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Supporting Lean Integration Principles and Reuse with Data Virtualization « EMA Blog Community

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There is highly valuable synergy between line of business (LOB) executives and data/business analysts in today’s information driven work environments. Unfortunately complex processes and data integration “hairballs” cause analysts to spend most of their time bogged down in mundane data integration tasks instead of collaborating with executives to supply critical business insights. Sadly this is not a new problem. The time gap between information request and information delivery is getting longer all the time. The complexity of data management environments is making it nearly impossible to enact changes to data warehouse data in time for executives to make decisions. Defining requirements, identifying data, making iterative changes, and getting final validation can take over a month in many cases far longer than the window of opportunity to take action.

A more agile data integration approach that leverages lean integration principles needs to be implemented to insure business executives and analysts can move at the speed required to make value based decisions. Jared Hillam, EIM Practice Director for Intricity, LLC a recent guest on the Architect-to-Architect & Business Value Series I’m participating in made the point that there are too many chefs in the data kitchen and new tools need to be applied to consolidate processes and to enable core teams to execute. I agree with Jared, the opportunity loss most enterprises experience because executives can’t access data is costing millions and taking a faster, leaner approach is defiantly necessary.

Data virtualization technology, when it leverages lean integration principles brings an innovative solution to this problem. Data virtualization compliments the data warehouse infrastructure while delivering repeatable success through reuse and by cutting the time between request and delivery of data. Data virtualization’s common access layer is especially well suited to rapid prototyping it brings the end user into the loop early and engages them with the analyst to shorten requirement gathering, time costs and risk. These platforms allow the analyst to profile data in real-time, apply transformations and with sophisticated platforms enact data quality functions on the data as its accessed by the data virtualization layer. Once these projects are complete fellow analysts within the organization creating further agile value in the system can reuse the processes and projects.

As data management ecosystems expand beyond traditional data warehouse’s to include cloud, Big Data and analytic platforms the ability to apply lean integration principles and agile work processes will become an even greater value to the information supply chain. Data virtualization is technology that enables this agility and creates time to value for its users.

On June 26th I will be a guest on the Architect-to-Architect & Business Value Series discussing how to Achieve Business Intelligence Nirvana with Self-Service and Data Virtualization. I hope you will join us for the program.

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