How to turn IoT data science into real business value | 7wData

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You’ve succeeded in connecting your devices, which are now dutifully publishing IoT data to the cloud. Now you’re in a position to remotely monitor and control your equipment in a production environment.  Congratulations, that’s no small accomplishment! However, you have bigger plans for your IoT investment. You know there are valuable insights to be gained by applying data science to your burgeoning store of bits and bytes that have the potential to significantly affect your bottom line.

You’re on to something.  

Having worked in industrial IoT for over a decade, It’s become increasingly clear to us at Bright Wolf that much of the value of IoT technology lies beyond the basic connect and monitor scenario, with the ability to analyze IoT data to improve products and operations as well as discover new business opportunities.  

There’s gold in them thar’ IoT data hills.  So how do we get it?  

At Bright Wolf, we’ve seen first hand the astonishing percentage of IoT initiatives that end up either discontinued or in pilot purgatory. It’s not because the technology isn’t really cool (it is), but rather because the initiatives fail to demonstrate any real or potential return on investment. For this reason, we have a pretty unwavering commitment to a methodology we call Zero Waste Engineering™. The basic idea is to employ an iterative discovery process that doesn’t break the bank while proving out the value of your initiative. While this seems like common sense, many industrial organizations are trapped in legacy patterns of development resulting in IoT project failures like the $3 millon spreadsheet and other common missteps on the path toward Digital Transformation.

With Zero Waste Engineering™ as a backdrop, and an eye toward better business outcomes, let’s walk through a proven approach for applying data science to industrial IoT systems.

With a nod to Stephen Covey, it’s imperative to start with the end in mind. If you don’t know where you’re going, you’re unlikely to get there. It’s important to clearly define the problem you are trying to solve, or the specific insight you’re looking for, and it’s equally important that the solution aligns with and supports your business goals.   

A common IoT data science application is predictive maintenance, an effort aimed at eliminating expensive, disruptive, or catastrophic failures. Other common use cases are discovering opportunities to lower operational costs, product and service improvements, and better customer support.

Finding and operationalizing insights to improve your business or enterprise requires a team; you can’t do this work alone, even if you are wearing your super hero cape. You obviously need a data scientist, but It’s critical to identify and enlist others to ensure the best chance of success.  

Your coalition will certainly include management at some level; the project sponsor or champion is responsible for defining the business goals. Of equal importance are subject matter experts who have an in-depth understanding of the equipment or process you’re focusing on. They could be plant managers, operators, or maintenance personnel. Your sales and customer support organizations are likely to provide valuable insights here as well. Product owners and engineers should also have a seat at the table.

Finally, be sure to include information technology (IT) leaders, and involve them early. They will be instrumental in accessing the data needed for your research, and are likely to be involved in anything you ultimately deploy resulting from your work.

Data science is just that – science, and this is how science is done. It’s an iterative process of observation-based “guess and check”. Your hypotheses will emerge from the collaborative efforts of your coalition, and you will generate and prove or disprove many hypotheses over the course of your investigations.