Big Data and Analytics Are an Ideal Match

By Tony Kontzer Print this article Print
big data and analytics

Companies like Guess? and Ford Motor are finding that big data and business analytics are like love and marriage: You can't have one without the other.

Big Data Analytics Drives Success

At Ford Motor, big data-enabled analytics has been tied to $100 million in annual profits, a figure that led to a recent analytics award from the Institute for Operations Research and the Management Sciences. Part of that success is attributable to the efforts of Michael Cavaretta, technical leader for predictive analytics and data mining for the automaker's research and advanced engineering group, who is focused on using data to improve Dearborn, Mich.-based Ford's internal business processes.

Cavaretta's team is using a combination of big data tools and business analytics applications in a number of interesting ways. They're creating data mashups of previously siloed information by linking business processes to warranty and marketing data and the like; crunching internal and external social media posts and figuring out how to link them with and inform business processes; and capturing huge amounts of data generated by vehicles—not only to refine vehicle design, but also to determine what additional types of data could be collected.

The latter of these, in particular, has huge implications as automakers add more sensors to vehicles so they can monitor performance, crank up customer service levels and improve future designs.

For instance, Ford's Fusion Energi plug-in hybrid generates and stores 25 gigabytes of data per hour on everything from engine temperature, speed and vehicle load to road conditions and general operating efficiency. That data flow can increase to as much as 4 terabytes per hour when running tests with special instruments—instruments that Cavaretta says could easily become standard equipment in a few years. Being able to capture, store and analyze that data, and then apply the insight to the right processes in real time will require finely tuned big data and analytics platforms.

Along those lines, Ford has been experimenting with a gamut of open-source and commercial technologies. Cavaretta says his team has worked with big data tools such as Hadoop, HIVE and Pig on the big data side; traditional databases such as SQLServer, MySQL, Oracle and Teradata; BI and BA software such as IBM's PASW Statistics and R; and specialized data mining tools like Weka, RapidMiner and KNIME.

It's an assortment that flies in the face of predictions that big data was essentially a replacement for business intelligence.

"The initial impression a lot of people had was that this was going to be a whole new thing: Put in big data and business intelligence goes away," says Cavaretta. "I don't think that's the case. There are a lot of BI initiatives that would be greatly helped by big data."

That said, Taneja Group's Matchett believes one of the big data mistakes that companies can make is to jump the gun before a viable business intelligence or analytics solution is in place. "If I just invest in big data without an application for it, I'm not going to get much of a return," he says.

As Ford works to refine its big data-business intelligence/analytics intersection, there's little doubt in Cavaretta's mind that the combination of the two is powerful.

"The biggest thing about big data is that it changes the value of analytics," he says. "People have been focusing BI/BA on large data sets, but not at the level where big data needed to play.

"Now, new tools are giving them the ability to analyze data in new ways. Soon, technology will make things relatively easy, and what you'll be left with is analytics that can give the company value."

This article was originally published on 2013-05-21

Tony has been writing about technology and business for nearly 20 years and is a contributor to Baseline and CIO Insight.

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