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How Enterprises Measure Data Virtualization Platform Maturity

Data virtualization adoption has evolved from project-level deployments to enterprise-scale data virtualization layers

Data virtualization is one of the fastest-growing segments in enterprise IT today. Originally deployed for light BI federation needs, today's data virtualization use cases span a range of applications, including customer experience management, risk management and compliance, M&A transition and more.

Data virtualization adoption has evolved from project-level deployments to enterprise-scale data virtualization layers that deliver data from multiple sources (Relational, Semi-structured XML, Dimensional MDX, the new NoSQL data types, and more) to multiple applications on demand.

At the same time, the data virtualization vendor landscape has evolved.  Nearly all of the Enterprise Information Integration (EII) companies from the early 2000s have been acquired or exited the market, with only Composite Software able to support today's more advanced data virtualization requirements.  New entrants from adjacent markets such as BI, Mashups, Search and ETL have recently announced data virtualization products hoping to penetrate this fast-growing market.

But rapidly evolving use cases, fast improving product capabilities and an evolving vendor landscape can be confusing to organizations evaluating data virtualization platforms, especially in the absence of a data virtualization Gartner Magic Quadrant or Forrester Wave.

With nearly a decade of pioneering R&D and enterprise-scale deployments, Composite Software has been solving the toughest data virtualization problems at the world's biggest enterprises, including 10 of the top 20 banks, six of the top 10 pharmaceuticals, four of the top five energy companies, leading telecom providers and numerous government agencies.

Leveraging this hard-earned expertise, Composite developed a Data Virtualization Platform Maturity Model that provides organizations with a systematic data virtualization assessment approach.

This model uses the eight major data virtualization functionality categories (Query Processing, Transformation & Quality, Information Delivery, Enterprise-Scale Operations, Data Source Access, Modeling and Metadata Management, Caching, and Goverance & Security) plotted against a five-stage maturity timeline (Entry Level, Limited, Intermediate, Advanced, Mature) to provide a common framework for measuring the maturity of any data virtualization platform.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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