Much has been written about Industrial IoT and its tremendous potential to help businesses improve operational efficiencies, drive down costs and enable new revenue streams. However, while opportunities to apply IoT within industrial settings abound, pitfalls face companies that fail to consider all aspects of IoT necessary to address real business problems.
The perception companies have of “IoT” and why they should implement IoT technologies can be misleading. In industrial segments, the “internet” is either not involved or merely incidental to the work at hand. Further, many of the “things” used to collect data from are expensive, complex capital assets.
The problem is that “IoT” implies that what’s really important is connectivity, but for most IIoT initiatives, connecting devices and transporting data are only the initial steps. They do not, by themselves, solve real business problems, says Kevin Walsh, VP Marketing, Bsquare.
In order to truly yield value and positively impact the business, IIoT initiatives need to go far beyond simply digitising devices and connecting them to enterprise systems. The act of connecting devices and extracting data from them (basically what M2M involves) is only done so that we can use the data to derive novel insights and apply those insights back to the enterprise in an automated fashion. Hence, it is “data,” not “internet” or “things” that is fundamental to IoT.
What, then, do industrial businesses need to do in order to ensure that their IoT initiatives deliver? There are two key areas within a broadly defined IoT umbrella that are critical in order for the technology to live up to the hype. The first is data analytics and the second is orchestration.
Data analytics, more of a discipline than a technology, can be harnessed in service of a number of goals. First, it can be used, along with machine learning, to develop complex digital models of physical assets in the real world.
These models, sometimes called “digital twins,” ideally include behavioral aspects (i.e., not just what the current device state is, but why) in order to be able to accurately predict future conditions. Second, data analytics is what allows IoT systems to do something that humans can’t: sifting through oceans of data in order to glean meaningful insights.
But data analytics by itself, while important and useful, still doesn’t completely address business challenges established at the outset. This is because the output of data analytics is often insight. Humans can use this insight to redefine processes, improve product design, and other useful tasks but, unfortunately, scalability and reliability suffers. The goal for many IoT systems is to automate processes.
Orchestration is a term used to define the range of activities that should automatically take place whenever data analytics discovers something that is actionable. Rules are typically used to parse through massive data streams, pick out complex conditions, and then invoke pre-defined sets of actions that deal with those conditions.
Yes, one of these actions may be to text or email someone, but that should just be informational. Scalability, reliability, accuracy and cost are all improved if the IoT system can automatically take actions. Interestingly, some of these actions may be to modify the IoT system itself, say when the accuracy of predictions drifts in the wrong direction.
The bottom line: yes, industrial IoT has the potential to wring billions of dollars of cost out of industrial infrastructure while improving efficiency and uptime. But these benefits are not a slam dunk. Simply connecting devices, extracting data, and dropping that data into “data lakes” will not, by itself move industrial companies in a positive direction.
These are simply pre-requisites to the really interesting and valuable work: data analytics and orchestration that close the loop on these complex initiatives making them dynamic, self-correcting systems that truly address underlying business objectives.
The author of this blog is Kevin Walsh, VP Marketing, Bsquare
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