This week's Wiki-Wednesday topic is Master Data Management. One of the eWorld presentations we shared with you yesterday, an Oracle presentation called "Keeping the Tiger in the Cage" covers master data management and its importance to supplier management, among other initiatives. An excerpt of the Wikipedia page on this topic is below, but you can also click here to read the full article at the source.
If you are looking for more on how the topic affects you, click here to read our Procurement Primer on Master Data Management.
Master Data Defined
Reference Data is basic business data used in a single application, system, or process.
Master Data is a single source of basic business data used across multiple systems, applications, and/or processes.
Enterprise Master Data is the single source of basic business data used across all systems, applications, and processes for an entire enterprise (all departments, divisions, companies, and countries).
Market Master Data is the single source of basic business data for an entire marketplace. Market master data is used among enterprises within the value chain. An example of Market Master Data is the UPC (Universal Product Code) found on consumer products.
Market Master Data is compatible with enterprise-specific and domain-specific systems, compliant with or linked to industry standards, and incorporated within market research analytics. Market master data also facilitates integration of multiple data sources and literally puts put everyone in the market on the same page.
Master Data Management
In computing, master data management (MDM) comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization (which may include reference data). MDM 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.
The term recalls the concept of a master file from an earlier computing era. MDM is similar to, and some would say the same as, virtual or federated database management.
At a basic level, MDM 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. A common example of poor MDM is the scenario of a bank at which a 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.
Other problems include (for example) issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues.
One of the most common reasons some large corporations experience massive issues with MDM is growth through mergers or acquisitions. Two 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.
Processes commonly seen in MDM solutions 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.
The tools include data networks, file systems, a data warehouse, data marts, an operational data store, data mining, data analysis, data virtualization, data federation and data visualization. One of the newest tools, virtual master data management (also called virtual mdm) utilizes data virtualization and a persistent metadata server to implement a multi-level automated mdm hierarchy.
The selection of entities considered for MDM depends somewhat on the nature of an organization. In the common case of commercial enterprises, MDM may apply to such entities as customer (Customer Data Integration), product (Product Information Management), employee, and vendor. MDM 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 MDM repository, from which all requests for a certain entity instance produce the same description, irrespective of the originating sources and the requesting destinations.