Wednesday May 02, 2012

Analytics And Agility – Why It Is So Important Today

No question that many IT professionals are feeling the pressure to rapidly deliver analytic solutions that respond to the continuous demands of the business user. Primary research shows that analytics in the context of Big Data continues to be top of mind for executive teams at global enterprises. Executives clearly understand the value of Big Data and analytics and many are very vocal proponents of the value it can bring to bear on the business. What executive is not interested in understanding sales trends, KPI’s, social sentiment about the business and critical metrics, even predictive views of the business?

All these requirements and many more have created a tremendous backlog of analytic application requests. This backlog grows significantly as IT professionals are successful in delivering highly tuned internal analytic solutions that quickly deliver value. Everyone begins to say “I want one of those”!

We are no longer operating in the past when these analytic projects took years to complete and where a “boil the ocean” approach was the norm. Today, we see a changing landscape where Agility is what matters most when it comes to delivering rapid returns on key Big Data and analytic investments. In fact, just about every IT professional is pursuing the Agile model for development where product development efforts of the past are streamlined using methodologies that lean towards delivering high value features that are delivered in short bursts of time. This is clearly the method that is emerging when it comes to rapidly delivering analytic solutions to the end user in short bursts of time where value is immediately evident.

Agility in analytics doesn’t require that the product be a desktop business intelligence solution delivered in silos. In fact, Agile analytics requires that IT and the business users work collaboratively and quickly to ensure strong IT governance while also providing powerful analytic solutions to the end user rather than having the user take on the whole effort.

New Information Discovery solutions such as Oracle Endeca Information Discovery clearly have embraced the notion of delivering high value agile analytic applications in short bursts of time. Some including situational analytic apps.  We have seen this play out frequently where POC’s (proof of concepts) quickly go into production in a very short period of time because of the compelling value that was delivered in the POC.

Agility also means developing in short product iterations, this enables one to deliver an analytic application, get immediate feedback from end-users and rapidly iterate to deliver new or expanded requirements that deliver further value. We are talking weeks not many months. Some projects have gone from 8 week iterations down to 2 week iterations. Because Agile analytic solutions are typically delivered through a browser in a private or public cloud, versus a silo desktop only tool, the new enhancements are readily available for test and validation by the end user. This capability combined with agility also gives the analytic application developer a chance to quickly show that they are meeting the key requirements of the end user.

With increased agility and business user success, we will eventually turn the corner and agility will be the new norm in analytics.
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