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Posted by Noel Yuhanna on August 15, 2013
For decades, firms have deployed applications and BI on independent databases and warehouses, supporting custom data models, scalability, and performance while speeding delivery. It’s become a nightmare to try to integrate the proliferation of data across these sources in order to deliver the unified view of business data required to support new business applications, analytics, and real-time insights. The explosion of new sources, driven by the triple-threat trends of mobile, social, and the cloud, amplified by partner data, market feeds, and machine-generated data, further aggravates the problem. Poorly integrated business data often leads to poor business decisions, reduces customer satisfaction and competitive advantage, and slows product innovation — ultimately limiting revenue.
Forrester’s latest research reveals how leading firms are coping with this explosion using data virtualization, leading us to release a major new version of our reference architecture, Information Fabric 3.0. Since Forrester invented the category of data virtualization eight years ago with the first version of information fabric, these solutions have continued to evolve. In this update, we reflect new business requirements and new technology options including big data, cloud, mobile, distributed in-memory caching, and dynamic services. Use information fabric 3.0 to inform and guide your data virtualization and integration strategy, especially where you require real-time data sharing, complex business transactions, more self-service access to data, integration of all types of data, and increased support for analytics and predictive analytics.
Information fabric 3.0 reflects significant innovation in data virtualization solutions, including:
Our research also shows that information fabric is no longer limited to only more advanced firms: all enterprises can benefit, supporting a wide range of workloads and patterns including real-time analytics, predictive analytics, and extreme transactions. Implementing an information fabric does take time and focused effort, best managed as an ongoing initiative, starting with the usage scenarios with the greatest obtainable value, and likely favoring analytics over transactions at first. For a successful data virtualization implementation, build on industry standard data formats, start small by integrating a few sources, staff your team with data virtualization experts, and move aggressively to exploit distributed in-memory architecture.
As the CTO of a major financial firm put it in his interview: “The only question is how long you can delay going with data virtualization. If you look to the future, you simply have to do it this way. Point-to-point spaghetti won’t work in the future. You have to create these environments if you want to do it right.”
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