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On the Marriage of IoT and Prescriptive Analytics

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Oct 19, 2016

For many businesses, data is a precious commodity. Its ability to reveal insights about an organization is unprecedented, which is why it has become a major driver for the adoption of the Internet of Things. The IoT’s network of interconnected devices can produce a remarkable amount of raw data, which is proving to be very attractive for business leaders across industries. Earlier this year, in fact, Gartner released a report revealing that 43 percent of businesses will have launched their own IoT strategy by the end of this year. But are CIOs and their counterparts thinking long-term about their IoT solutions?

There are plenty of reasons to invest in the IoT, but without pairing the data it generates with the right tools, you’d effectively have the world’s most valuable book without any way to read it. Data collection alone is not enough to draw value; the real value is found in translating that information into insights and actions, which can be taken immediately to improve operations. That’s what makes prescriptive analytics a natural fit for any IoT solution. Once integrated with IoT devices, prescriptive analytics tools collect information and intelligently identify trends. The idea is to ingest the data, look for patterns of behavior through machine learning and spit out a descriptive insight, combined with a prescriptive action. These are then delivered in real time to the most relevant person. In simple terms, this eliminates the need for a data scientist to review and submit reports. It creates a constant loop of data collection, translation, insight delivery and action. It is, interestingly, similar to what the retail industry is doing with RFID for cold chainmonitoring.

According to IDC, the industry with the highest investment in the IoT in 2015 was discrete manufacturing. How could manufacturers benefit from pairing their solution with a prescriptive analytics tool? Let’s look at car manufacturers, specifically. Through IoT-connected devices, data is collected at every step of production. During a set period, the prescriptive engine identifies that one station is taking X number of seconds longer to complete its task, compared to Y fewer seconds at other stations, resulting in the loss of $Z in total profit. The prescriptive analytics tool then immediately flags this to the appropriate engineer, and provides recommendations on how to fix the problem.

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