Master Data Management

In computing, master data management (MDM) comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization (also called reference data). MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.

The term recalls the concept of a "master file" from an earlier computing era.


At a basic level, MDM seeks to ensure that an organization does not use multiple (potentially inconsistent) versions of the same master data in different parts of its operations, which can occur in large organizations. A common example of poor MDM is the scenario of a bank at which a customer has taken out a mortgage and the bank begins to send credit-card solicitations to that customer, ignoring the fact that the person already has an account relationship with the bank. This happens because the customer information used by the mortgage-lending section within the bank lacks integration with the customer information used by the credit card organization of the bank. Thus the two groups remain unaware that they share a customer.

Other problems include (for example) issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues.

One of the most common reasons why some large corporations experience massive issues with MDM has to do with the way in which organizations grow through mergers or acquisitions. Two organizations which merge will typically create an entity with duplicate master data (since each likely had at least one master database of its own prior to the merger). Ideally, database administrators resolve such duplication in master data as part of the merger. In practice, however, reconciling several master data systems can present difficulties because of the dependencies that existing applications have on the master databases. As a result, more often than not the two systems do not fully merge, but remain separate, with a special reconciliation process defined that ensures consistency between the data stored in the two systems. Over time, however, as further mergers and acquisitions occur, the problem multiplies, more and more master databases appear, and data-reconciliation processes become extremely complex, and consequently unmanageable and unreliable. Because of this trend, one can find organizations with 10, 15, or even as many as 100 separate, poorly-integrated master databases, which can cause serious operational problems in the areas of customer satisfaction, operational efficiency, decision-support, and regulatory compliance

For detailed definitions and examples of MDM, including buying and technical best practices and user implementation examples, visit the [ MDM Resource Guide] .


Processes commonly seen in MDM solutions include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, and data governance.

The tools include data networks, file systems, a data warehouse, a data mart, an operational data store, data mining, data analysis, data federation and data visualization.

The selection of entities considered for MDM depends somewhat on the nature of an organization. In the common case of commercial enterprises, MDM may apply to such entities as customer (Customer Data Integration), product (Product Information Management), employee, and vendor. MDM processes identify the sources from which to collect descriptions of these entities. In the course of transformation and normalization, administrators adapt descriptions to conform to standard formats and data domains, making it possible to remove duplicate instances of any entity. Such processes generally result in an organizational MDM repository, from which all requests for a certain entity instance produce the same description, irrespective of the originating sources and the requesting destinations.

ee also

* Reference Data
* Master Data
* Customer Data Integration
* Product Information Management
* Identity resolution

External links

* [ The What, Why, and How of Master Data Management]
* [ An Introduction to the Master Data Management Reference Architecture]
* [ Master Data Management Resource Guide] 200+ Free online MDM resources including MDM definitions, presentations, buying and technical best practices, user implementations, user and analyst quotes and a free MDM Pocket Guide.

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