Moving from The Internet of Things to The Analytics of Things

The Internet of Things (IOT)—the vast array of machines, sensors, and wearable devices generating reams of information—is one of the main sources of Big Data. And it’s expected to keep growing. The number of connected things will grow from 4.9 billion in 2015 to 25 billion by 2020, according to a study from research firm Gartner Inc.

Connected things, such as automated teller machines, have existed before. However, Gartner notes, that new devices and novel items are being reinvented with digital sensing and computing capabilities by enabling these objects to transmit information streams about their status and surrounding environment.

“However, CIOs must understand that the most disruptive impact and competitive threats—and, equally, the greatest competitive opportunities—arise not from simply digitalizing a product or service, but from creating a new business model and value proposition,” noted Steve Prentice, vice president and Gartner Fellow, in a press release about the study.

Tom Davenport, independent senior adviser to Deloitte Analytics, agrees—going on to note in a blog post—that while the phrase IOT suggests that the most important attribute of sensors is their connectedness, analytics are needed to make sense of the data generated by the sensors and for the devices to take intelligent action. Davenport describes this as “The Analytics of Things.”

“The primary virtue of connected analytics is that you can aggregate data from multiple devices and make comparisons across time and users that can lead to better decisions,” he points out. “Comparative usage of an important resource such as energy, then, is one key analytical approach to connected data.”

Other types of Analytics of Things include:

  • Understanding the reasons of variation.
  • Detecting anomalies, such as a temperature being too high.
  • Detecting potential maintenance problems in machines before they occur.
  • Using data to optimize processes, such as cutting wood at a lumber mill.

“Predictive asset maintenance suggests the best time to service machinery, which is usually much more efficient than servicing at predetermined intervals,” according to the post. “A municipal government could analyze traffic data sensors in roads and other sources to determine where to add lanes and how to optimize stoplight timing and other drivers of traffic flow.”

As far as which types of organizations should begin to invest in The Analytics of Things, Davenport suggests that companies that have relatively little experience with sensor analytics or that have exposure to fast-moving data should begin building their sensor data analytics capabilities.

In addition, firms that see a lot of data coming and no way to make sense of it—such as those planning to use drones to capture large amounts of video—should quickly focus on capabilities to analyze video data and detect anomalies with little human intervention.