Industrial IoT: Making it Real and Quantifiable

Let’s first agree this is industry, not a consumer market. For industrial manufacturing, this is not a sauce bottle attempting to talk to a milk carton in the refrigerator. Every week there’s more written in email magazines, articles, and blogs about the acronyms and buzz words. What does it all mean? Is it just hype? Is there a trough of disillusionment on its way where all this crashes – for the semblances of truth to reappear later? I think so!

Regardless of what the writers express, such words all head one way; how do we make more with less? How can you avoid the things that stop you and encourage the things that help you?  The way those words are projected can cause confusion and obscure the things needed to achieve the real goals. Just adopting the buzzword technologies does not bring a revelation. IT technologies including business intelligence and data warehouses heralded similar great economic outcomes. Why did such promises remain unfulfilled? Because we were throwing technology answers at unknown problems.

My advice is not to proceed with IIoT unless you clearly articulate the problem and the value that comes from solving it. The IIoT is a platform, a way to gather and aggregate industrial data so that you can do something with it. You can make reports, see trends, make comparisons of this month versus last month. In this way, you may develop insights and make decisions. Is that enough. I submit, the IIoT is little or nothing new without analytics; that’s where the good stuff is! It can bring more than insight; but, it can bring foresight. That’s where we start to predict the future based on patterns that we have recognized as valid indicators of behavior…and where we can change the future.

You hear about Predictive, Operational, Prescriptive, Maintenance Analytics and more. Each is based on analytical techniques involving statistical mathematics. The latest and most effective is Machine Learning (ML); again just mathematics, albeit smart, and ultimately transformative. You all know this technology! It has been used since the 90’s for credit card fraud detection, voice recognition on your smartphone, facial recognition on Facebook, it drives the Google car, etc. Industrial professionals are skeptical that it actually works, but you’ve all witnessed it, perhaps without even knowing it. So, from here let’s discuss only ML since it has proven dominant in all IT fields world-over.

About your Guest Blogger: Mike Brooks is COO and President of Mtell.  Mike comes with 25 years of leadership and management in Exxon, Chevron, and start-up companies in process operations, planning and scheduling, control systems, and IT systems. Before Mtell, at Chevron Technology Ventures he invested in early-stage technology companies and held board positions at portfolio companies. Previously, Mike drove IT technology design, manufacturing intelligence, dashboards, and user interface direction. He participated in four previous IT start-up ventures. Mike invented a manufacturing operations solution and founded INDX Software to commercialize the product; later acquired by Siemens. Mike was also Director of Products and Technology at Wonderware guiding new directions. Mike’s other ventures were in Control Systems, Process Information Management Systems, and Graphical User Interface products. Mike holds a B.Sc. in Chemical Engineering from the University of Bradford, UK.


  1. Well said! The most effective projects start with a business goal, not a misplaced enthusiasm to use a specific technology. For IIoT to grow and users to benefit it must be more than a solution looking for a problem.