Predictive data, the real workhorse behind the Internet of Things

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.


The market for connected devices like fitness wearables, smart watches and smart glasses, not to mention remote sensing devices that track the health of equipment, is expected to soar in the coming years. By 2020, Gartner expects, 26 billion units will make up the Internet of Things, and that excludes PCs, tablets and smartphones.

With so many sensors collecting data about equipment status, environmental conditions and human activities, companies are growing rich with information. The question becomes: What to do with it all? How to process it most effectively and use it in the smartest way possible?

Businesses are learning that it's not enough to gather mounds of data. The data on its own is only marginally interesting. "Where we are today is static," says Vernon Turner, an IDC analyst.

Some current examples in the consumer world exemplify this. A fitness wearable, for instance, might tell users how many steps they've walked in a day. But the device could be much more valuable if it were linked to other health data. In that case, an app could tell the user that lack of activity might explain higher blood pressure results. Or, the device could learn that the user tends to walk less on weekends and send a reminder during a gap on her calendar to get some exercise.

A SunPower Corp. employee points to app that allows homeowners with integrated solar panel roofs to track their home's daily, weekly and monthly power production and consumption. REUTERS/Mike Blake

It's a similar situation for businesses that are collecting detailed information about products in the field and trying to marry it with data from other sources so that they can make smart business decisions.

"It's increasingly coming down to 'what does the rest of the world look like vis a vis your company?'" says Kurt Cagle, principal evangelist for semantic technology at Avalon Consulting, a company that helps businesses manage the Internet of Things. "This is a radical shift in thinking."

Traditionally, businesses have used tools like business intelligence software to look at data about the company's internal activities, he says. But adding other information including public data about the environment or local events, for instance, as well as data produced by sensors that other companies have in the field, can add much more value, he says.

It turns out, though, that combining that data is often tough because it typically comes in different forms. For now, while many companies are moving in the right direction, not many have built fully integrated, elegant solutions -- or if they have, they've had to do a lot of custom wrangling to get it right.

Next section: Putting the pieces together

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