Master Reference Data: Governance and Maintenance for Analytics and Operations
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  John Biderman   John Biderman
Information Architect
Harvard Pilgrim Health Care
 


 

Tuesday, May 1, 2012
03:20 PM - 04:10 PM

Level:  Introductory


Reference data is often the orphan child of Master Data Management, sometimes segregated from MDM entirely or assumed to come along for the ride as MDM strategies are implemented for other domains.

But reference data is at the core of your enterprise semantics -- it describes your business -- and as such requires a holistic approach to its governance and mastering. Tools and processes need to be in place to harmonize reference data across the enterprise, since application systems tend to proliferate their own coding and classification schemes with varying but overlapping taxonomies.

This case study will describe both a toolset and a governance model, developed first in the data warehouse space and now extended to the operational realm. We first addressed the problem at the focal point of data integration, our Enterprise Data Warehouse, in which reference domains from multiple systems needed to be rationalized into an enterprise standard. This led us to develop a tool (the Reference Table Utility, or RTU) and a data store (the Corporate Reference Center, or CRC), and to engage business stakeholders in governance over reference data. Now, as we build a new operational platform with federated components integrated via Service Oriented Architecture (SOA), the same processes and toolkits are being leveraged to essentially virtualize the master reference data hub in the operational realm.


John Biderman has over 20 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 is responsible for data architecture, data integration, logical data modeling, metadata capture, architecture standards and policies, data quality interventions, project guidance, and engaging the business in data stewardship processes.


   
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