As new data systems are built from an enterprise data model framework, many potential data quality issues will be exposed and resolved, prior to implementation. Existing data quality issues can be identified by “mapping” data systems to the EDM. An EDM is essential for data quality because it exposes data discrepancies, inherent in redundant data. Disparate redundant data is one of the primary contributing factors to poor data quality. It also plays a vital role in several other enterprise type initiatives:ĭata is an important enterprise asset, so its quality is critical. An EDM facilitates the integration of data, diminishing the data silos, inherent in legacy systems.
For enterprise data initiatives, such as an Operational Data Store (ODS) or Data Warehouse (DW), an EDM is mandatory, since data integration is the fundamental principle underlying any such effort. The model can be thought of much like an architectural blueprint is to a building providing a means of visualization, as well as a framework supporting planning, building and implementation of data systems. It minimizes data redundancy, disparity, and errors core to data quality, consistency, and accuracy.Īs a data architectural framework, an EDM is the “starting point” for all data system designs. Integrated data provides a “single version of the truth” for the benefit of all. It enables the identification of shareable and/or redundant data across functional and organizational boundaries. The model unites, formalizes and represents the things important to an organization, as well as the rules governing them.Īn EDM is a data architectural framework used for integration. It is independent of “how” the data is physically sourced, stored, processed or accessed. An Enterprise Data Model (EDM) represents a single integrated definition of data, unbiased of any system or application.
It incorporates an appropriate industry perspective. An Enterprise Data Model is an integrated view of the data produced and consumed across an entire organization.
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