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What is Master Data Management and How Does It Differ from Master Data Integration?

What is master data management (MDM)?

Master data management is the discipline of creating one trusted master reference (a single version of the truth) for all important business data, such as product data, customer data, asset data, finance data, and more. MDM helps ensure businesses don’t use multiple, potentially inconsistent versions of data in different parts of business, including processes, operations, and analytics and reporting. The three key pillars to effective MDM include: data consolidation, data governance, and data quality management.

“A technology-enabled discipline in which business and the IT organization work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official, shared master data assets.”

Gartner definition of MDM

Consistent master data fuels operational excellence. Clean master data helps ensure that client applications collect and deliver complete, accurate transactional data to construct a 360-degree view of each domain. Trusted master data fuels cross-functional analytics to drive performance management and business decision-making. Integration of master data with high-volume unstructured and machine data is critical to understand real-world performance and opportunity. Audit trails of all financial data across functional boundaries support compliance with government and industry regulations.

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Business-critical initiatives supported by MDM

 

What is master data integration (MDI) and how does it differ?

The purpose of master data integration is to distribute master data to enable a harmonized view of master data across all applications.  Master data integration allows for end-to-end process integration across the enterprise.  Master data integration does not change the quality of master data, but always distributes master data in its current state.  Instead, integration provides a distribution layer that gives your line-of-business applications a consistent view of data. Ultimately, a master data integration should increase the effectiveness and reduce the cost and effort of data sharing.

The purpose of master data management, on the other hand, is to ensure high quality for trusted master data across the enterprise. Master data will therefore be changed or enhanced towards better quality while processing, and duplicates might be detected and removed or merged. Companies typically prioritize quality only for selected domains according to business needs, as establishing master data management for a domain typically also includes significant investments in personnel and management of technologies. An intelligent combination of technologies allows to decouple integration and management and enables new patterns for agile handling of master data.

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