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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, hierarchy management, business semantics management and data governance.

A master data management tool can be used to support master data management by removing duplicates , standardizing data mass maintaining , [5] 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. Where the technology approach produces a "golden record" or relies on a "source of record" or "system of record", it is common to talk of where the data is "mastered".

This is accepted terminology in the information technology industry, but care should be taken, both with specialists and with the wider stakeholder community, to avoid confusing the concept of "master data" with that of "mastering data".

There are a number of models for implementing a technology solution for master data management. These depend on an organisation's core business, its corporate structure and its goals.

These include:. This model identifies a single application, database or simpler source e. The benefit of this model is its conceptual simplicity, but it may not fit with the realities of complex master data distribution in large organisations.

The source of record can be federated, for example by groups of attribute so that different attributes of a master data entity may have different sources of record or geographically so that different parts of an organisation may have different master sources.

Federation is only applicable in certain use cases, where there is clear delineation of which subsets of records will be found in which sources.

The source of record model can be applied more widely than simply to master data, for example to reference data. This model maintains a central registry, linking records across various source systems.

It spots duplicates by running cleansing and matching algorithms, then assigns unique global identifiers to matched records to help identify a " single version of the truth ".

This model does not send data back to the source systems, so changes to master data continue to be made through existing source systems.

When a single, comprehensive view of a customer is needed, it uses each reference system to build a view in real-time.

This model may be useful where an organisation has a large number of source systems spread across the world, and it is difficult to establish an authoritative source.

It also enables analysing data while avoiding the risk of overwriting information in the source systems. In this model, master data is generally consolidated from multiple sources in the hub to create a single version of truth, often referred to in this context as the "golden record".

Any updates made to the master data are then applied to the original sources. Consolidated hubs are inexpensive and quick to set up as MDM solutions go!

This model is mainly used for analysis and reporting. This model provides a "golden record" in the same way as the Consolidation model, but master data changes can happen in the MDM system as well as in the application systems.

This tends to make deployment more expensive. The main benefit of this style is that data is mastered in source systems and then synchronized with the hub, so data can coexist harmoniously and still offer a single version of the truth.

Another benefit of this approach is that the quality of master data is improved, and access is faster. Reporting is also easier as all master data attributes are in a single place.

This model stores and maintains master data attributes using linking, cleansing, matching and enriching algorithms to enhance the data.

The enhanced data can then be published back to its respective source system. This requires intrusion into the source systems for the two-way interactions.

Source systems can subscribe to updates published by the central system to give complete consistency. The main benefit of this style is that master data is accurate and complete at all times while security and visibility policies at a data attribute level can be supported by the Transaction style hub.

An organisation gains a centralized set of master data for one or more domains. There are several ways in which master data may be collated and distributed to other systems.

Master data management can suffer in its adoption within a large organization if the " single version of the truth " concept is not bought into by stakeholders who believe that their local definition of the master data is necessary.

For example, the product hierarchy used to manage inventory may be entirely different from the product hierarchies used to support marketing efforts or pay sales reps.

It is above all necessary to identify if different master data is genuinely required. If it is required, then the solution implemented technology and process must be able to allow multiple versions of the truth to exist, but will provide simple, transparent ways to reconcile the necessary differences.

If it is not required, processes must be adjusted. Without this active management, users that need the alternate versions will simply "go around" the official processes, thus reducing the effectiveness of the company's overall master data management program.

From Wikipedia, the free encyclopedia. Retrieved 6 June Retrieved Simple Talk. Stibo Systems. Soulsby, 22 October Data warehouses.

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