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?
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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, 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.
Daikin also uses a power meter that monitors the supply coming into the unit. Via the gateway, it sends data about the power signal to an Intel cloud, where it's analyzed to determine the power usage of each component inside the HVAC system, like fans and refrigerant compressors.
Without the back-end analytics, Daikin would have to install meters on each component, an implementation that would be prohibitively expensive, Facinelli says.
Once the component energy use data is available, it's sent to Daikin's cloud, running on Microsoft Azure, where Daikin uses it for fault detection and diagnoses and to predict if the equipment needs maintenance.
Many businesses have been collecting data about equipment in the field for years. But what's new now is that they can collect enough data, and the right kind of data, to do predictive analytics.
At Daikin, the data about individual component use of energy is very valuable.
"Over time if you see energy increasing for a motor, it can be a good indication that the motor is starting to fail," Facinelli says. Technicians have enough advance warning, probably a month, before the failure happens so they can service the unit before problems start.
The energy use data also means Daikin can change filters only when needed, rather than on a regular schedule. That's because components like the fan have to work harder, pulling more energy, when pollen and other material clog the filter. "Instead of changing the filter every week or every month, we do it when it needs it, based on performance," he says.
Daikin and its partners have been working on its system, including the gateway and the power meter, for about a year and have six installed systems as a field trial. The technology will be built into all units going forward and can be retrofitted into units built since 2008.
A number of technologies had to be available for the companies to build this system. Mobile, cloud, analytics and a good user experience were all necessary, Facinelli says. "It isn't about a lot of data but about contextualizing it for the user," he says.
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."
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.
That same data, it turns out, is now helpful to NCR internally, because the company can use it to make predictions that help with machine maintenance. NCR knows how many card swipes the hardware can take before it begins to fail or how many receipts a printer will handle before it will have problems. Being able to factor in heavy usage related to events in advance allows NCR to more accurately predict when a component should be serviced -- before it fails.
"That's the part we're excited about. The new technologies that allow us to look across multiple data sets that allows us to crunch those numbers that we weren't able to do previously," Vigoroso says.
NCR is using Aster software from Teradata, a company that was spun off from NCR in 2007. Aster lets users create SQL-like queries to do complex analysis in a simple way, says Brian Valeyko, senior director of enterprise data warehouse and business intelligence for NCR. Analysts can make queries in an isolated environment without having to fear any negative effects on production apps, he says. NCR has built a unified data architecture that allows those queries to pull from Aster datasets as well as from other Teradata warehouses and Hadoop, he says.
The setup allows NCR to build new queries much quicker than it used to. In the past, it might take three to six months to build a new algorithm to do predictive analysis about a given component, Valeyko says. Plus, depending on the size of the data set, those algorithms might take days or weeks to produce results. With its current implementation, Valeyko figures the company can now run through that process in 20 percent of the time it used to.
That allows it to tackle new types of analysis, by correlating data, for example. Valeyko describes a scenario where NCR can now look at data about a printer component that's used in many different products. Rather than just knowing that the printer is having problems in all the products, analysts can discover, for instance, that it's actually only failing in products where it's combined with a certain kind of power supply.
For now, companies like Daikin and NCR have pieced together their sensor-analysis systems, using some off-the-shelf products plus plenty of their own development. Will it get easier? "Absolutely," says Avalon Consulting's Cagle. Once more work is done on easing the pain around unifying different kinds of data, putting together systems like what Daikin and NCR have won't be quite so challenging, he says.
This article, Predictive data, the real workhorse behind the Internet of things, was originally published at Computerworld.com.
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This story, "Predictive data, the real workhorse behind the Internet of things" was originally published by Computerworld.