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 データ管理 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.
Master data concepts, such as customer, product, or employee, are those core business objects used in different applications across the organization. These objects include associated metadata, attributes, definitions, roles, connections, and taxonomies.
Master data objects represent the things we care about, logged in transaction systems, measured in reporting systems, and analyzed in 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, magnified when developing a multi-domain master environment incorporating hubs for multiple master data concepts, result from diminished oversight over shared organizational information and データモデリング.
Read the 9-page whitepaper “Mastering Data Modeling for Master Data Domains” by David Loshin to explore the root causes influencing organizational data model development. It also highlights how organic development introduces inconsistency in structure and semantics, complicating master data integration.
A particular concern is that managing multiple data concepts in a master data environment allows duplication to occur. By applying a governed approach using universal models, data professionals can reduce duplication and inconsistency, improving both the quality of the process and the results of master data integration.
