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Data Virtualization and MDM (Master Data Management)

A simple way to describe the interaction between Data Virtualization and Master Data Management is to use an example. If we want a truly 360° view of a customer where there are hundreds of attributes from demographic to transactional, historical fact to future intentions and preferences, as well as internal and external, structured and unstructured, real-time and social media data, and more, it would be impossible to "master" all that data in a physical Master Data Management or "golden record" of the customer. Rather, it makes more sense to have the truly master or reference data in a traditional MDM system and then to use Data Virtualization to deliver a virtual master which includes the other attributes.

  • Data Virtualization is used to rapidly create prototype Master Data Management solutions which can significantly accelerate conversations with business stake-holders and data owners. Once finalized and operational as a virtual MDM solution, parts or whole of the integration, data quality and data mastering of the models can be transferred or exported to the enterprise Master Data Management tool.
  • Data Virtualization is used to bring in normalized views of unstructured and social media information into Master Data Management systems to be included in an extended master data view.
  • Data Virtualization can work with two or more disparate Master Data Management systems to create a virtual combined master of the same entity (e.g. product) or a cross entity master (product and supplier), in the way that a logical data warehouse combines multiple physical data warehouses.
  • Delivering master data as a data service is more easily accomplished by using a Data Virtualization layer that can expose master data (directly from Master Data Management system or virtual master) as REST or other data service.

Learn more about Data Virtualization and MDM (Master Data Management) with this whitepaper.

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Gartner MQ