ES=EDM+RDR: Enterprise Semantics Captured in the Enterprise Data Model and Reference Data Repository
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  John Biderman   John O. Biderman
Sr. IT Architect
Harvard Pilgrim Health Care


Tuesday, March 31, 2015
01:15 PM - 02:00 PM

Level:  Introductory

This equation may not be as catchy or important as E=mc2, but data management shops have some standard tools which together present a powerful capability to capture and describe our enterprise semantics. In this session we'll discuss the use of an Enterprise Data Model to define the business data elements across the enterprise. While a potent tool, by itself it only goes so far. For example, the model will tell you there exists a business data element for, say, Customer Type, but doesn't describe the possible actual values. That's where the Master Reference Data Repository comes into play. A repository can hold industry-standard data sets used in your enterprise, as well as enterprise-standard local values and their translations to system-specific representations.

This will be more than a conceptual discussion, as we'll give examples how we employ our Enterprise Logical Data Model (ELDM) as the semantic master to which metadata from all our documented systems get mapped. The ELDM is an application-neutral representation of nearly all the data entities and attributes required to run the business, and names everything in business terms. And we'll also discuss our master Reference Data Repository and our approach to a global code lookup service.

John Biderman has over 25 years of experience in application development, database modeling, systems integration, and enterprise information architecture. He has consulted to Fortune 500 clients in the US, UK, and Asia. At Harvard Pilgrim Health Care (a New England-based not-for-profit health plan) he leads major application development and systems integration projects related to platform modernization and implementation of national health care reform, while having responsibility for data architecture, data integration, logical data modeling, metadata capture, architecture standards and policies, data quality interventions, and engaging the business in data stewardship processes.

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