Question

Research the master data management approach (MDM). Share your results with the class, specifically covering operational, analytic, and enterprise MDM. Be sure to discuss how MDM impacts data warehouse extraction, transformation, and loading.

Answer 1

As per the Forrester's definition of master data management- DMD is a tool y=that brings data harmony between systems of engagement and systems of record, so that systems of insights can feed off a connected and open data ecosystem, to drive insight to action.

MDM is an enterprise function. It is a delicate initiative to make master data within the organization accessible, secure, transparent and trustworthy. Master data pertains to information relsted to customers, products, vendors etc.

An enterprise has three kinds of actual business data: Transactional, Analytical, and Master. Transactional data supports the applications. Analytical data supports decision-making. Master data represents the business objects upon which transactions are done and the dimensions around which analysis is accomplished. As data is moved and manipulated, information about where it came from, what changes it went through, etc. represents a fourth kind of enterprise data called metadata (data about the data). Though not a prime focus of this paper, the key role metadata plays in the broader information management space and how it relates directly to MDM is described

Solutions that focus on managing analytical master data are called Analytical MDM. They focus on providing high quality dimensions with their multiple simultaneous hierarchies to data warehousing and BI technologies. They also bring real value to the enterprise, but lack the ability to influence operational systems. Any data cleansing done inside an Analytical MDM solution is invisible to the transactional applications and transactional application knowledge is not available to the cleansing process. Because Analytical MDM systems can do nothing to improve the quality of the data under the heterogeneous application landscape, poor quality inconsistent domain data finds its way into the BI systems and drives less than optimum results for reporting and decision making.

Solutions that focus on managing transactional data under operational applications are called Operational MDM. They rely heavily on integration technologies. They bring real value to the enterprise, but lack the ability to influence reporting and analytics.

MDM supplies critically important dimensions, hierarchies and cross reference information to the Oracle Data Warehouse. With all the master data in the MDM Hubs, only one pipe is needed into the data warehouse for key dimensions such as Customer, Supplier, Account, Site, and Product. The “Star Schema” illustrated on the left from the Oracle whitepaper on Data Warehousing Best Practices16 shows all dimensions except Time are managed by Oracle MDM. Not only that, Oracle MDM maintains master cross-references for every master business object and every attached system. This cross-reference is available regardless of data warehousing deployment style. For example, an enterprise data warehouses or data mart can seamlessly combine historic data retrieved from those operational systems and placed into a Fact table, with real-time dimensions form the MDM Hub. This way the business intelligence derived from the warehouse is based on the same data that is used to run the business on the operational side.