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Big Data ROI will take time, clear goals and talent

Brian Eastwood | July 23, 2013
The big data market is expected, by one estimate, to grow more than 30 percent annually until the end of the decade. But more than half of big data projects fail--and even those that do succeed can fall apart if the findings aren't applied to operational efficiencies. Ron Bodkin, CEO of Think Big Analytics, offers advice to help you prevent your business from becoming just another statistic.

To put it bluntly, big data is a big business, one that Wikibon expects to grow at a 31 percent annual clip and approach $50 billion in 2017 (up from $11 billion in 2012).

So far, though, a lot of that money has been wasted. Earlier this year, an InfoChimps survey found that 55 percent of big data projects fail.

Ron Bodkin, CEO of Think Big Analytics, a San-Francisco based analytics service and solution firm, says big data project failures can be traced to several root causes: No business goal, no alignment with business outcomes, insufficient budgets, poor planning and failure to recognize project scope. (Don't forget about the data analytics skills shortage that will only get worse over the next five years.) Even those who do succeed can fail if the benefits of the big data project aren't realized outside of IT to, say, improve operational efficiency.

Big Data Spending Focuses on Sales, But ROI's in Logistics and Finance
A recent Tata Consulting Services survey, The Emerging Big Returns on Big Data, finds that firms around the world are focusing their big data investments on sales, marketing and customer service more than any other business function. The so-called " gold in big data", Tata says, "exist[s] in numerous corners of a large, global company," with the highest potential benefits attached to examining data about customer value and needs, product quality, campaign effectiveness and inventory tracking.

Companies expect the best ROI, though, from big data projects for logistics/distribution and finance. Marketing ranked last among the eight business functions Tata tracked; marketing executives were most concerned about how their firms would organize and optimize data from disparate information silos, "reskill" IT to use big data technologies and, above all, handle the volume, velocity and variety of big data.

Bodkin sees several industries primed to benefit from investments in big data. Manufacturers can use test data to drive efficiency and improve cycle times. Financial services, an early adopter of big data, can integrate "less traditional," unstructured data sets and conduct intraday trade analysis. Online services such as Quantcast-where Bodkin was vice president of engineering prior to leading Think Big Analytics-can measures all sorts of information about visitors of all sorts of websites.

Internet of Things, Healthcare Present Big Data Opportunities
Two fields in particular interest Bodkin. The first is the Internet of Things, where the capability to collect data from smartphones and a whole host of connected devices can improve sales, drive project management decisions, improve efficiency, reduce waste and drive what firms such as General Electric deem the " Industrial Internet."

 

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