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The IoT Is Analytics: Making Sense of All the Data

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The Internet of Things (IoT) is becoming a reality in spite of the marketing hype surrounding it. The concept sounds like something too good to be true anytime soon—a myriad of devices connected and talking to smart services. Most consumers find it hard enough to set up a new cell phone much less believe that their appliances can tell manufacturers about impending parts failures in real time.
The electronic parts in devices have traditionally been independent. Sometimes, like a car with a diagnostic port, trained technicians plug in specialty devices to read esoteric codes. People understand that. It’s a discreet process.
Now, with the increase in connectivity around the world, consumer and industrial devices are accessible remotely and transmit a large amount of data about their status and performance. This is the IoT: a large of amount of data flowing into data centers.
The future of the IoT is in using those data. A few companies do it now; many more don’t or at least make unsuccessful attempts at using it. Most know that deriving any value from the IoT requires analytics to distill meaningful and actionable information.
Most companies will go through four distinct phases before they truly maximize the value of the IoT. Most companies are in the first phase now.
Phase 1: The Gathering
Like a school of fish, companies feel that they must at least appear to move in the same direction as the rest of their industry. They need to market their devices as being part of the IoT, which means that they need to set up some sort of receiving database system that stores the data that their devices throughout the world send in. This is where most businesses are now, with a few moving into the next phase.
Phase 2: Reporting
The first step in analytics is to report on what happened. Tools exist to create and display reports on trends and outlier events that occurred and were recorded in the IoT database. Admittedly, reporting on IoT data is not exactly the same as reporting on financial or transactional data, although those skills are a good start.
Creating nice graphical reports about a trend in usage or the number of failures by product over time may be useful to company engineers and customer service. Even if such information isn’t useful, however, using pretty pictures to get management and IT used to the idea that the data in the IoT database are worth something is a good internal public relations move. The goodwill and interest such presentations generate help fund the next phase.
Phase 3: Discovery
There is too much data in IoT databases to realistically glean much meaningful insight using traditional reports. The variety of fields about machines and their performance is just too large for people to digest even in summary formats with large graphs.
For this amount of data, businesses need analytics. The kind of analytics they need involves statistical analysis, predictive modeling, and other techniques to find actionable information from mounds of data.
Phase 4: Proactive Action
With predictive analytics comes an opportunity to be proactive and change what might happen in the future. The models may predict the factors right before a piece of equipment is going to fail. The predictive model is deployed to scan, in real time, the incoming information from the IoT to look for the characteristics immediately preceding a failure point. Automated systems take action either through notification or by sending fixes to the soon-to-fail system automatically.
In the End
There is no end. For the foreseeable future, the IoT will be inextricably linked to analytics. As analytic systems become better and easier to use, IoT analytics will move from a technical, specialized function to something the average business analyst can use. For now, companies that think of themselves as involved in the IoT need to start looking to the future. That future is analytics.

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