Showing posts with label Master Data Management. Show all posts
Showing posts with label Master Data Management. Show all posts

December 17, 2007

Master Data Management and Enterprise Architecture

“Master Data Management (MDM), also known as Reference Data Management, is a sub-discipline of data architecture within Information Technology (IT) that focuses on the management of reference or master data that is shared by several disparate IT systems and groups. MDM is required to enable consistent computing between diverse system architectures and business functions.” (Wikipedia)

Master data are the critical nouns of a business and fall generally into four groupings: people, things, places, and concepts. Further categorizations within those groupings are called subject areas, domain areas, or entity types…Master data can be described by the way that it interacts with other data. For example, in transaction systems, master data is almost always involved with transactional data. A customer buys a product. A vendor sells a part, and a partner delivers a crate of materials to a location… Master data can be described by the way that it is Created, Read, Updated, Deleted, and searched. This life cycle is called the CRUD cycle…Why should I manage master data? Because it is used by multiple applications, an error in master data can cause errors in all the applications that use it. (“The What, Why, and How of Master Data Management” by Wolter and Haselden, Microsoft Corporation, November 2006)

How can MDM software help manage MDM? Wolter and Haselden identify three primary methods:

  • Single-copy of master data—where all changes and additions are made to the master and all applications accessing it use the current master data set
  • Multiple copies of master data—master data is updated in a single master, but the data is sent out to the source systems where data sets are stored locally and changes to non-master data can be made)
  • Continuous merge—where changes are made to the source data sets and are sent to the master to be merged and resent out to the source data sets again., in “Demystifying Master Data Management”, 30 April 2007 reports that “unfortunately, most companies don't have a precise view about their customers, products, suppliers, inventory or even employees. Whenever companies add new enterprise applications to "manage" data, they unwittingly contribute to an overall confusion about a corporation's overall view of the enterprise. As a result, the concept of master data management (MDM)—creating a single, unified view of an organization—is growing in importance.” However, the article notes that adding MDM technologies will not magically correct an organization’s data quality issues, as noted in “a recent report from The Data Warehousing Institute that found 83 percent of organizations suffer from bad data for reasons that have nothing to do with technology. Among the causes of poor-quality data were inaccurate reporting, internal disagreements over which data is appropriate and incorrect definitions rendering the data unusable.”

So the essence of an MDM initiative is to first improve data quality by developing the process to define, categorize, and identify authoritative sources for data, and only then to apply MDM software to build a single view of the data.

MDM is important to enterprise architecture for a number of reasons:

  • Information sharing—MDM is critical to information sharing, data integration, and reconciliation, as it establishes an authoritative source of data that can be shared between systems or organizational entities.
  • Data governanceMDM helps establish the basis for sound data governance, since data owners, stewards, and users need to be able to distinguish good data from bad data, define data objects, establish data standards, metadata requirements and registries for discoverability, access rights, transfer protocols and methods, and maybe most importantly a governance process that defines who is allowed to change system data and how.
  • Business IntelligenceMDM enables business intelligence by providing for an integration of data for mining, reporting, and decision support.

Creating authoritative master data is an imperative for data and systems integrity, and good decision making based on sound enterprise data.