Wikipedia Says “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“.
Master Data Examples may includes:
- Product
- Customer
- Well Identifier
- Worker/Employee
- Supplier
Master Data Management – MDM
Master Data Management (MDM) entails control over Master Data values and identifiers that enable consistent use, across systems, of the most accurate and timely data about essential business entities. The goals of MDM include ensuring availability of accurate, current values while reducing risks associated with ambiguous identifiers (those identified with more than one instance of an entity and those that refer to more than one entity).
MDM Two Architectural Components:
- Technology: To Profile, Consolidate and Synchronize the Master Data across the Enterprise
- Applications: To Manage, Cleanse and Enrich, Structured as well as Unstructured Master Data
MDM must be Integrated smoothly with Modern SOA (Service Oriented Architecture) to manage the Master Data across many Systems responsible for Data Entry and bring the clean corporate Master Data to the Applications and Processes that run the Business. MDM becomes the Center Source for Precise Fully Cross-Reference Real Time Master Data. MDM Applications must also Support Data Governance (Business Process for Defining Data Definitions, Standards, Access Rights and Quality Rules.
In short, MDM needs to be implemented, which includes Optimize Management of Master Data Assets in the Organization and MDM helps to Extract Meaningful Insight for Management to take Smarter Decision.
The most common drivers for initiating a Master Data Management Program are:
- Meeting Organizational Data Requirements: Multiple areas within an organization need access to the same data sets, with the confidence that the data sets are complete, current, and consistent. Master Data often form the basis of these data sets (e.g., determining whether an analysis includes all customers depends on having a consistently applied definition of a customer).
- Managing Data Quality: Data inconsistencies, quality issues, and gaps, lead to incorrect decisions or lost opportunities; Master Data Management reduces these risks by enabling a consistent representation of the entities critical to the organization.
- Managing the Costs of Data Integration: The cost of integrating new data sources into an already complex environment are higher in the absence of Master Data, which reduces variation in how critical entities are defined and identified.
- Reducing Risk: Master Data can enable simplification of data sharing architecture to reduce costs and risk associated with a complex environment.
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