IoT Analytics: How Solution Providers Can Bank On Data Proliferation

One indisputable fact about the Internet of Things: It generates data. Lots and lots of data.

And some solution providers are already reaping the rewards of applying business analytics to that data, turning it into valuable information and insights for their customers in industries as varied as health care, pharmaceuticals, agriculture and utilities.

"We call ourselves the data-to-decisions enabler by helping [businesses] monetize data as a strategic asset," said Manav Misra, vice president, chief knowledge officer and chief scientist with Cognilytics, a San Jose-based solution provider, in an interview.

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Market researcher International Data Corp. predicts there will be 28 billion connected devices by 2020, while research firm Gartner puts the number at 32 billion. Those devices include everything from sensors in farmers' fields gathering temperature and moisture data, to RFID (radio frequency identification) tags on shipping crates broadcasting location information, to athletes' "wearables" collecting heart rate statistics.

Businesses want to capture and store that data for analysis. In many cases they even want to analyze streaming data in real time -- something that has become really practical only in recent years with a new generation of business analytics software. IDC forecasts that adoption of technology to continuously analyze streams of data will grow at a compound annual growth rate of 30 percent for the next five years.

Some solution providers have been quick to recognize the opportunities. "We're dedicating a huge amount of resources here because we see it as one of the fastest-growing [IT] areas," said Bill Busch, a senior solutions architect who leads the enterprise data practice at Perficient Inc., a St. Louis-based solution provider (No. 66 on the CRN Solution Provider 500) that works with IBM, Microsoft, Oracle and other major IT vendors.

Perficient launched an Internet-of-Things practice earlier this year that helps customers capture and store real-time IoT data, Busch said. A separate data science practice works with customers to set up processes for analyzing the data.

Perficient is working with an electric utility (which Busch declined to identify) to collect and analyze data generated by the "smart" meters at homes serviced by the company. Right now the focus is on detecting fraud -- cases where meters have been tampered with to reduce electricity charges -- but the utility also plans to study the collected information to develop a peak-usage rate plan.

Another Perficient customer, a pharmaceutical company Busch also declined to name, is collecting heart rate and other health-related data from wearable devices worn by patients using the company's drugs. The company hasn't even determined what kind of analyses it will use the data for, Busch said. Possibilities include detecting adverse effects of the drugs or learning how they may be improved.

But the company knows the data will be valuable and it has engaged Perficient in a three-year project to upgrade its IT infrastructure to handle what the company expects will be huge volumes of IoT data.

Cognilytics' Misra said the demand-drivers are clear. "Sensors are becoming cheaper. Storage is becoming cheaper. Connectivity is becoming prevalent." With the cost of collecting so much data so low today, the challenge for businesses -- and the opportunity for solution providers -- is helping businesses derive value from the data.

"We look to call ourselves the data-to-decisions company," Misra said in an interview. "These projects are real. They're generating revenue for us and delivering value for customers."

Cognilytics first got into the IoT data analytics business almost accidently: It began working with a manufacturer of commercial and residential HVAC (heating, ventilation and cooling) systems, collecting data from a thermostat system with a debugging mode. The two companies soon realized that data could be used for energy consumption and efficiency analysis, according to Misra.

Today Cognilytics has a quickly growing business in collecting IoT data from a wide variety of devices in a broad range of industries. The company's forte is organizing and formatting the data, using it to develop predictive models and conducting predictive analysis, and presenting the results back to customers in the form of alerts, visual dashboards and other means.

In the oil and gas industry, for example, Cognilytics collects and analyzes data from equipment on offshore drilling rigs to help predict equipment failures. It's working with a bus fleet owner to collect and analyze data from sensors on buses to predict when parts are about to fail. Cognilytics, which partners with SAP and Cisco, among other IT vendors, also has IoT predictive analysis solutions for the manufacturing, healthcare and retail/consumer packaged goods industries.

"The IoT is definitely a very rapidly growing part of the work we do," Misra said. While he declined to provide revenue figures, he said it could account for as much as half of Cognilytics' business in the not-too-distant future.

In December, Cognilytics was bought by communications services giant CenturyLink for an undisclosed sum, and Misra said the potential demand for Cognilytics' predictive analysis solutions for IoT data was definitely one of the attractions for CenturyLink.

Business analytics for IoT is still early in its evolution and so are the channel partner models. Case in point: Body Biolytics, a startup that's developing analytical algorithms for analyzing physiological data collected from wearable devices. Those algorithms are based on data mining and predictive analytics technology from RapidMiner.

President Kevin Logan launched Body Biolytics based on his experience with his other company, Stonington, Conn.-based MACSEA, which develops "ship health monitoring solutions" that collect and analyze data from sensors within diesel engines and other systems on commercial and U.S. Navy ships. The software predicts machinery failures and helps ship operators reduce maintenance and operating costs.

Logan's idea is to take the ship health-monitoring concept and apply it to people. The company is initially focused on the sports fitness area, using its Rapid Miner-based predictive analytics algorithms to analyze athlete's hydration, heart rate, respiration and blood pressure in real time. Last year an early version of the software won first place in a contest sponsored by sports apparel company Under Armour in which developers submitted ideas to expand the capabilities of the Armour39 digital fitness performance monitor.

The market for wearable devices is very crowded and Logan noted that Body Biolytics isn't building the devices. "Our focus is on the [analytical] algorithms," he said. Some fitness- and health-related wearable systems can have as many as 20 sensors that combined generate 2.4 gigabytes of data per person, per day, Logan said. The Rapid Miner tools are "the kind of technology we're going to need to automate the processing of these [IoT] data streams."

Once Body Biolytics proves the accuracy of its software, Logan says it could be applied to a wide range of biometric monitoring applications in such areas as sports, health care and elderly care.

"These are real-world things that are happening now," he said.