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Master data modeling for master data domains

Greater dependence on enterprise-class applications has created a demand for centralizing organizational data in their support. Imperatives such as enterprise resource planning (ERP), data warehousing for business intelligence, and customer relationship management (CRM) rely on data integration programs such as customer data integration (CDI) and master data management (MDM). That is a set of data management techniques used to facilitate the definition and observance of policies, procedures, and infrastructure to support the capture, integration, and sharing of a trustworthy set of unified views of master data concepts.

These master data concepts, such as customer, product, or employee, are those core business objects that are used in the different applications across the organization, along with their associated metadata, attributes, definitions, roles, connections, and taxonomies. Master data objects are those things that we care about. The things that are logged in our transaction systems, measured and reported on in our reporting systems, and analyzed in our analytical systems.

Some objectives of master data integration include improved data quality and operational efficiency. However, we often complicate how we develop master data indexes, registries, and hubs by challenges inherent in the organic manner in which the de facto enterprise application infrastructure developed. These challenges, which are magnified when developing a multi-domain master environment that incorporates hubs for each of several master data concepts, are a byproduct of diminished oversight over shared organizational information and data modeling.

Read the 9-page whitepaper “Mastering Data Modeling for Master Data Domains by David Loshin to explore some of the root causes that have influenced how we develop a variety of data models of an organization. Also, discover how that organic development has introduced potential inconsistency in structure and semantics, and how those inconsistencies complicate master data integration. A particular concern is that the desire for a master data environment managing multiple data concepts allows duplication to occur. But by applying a governed approach using universal models, data professionals can reduce the risk of duplication and inconsistency while improving the quality of the process and the results of master data integration.

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