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.
This information has been de-duplicated, reconciled and enriched, becoming a consistent, reliable source. Once created, this master data serves as a trusted view of business-critical data that can be managed and shared across the business to promote accurate reporting, reduce data errors, remove redundancy, and help workers make better-informed business decisions.
Transactional Data: Data about business events (often related to system transactions, such as sales, deliveries, invoices, trouble tickets, claims and other monetary and non-monetary interactions) that have historical significance or are needed for analysis by other systems. Transactional data are unit level transactions that use master data entities. Unlike master data, transactions are inherently temporal and instantaneous by nature.
Unstructured Data: Data found in email, white papers, magazine articles, corporate intranet portals, product specifications, marketing collateral and PDF files.
Metadata: Data about other data. It may reside in a formal repository or in various other forms, such as XML documents, report definitions, column descriptions in a database, log files, connections and configuration files.
Hierarchical Data: Data that stores the relationships between other data. It may be stored as part of an accounting system or separately as descriptions of real world relationships, such as company organizational structures or product lines. Hierarchical data is sometimes considered a super MDM domain because it is critical to understanding and sometimes discovering the relationships between master data.
Reference Data: A special type of master data used to categorize other data or used to relate data to information beyond the boundaries of the enterprise. Reference data can be shared across master or transactional data objects (e.g. countries, currencies, time zones, payment terms, etc.)
Master Data: The core data within the enterprise that describes objects around which business is conducted. It typically changes infrequently and can include reference data that is necessary to operate the business. Master data is not transactional in nature, but it does describe transactions. The critical nouns of a business that master data covers generally fall into four domains and further categorizations within those domains are called subject areas, sub-domains or entity types.
MDM eliminates costly redundancies that occur when organizations rely upon multiple, conflicting sources of information. For example, MDM can ensure that when customer contact information changes, the organization will not attempt sales or marketing outreach using both the old and new information.
Common business initiatives addressed by MDM include:
- Customer experience
- Mergers and acquisitions
- Governance and compliance
- Operational efficiency
- Supplier optimization
- Product experience
Transmission of master data
There are several ways in which master data may be collated and distributed to other systems, 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.
People, Process and Technology
Master data management is enabled by technology but is more than the technologies that enable it. An organization’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.
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.
Master data management can be viewed as a “discipline for specialized quality improvement” 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, 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), 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”.