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Watch Out: Don’t Go Down the Wrong Big Data Path
December 11, 2015 Blog big data

This article was originally published by techrepublic.com and can be viewed in full here

This article discussed the value of learning from analytics project failures

A major industrial products company made a huge predictive analytics commitment to preventive maintenance to identify and fix key components before the components failed so the firm’s limited technical services talent could be optimized. Halfway through it was observed that many of the subsystems could be instrumented and remotely monitored in real time as part of a networked system. The insight changed the direction of the entire project.

It was observed that “The value emphasis shifted from preventive maintenance to efficiency management with key customers. The predictive focus initially blurred the larger vision of where the real value could be.”

Many corporate analytics efforts are like this. Companies begin by asking questions, and those questions in turn direct them into certain directions, but sometimes that’s at the expense of other directions that might have yielded greater results.

It is this fear of getting on the wrong track of data analysis that has corporate data scientists and analysts moving carefully and issuing numerous disclaimers about what results might yield along the way. This is also why many of these corporate practitioners look to outside business partners and consultants for help with their analytics.

Businesses are more committed to implementing big data analytics than ever before, but many are still struggling with how to maximize the benefit. Survey results underpin how investing in analytics is just the first step. It’s organizations that go the next level by removing complexities from the analytics process and empowering others in the organization, namely business analysts, that are going be able to turn data insights into actionable business enhancements for long-term success.

The good news is that more and more companies are providing ways to abstract the complexities out of big data and analytics with semantic and reporting tools that sit on top of “raw” processing engines like Hadoop and enable end users and data analysts to easily access and manipulate information to gain business results. This effort is further amplified and expedited when expert partner-implementers are called into the process.

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