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Master data management (MDM) is a technology-enabled 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]

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.

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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.

One of the most common reasons some large corporations experience massive issues with master data management is growth through mergers or acquisitions. Any 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 this problem through deduplication of the 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.

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.

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.

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.

Master data management can be viewed as a "discipline for specialized quality improvement"[3] defined by the policies and procedures put in place by a data governance organization. It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control,[4] in the ongoing maintenance and application use of that data.

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".

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.

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.

SDM integrates advanced studies in engineering and management sciences, preparing early- and mid-career professionals to be the technically-grounded senior leaders of their enterprises. We offer our students the opportunity to participate in a lifelong partnership with an innovative global community. Created, designed, and managed by an active partnership between the School of Engineering and Sloan School of Management, SDM is setting the standard for delivering career-and-family-compatible professional education, using advanced information and distance learning technologies.

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The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members. More information about signing up for classes.

Most students participate in a paid summer internship between the first and second years of their program, to put into practice and build upon knowledge gained in the first-year course work and learn more about work practices in the field of information management.

As a continuation of the long-established on-premises version, MDMS is already used by 140 corporate groups bringing together 1000 companies. Each day, 4000 users work in Dynamics 365 Business Central or Dynamics NAV with master data synchronized by MDMS. e24fc04721