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 chain monitoring.
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.
While an analytics tool might seem like an obvious step in designing the optimal IoT strategy, remember that this isn’t our first time down this road. What many do not realize is that the Internet of Things as we know it is, in many ways, the second iteration of retail data-collection technology. In the early 2000s, those in the retail industry saw RFID in the same light as the IoT: a means to collect mountains of data on customers and their purchasing habits. Despite investment from several industry giants, it never fully reached its potential. Why? A lack of reporting. In those days, RFID was never matched with any sort of long-term solution to draw meaningful insights. Tools such as prescriptive analytics help IoT technologies, including RFID, succeed where they had failed in the past.
The IoT isn’t filtering out and streamlining data—in fact, it is doing the opposite. The sheer volume it is creating is staggering. To maximize the value of the IoT, the data it generates must come paired with the right solution that sifts through the junk and hand-delivers the value. While there are other options, what makes prescriptive analytics the ideal candidate for that task is its instantaneous value-added functionality. Real-time delivery of actions can result in quick fixes which, over time, can save a fortune in profit. Prescriptive analytics is the next logical step in reaping the true value of IoT data.
Guy Yehiav is the CEO and chairman of the Board of Profitect, a leading provider of prescriptive analytics. Prior to working at Profitect, Guy Yehiav served as the VP of sales and strategy for Oracle‘s Value Chain Planning Solutions division, where he was responsible for sales, strategy and customer success. Yehiav was also the founder of Demantra US, a global provider of demand-driven planning solutions that was acquired by Oracle in 2006. Previously, he directed the Global Professional Service Group, where he was in charge of creating methodologies and infrastructure through value chain transformations that enabled demand-driven and seamless operations for Fortune 1000 companies.