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Master data management

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Title: Master data management  
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Master data management

In business, master data management (MDM) comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an

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  • MDM Community
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  • Reprise: When is Master Data and MDM Not Master Data or MDM?

External links

  1. ^ "What is Master Data" SearchDataManagement, TechTarget, 22 November 2010,
  2. ^ "Introduction to Master Data Management", Mark Rittman, Director, Rittman Mead Consulting, 9 May 2008
  3. ^ ""Defining Master Data", David Loshin, BeyeNetwork, May 2006
  4. ^
  5. ^ DAMA-DMBOK Guide,2010 DAMA International
  6. ^ "Creating the Golden Record: Better Data Through Chemistry", DAMA, slide 26, Donald J. Soulsby, 22 October 2009


See also

  • Data consolidation – The process of capturing master data from multiple sources and integrating into a single hub (operational data store) for replication to other destination systems.
  • Data federation – The process of providing a single virtual view of master data from one or more sources to one or more destination systems.
  • Data propagation – The process of copying master data from one system to another, typically through point-to-point interfaces in legacy systems.

There are several ways in which Master Data may be collated and distributed to other systems.[6] This includes:

Transmission of Master Data

The tools include data networks, file systems, a data warehouse, data marts, an operational data store, data mining, data analysis, data visualization, data federation and data virtualization. One of the newest tools, virtual master data management utilizes data virtualization and a persistent metadata server to implement a multi-level automated master data management hierarchy.

The selection of entities considered for master data management depends somewhat on the nature of an organization. In the common case of commercial enterprises, master data management may apply to such entities as customer (customer data integration), product (product information management), employee, and vendor. Master data management 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 master data management repository, from which all requests for a certain entity instance produce the same description, irrespective of the originating sources and the requesting destination.

Processes commonly seen in master data management include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, data classification, taxonomy services, item master creation, schema mapping, product codification, data enrichment and data governance.


One of the most common reasons some large corporations experience massive issues with master data management is growth through customer satisfaction, operational efficiency, decision support, and regulatory compliance.

Other problems include (for example) issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues. Master data management of disparate data systems requires data transformations as the data extracted from the disparate source data system is transformed and loaded into the master data management hub. To synchronize the disparate source master data, the managed master data extracted from the master data management hub is again transformed and loaded into the disparate source data system as the master data is updated. As with other Extract, Transform, Load-based data movement, these processes are expensive and inefficient to develop and to maintain which greatly reduces the return on investment for the master data management product.

At a basic level, master data management seeks to ensure that an organization does not use multiple (potentially customer has taken out a mortgage and the bank begins to send mortgage solicitations to that customer, ignoring the fact that the person already has a mortgage account relationship with the bank. This happens because the customer information used by the marketing section within the bank lacks integration with the customer information used by the customer services section of the bank. Thus the two groups remain unaware that an existing customer is also considered a sales lead. The process of record linkage is used to associate different records that correspond to the same entity, in this case the same person.


At its core Master Data Management (MDM) can be viewed as a "discipline for specialized quality improvement"[5] defined by the policies and procedures put in place by a data governance organization. The ultimate goal being to provide the end user community with a "trusted single version of the truth" from which to base decisions.

Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, master data management streamlines data sharing among personnel and departments. In addition, master data management can facilitate computing in multiple system architectures, platforms and applications.[4]



  • Definition 1
  • Issues 2
  • Solutions 3
  • Transmission of Master Data 4
  • See also 5
  • References 6
  • External links 7

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

Master data management 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.

In computing, a master data management tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. Master data are the products, accounts and parties for which the business transactions are completed. The root cause problem stems from business unit and product line segmentation, in which the same customer will be serviced by different product lines, with redundant data being entered about the customer (aka party in the role of customer) and account in order to process the transaction. The redundancy of party and account data is compounded in the front to back office life cycle, where the authoritative single source for the party, account and product data is needed but is often once again redundantly entered or augmented.

  • reference data – the business objects for transactions, and the dimensions for analysis
  • analytical data – supports decision making[2][3]

The data that is mastered may include:


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