BOSTON — When he was asked at Thursday’s MIT Sloan CFO Summit if Big Data is just a buzzword, a marketing tool vendors use to sell more software to CFOs, Justin Borgman, the founder and chief executive officer of Hadapt, a data-analysis software vendor, answered honestly: “Yes. It’s an excuse to sell you more stuff.”

However, he added, “it’s stuff you’ll eventually need.”

Borgman, along with Ron Gill, CFO of cloud enterprise resource planning provider NetSuite, and Jeanne Johnson, business-intelligence principal at KPMG, discussed what Big Data can do for business and what business can do to harness the power of the data it already has.

Surprisingly, the latter could turn out to be more important than the former in the short run. Business already has lots of data; it just doesn’t know what to do with it. Before tackling the massive quantities of unstructured data such as tweets, product reviews, and Facebook “likes” that comprise the volume and variety of what’s known as Big Data, companies should first figure out how to make use of the structured data — numbers in spreadsheets, numbers stored in a business’s servers — they can retrieve from their operational systems.

“Big Data is a great opportunity,” KPMG’s Johnson said. “I think it’s changing the way business is going to be done, but it takes time to get the good foundation to harness the data.”

Another word for that foundation is infrastructure. Gill emphasized that many businesses do not have the supply chain and manufacturing infrastructure in place to act on the data already available to them, much less the ability to collect and analyze new kinds of data. He described the case of a sportswear company that analyzed Twitter streams and discovered that people in the Deep South really liked New York Jets quarterback Tim Tebow. It didn’t take a genius to figure out that Tebow jerseys would probably sell well south of the Mason-Dixon Line.

What it took, said Gill, was an infrastructure that could respond to the data by increasing the manufacture of Tebow’s number 15 Jets jersey, and a supply chain that could get them into the stores “in advance of the demand showing up” in the point-of-sale or inventory reports. The manufacturer used the Big Data — the Twitter sentiment analysis — to predict demand before it surfaced in the stores and, said Gill, it sold a lot of jerseys. But without that manufacturing and supply-chain capability, the data, big or small, would have been useless.

The sportswear company knew what it was looking for when it began analyzing the Twitter feeds: sentiment that would translate into demand that would directly affect its product and its sales.

“With any Big Data project, if you don’t spend time thinking about analysis, you’re wasting your money,” said Gill. “You must have a structured idea of what you want to get out of unstructured data.”

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