Predictive data, the real workhorse behind the Internet of Things
It's not just about collecting mounds of data anymore, but analyzing it to make smart decisions.
Computerworld - 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.
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, LLC, 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.
Putting the pieces together
"We see countless companies that are past the part of experimentation and deploying sensors and collecting data" but that don't have a fully integrated solution, IDC's Turner says. "It's the complexity of the implementation."
Businesses need infrastructure on the back end that enables the combination of data from various sources as well as the analytics power to make sense of it all. Then they need dashboards or visualizations that let line of business people understand the meaning of the data so they can make smart decisions based on it, he says.
Daikin Applied is one company that, with the help of partners, has put together a sophisticated set of hardware and software that collects and analyzes 4,000 different data points about its commercial heating and air conditioning rooftop units. The system, designed with Intel, syncs with weather forecasts to allow building owners to adjust for changing temperatures in advance and lets Daikin know when changes in energy use by individual components indicate a failure is imminent so that the company can dispatch a repair technician beforehand.
In the future, the system also will let Daikin feed important data to local utilities that might be able to use it to reduce the power output to any given piece of gear. Talks with utilities are in preliminary stages right now, says Kevin Facinelli, executive vice president of operations at Daikin Applied. (Daikin Applied is part of Daikin Industries, the largest HVAC manufacturer in the world.)
In this implementation, hardware plays an important role. The system starts with a gateway that's based on Intel's Quark system on a chip (SoC), runs Wind River's operating system and is secured by McAfee software.
"Instead of just passing all the data through to the cloud, we have an SoC so we can do pre-possessing," Facinelli explains. That means the gateway, which will be built into all future Daikin rooftop systems, sends only important data, like a change in status of a component, rather than sending along an endless stream of "I'm normal" signals, he says. Doing some processing on site reduces the volume of data that needs to be transmitted -- Daikin primarily uses cellular connectivity -- and also helps to reduce the data warehousing load on the back end.
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