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Predictive data, the real workhorse behind the Internet of Things

Nancy Gohring | June 12, 2014
Companies are having to be pickier about which data sets they collect, process and use. It's all about having the right architecture in place.

That means Adidas could recommend products to an individual user based on her results. For instance, it could send a message to the user congratulating her on her improvements putting spin on the ball and recommending a new Adidas shoe with a special design that could improve her shot even more, Murphy says.

Similarly, Adidas currently offers a device that runners can attach to their shoes to monitor their performance. In addition to offering value to users, the data allows Adidas to market relevant products to them, he says.

Adidas has also been selling smart apparel to professional sports teams, and last year all Major League Soccer teams began using them. Athletes wear shirts that have electrodes and sensors woven into the fabric. The shirts transmit more than 200 data records per second and a coach on the sidelines can use an iPad to monitor individual players, compare two players or view the whole team. Coaches can also view players' heart rate, speed, acceleration, distance and field position.

"We're seeing real adoption at the professional level," Murphy says of Adidas products aimed at top-tier athletes. In fact, he says, some teams are hiring new staff members to focus on how to best use the data from its smart products in ways that can help the teams train smarter.

-- Nancy Gohring

Building a crystal ball

NCR, which similarly collects information about the status of many of its products, including ATMs, self-checkout machines at grocery stores and movie theater ticket kiosks, is also using predictive analytics to get ahead of problems, says Mark Vigoroso, vice president of global services strategy and program management at NCR. The predictions indicate that a failure is likely to happen -- usually with a few days notice -- giving technicians time to get to the site with the right diagnostic and repair equipment before a failure happens, he says.

NCR has been doing this kind of prediction for several years, but Vigoroso says previously "it was a smaller operation with less precision, less accuracy and less coverage." That said, it is still the "early days of capturing the value of predictive services. Our effectiveness depends on how broad our predictive logic coverage is."

Our effectiveness depends on how broad our predictive logic coverage is. Mark Vigoroso, vice president, NCR

NCR has done some pilot programs where it marries data collected from its machines with other sources of data to draw different types of conclusions. For example, it has combined weather data with equipment performance data to look for patterns that might indicate that heat, humidity or cold are impacting equipment performance, Vigoroso says.

It has also started using cash management data, which it already supplies to customers of its ATMs, in new ways. NCR has long notified banks about nearby events like a major sporting game so that the bank can ensure an ATM will have enough cash to support users.

 

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