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Completing the picture: Integrating IoT into your analytics strategy

In just a few short years, the internet of things has revolutionized the way many organizations do business, and the results are starting to show: McKinsey has estimated that by 2025, the economic impact of IoT applications could be as high as $11 trillion per year.

Every minute of every day, valuable data from IoT devices — ranging from smartphones to equipment sensors to “smart” shelves in retail stores — zooms its way into our servers to be ingested, warehoused, analyzed and (ideally) leveraged as a strategic asset. You can almost hear the shouts of victory from elated C-suiters as they watch the real-time data flowing in, thinking to themselves, “With this data and these insights, our competitors don’t stand a chance.”

But here’s the problem: Far too many organizations stop there. Yes, the data collected by smart devices is powerful in itself, but it pales in comparison to what is possible when it’s integrated with your overall analytics infrastructure. Instead of an “IoT data strategy,” what organizations really need is a comprehensive analytics strategy that harnesses the power of their IoT data.

Integration in action: A case study

Let’s look at a real-world example. A company that monitors restaurant equipment had installed smart sensors to track the performance of its customers’ refrigerators, ovens and other kitchen assets. In assessing the performance of its customers’ equipment, the company noticed that the ice makers at some restaurants were failing faster than expected.

When it looked only at its IoT data — compressor temperatures, amount of ice made and usage per day, hours of operation — it was unable to isolate the source of the problem. It was only when the company looked beyond its IoT network that the failures began to make sense: Early failures tended to occur in restaurants that baked their own bread, which caused yeast to collect on the ice makers’ compressor coils. Finally, the source of the problem was identified … but only when they looked at the bigger picture.

Starting with “why”

Before organizations jump into creating a strategy for their IoT data, it’s critical that, as Simon Sinek would say, they “start with why.” What goals do you aim to achieve by using this IoT data? Is it to provide better customer service on a case-by-case basis? Or is it to improve the overall brand experience? To answer this, we need to defer to our overall business strategies.

Getting back to the restaurant equipment company in our case study, its goal might be to have the brand perceived as the premier provider of industrial kitchen equipment, with a promise of zero failures. If it’s going to reach that goal, it’s going to need predictive analytics — not only to tell it that a piece of equipment is in danger of failure, but to initiate a process that closes the loop and ensures the matter is handled proactively.

For example, if a piece of equipment has a 25% chance of failure over the next three months, the action item might be to contact the service manager and recommend that he schedule an early servicing. If the chance of failure is closer to 90%, it could schedule the service automatically; it’s what the service manager would have done anyway, and it’s saving him the trouble of picking up the phone.

Closing the loop

As with all decisions involving technology, our process for leveraging the data from our IoT networks must begin with our mission to deliver greater business value.

We need to approach our IoT data the same way we would approach a product launch. Now, I’ve been involved with enough product launches to know that if the technology is the starting point, the product is doomed. Technology is the “how” that lets us achieve our business goals — never the “what” that we’re aiming for.

That means building teams that reach beyond the IT department and bringing in team members who understand our customers and their world. It means keeping our customers’ needs and our overall business objectives at the forefront of every decision. And it means evaluating our results to make sure our IoT data continues to serve the aspirations of our organizations. If we can do that, we’ll be well on our way to a strategy that harnesses the power of IoT data in transforming our organizations for the better.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.