across all the systems that use them.
Overview
Large organizations typically store customer or product data in many separate systems: a CRM, an ERP, a billing platform, and various analytics tools. Without coordination, the same customer can end up with slightly different names, addresses, or IDs in each system, leading to duplicated records, inconsistent reporting, and costly reconciliation work. MDM addresses this by establishing a 'golden record' — one trusted, deduplicated version of each master entity that other systems reference or synchronize against. Building an MDM program typically involves profiling data across source systems, defining matching and merge rules to identify duplicate records, establishing governance workflows for who can create or edit master records, and distributing the resulting golden records back out to consuming applications, often via APIs or scheduled synchronization jobs. MDM is closely tied to data governance, which defines the policies and accountability around data quality, and to data catalog and data lineage tooling, which help teams understand where master data originates and how it flows through the organization. Effective MDM materially improves the reliability of a data warehouse built on top of it, since inconsistent master data otherwise propagates into every downstream report. MDM is more of an organizational and process discipline than a single tool, though dedicated MDM platforms (from vendors like Informatica, SAP, and Reltio) exist to support matching, merging, and workflow automation at scale.
Key Concepts
- Establishes a single 'golden record' for core entities like customers, products, or suppliers
- Uses matching and merge rules to deduplicate records across source systems
- Defines governance workflows for creating, editing, and approving master data
- Distributes trusted master data back out to consuming applications and systems
- Reduces reporting inconsistencies caused by conflicting entity definitions
- Closely tied to data governance, data quality, and data lineage practices
Use Cases
Frequently Asked Questions
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