Master data management
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Master data management (MDM) is a discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.[1][2]
Drivers for master data management
Organisations, or groups of organisations, may establish the need for master data management when they hold more than one copy of data about a business entity. Holding more than one copy of this master data inherently means that there is an inefficiency in maintaining a "single version of the truth" across all copies. Unless people, processes and technology are in place to ensure that the data values are kept aligned across all copies, it is almost inevitable that different versions of information about a business entity will be held. This causes inefficiencies in operational data use, and hinders the ability of organisations to report and analyze. At a basic level, master data management 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.
Other problems include (for example) issues with the
There are a number of root causes for master data issues in organisations. These include:
- Business unit and product line segmentation
- Mergers and acquisitions
Business unit and product line segmentation
As a result of
A typical example is the scenario of a bank at which a
Mergers and acquisitions
One of the most common reasons some large corporations experience massive issues with master data management is growth through
Another problem concerns determining the proper degree of detail and normalization to include in the master data schema. For example, in a federated HR environment, the enterprise may focus on storing people data as a current status, adding a few fields to identify date of hire, date of last promotion, etc. However this simplification can introduce business impacting errors into dependent systems for planning and forecasting. The stakeholders of such systems may be forced to build a parallel network of new interfaces to track onboarding of new hires, planned retirements, and divestment, which works against one of the aims of master data management.
People, process and technology
Master data management is enabled by technology, but is more than the technologies that enable it. An organisation's master data management capability will include also people and process in its definition.
People
Several roles should be staffed within MDM. Most prominently the Data Owner and the Data Steward. Probably several people would be allocated to each role, each person responsible for a subset of Master Data (e.g. one data owner for employee master data, another for customer master data).
The Data Owner is responsible for the requirements for data quality, data security etc. as well as for compliance with data governance and data management procedures. The Data Owner should also be funding improvement projects in case of deviations from the requirements.
The Data Steward is running the master data management on behalf of the data owner and probably also being an advisor to the Data Owner.
Process
Master data management can be viewed as a "discipline for specialized quality improvement"
Processes commonly seen in master data management include source identification, data collection,
Technology
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 "
Implementation models
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:
- Source of record
- Registry
- Consolidation
- Coexistence
- Transaction/centralized
Source of record
This model identifies a single application, database or simpler source (e.g. a spreadsheet) as being the "source of record" (or "system of record" where solely application databases are relied on). 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.
Transmission of master data
There are several ways in which master data may be collated and distributed to other systems.[6] This include:
- 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.
Change management in implementation
Master data management can suffer in its adoption within a large organization if the "single version of the truth" concept is not affirmed 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.
See also
- Business semantics management
- Customer data integration
- Data governance
- Data integration
- Data steward
- Data visualization
- Enterprise information integration
- Information management
- Linked data
- Master data
- Operational data store
- Product information management
- Record linkage
- Reference data
- Semantic Web
- Single customer view
- Web data integration
References
- ^ "Gartner Glossary: Master Data Management". Gartner. Retrieved 6 June 2020.
- ^ Rouse, Margaret (2018-04-09). "Definition from WhatIs.com". SearchDataManagement. Retrieved 2018-04-09.
- DAMA International
- ^ "Learn how to create a MDM change request – LightsOnData". LightsOnData. 2018-05-09. Retrieved 2018-08-17.
- ^ Jürgensen, Knut (2016-05-16). "Master Data Management (MDM): Help or Hindrance?". Simple Talk. Retrieved 2018-04-09.
- ^ "Creating the Golden Record: Better Data Through Chemistry", DAMA, slide 26, Donald J. Soulsby, 22 October 2009