IoT Data Management Challenges

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Who isn’t thinking about the repercussions of an exploding Internet of Things (IoT) market as smart devices further expand their reach in both the commercial and consumer worlds?

John McDonald, CEO of ClearObject, an IoT solution provider, works with companies that are seeking to understand what the technology means to their business, how implementations might cause customer pushback, and of course, what to do with all the data that will be generated as smart objects become the norm.

“It’s an economy based on collecting data points, on creating outputs and using these as golden nuggets to create leads and opportunities,” he said. As much as anything, it’s about transforming physical business models into something radically different from what they are now.

As an example, McDonald points to one ClearObject customer, a small gas-stove-igniter manufacturer in Indiana that’s been in business for over a quarter of a century. It suddenly came face-to-face with how IoT was going to change its business. It wanted to bid on a contract for a large appliance manufacturer, but that manufacturer was already thinking ahead to how it could put sensor technology to work to offer its customers proactive support services.

To win the business, igniter suppliers would have to be able to install sensors that would detect when the igniter was about to fail. When the appliance company received the data, it could take steps to get a replacement to a customer before the event happened.

McDonald said if the company couldn’t figure out how to do it, its CEO knew that some tech vendor in California or China would—and chances are that the manufacturer wouldn’t be in business for the next 25 years. To survive meant becoming an IoT company. “We help companies like that build and run their digital product that augments and supports and differentiates a physical product they make,” he said.

Big Data Gets Bigger

ClearObject’s history of helping companies manage the smarts they’re putting into products has been evolving to aid them in getting data from their products and performing analytics using it.

“The surface area of data collection becomes so huge so quickly,” he said. “We’re really driving past the big data era where you are just trying to collect all the data. There’s just too much of it.”

A rethinking is in order, he said, as manually slicing, dicing, and interpreting an avalanche of data will be unsustainable using human power alone. Marrying smart device data to machine learning models trained by humans is the road to interpreting data—no matter how much there is of it—and assisting people in understanding it.

“That helps you deal with data better because you can ingest more data from products,” McDonald said. “You get faster in understanding what it tells you and in putting it to work to monetize it.”

Expect and Prepare for Resistance

Of course, there’s no such thing as no consequences. In the case of IoT, questions most likely will come—and in fact already are coming—from consumers wondering just how much of their data lives companies should be able to access.

Today, for example, any car under five years old has the sensors and software capacity to learn a lot about and do a lot with individual consumer information. “There’s an explosion of software in everyday devices,” an example being the 2019 Mercedes Benz S550. It has 20 million lines of software code, 14 million of them in the radio alone.

Theoretically, sensors could be coupled with machine learning to understand an individual driver and make recommendations in real time. Its sensors could detect, for example, that at 3 a.m. the car is swerving in and out of lanes, and conclude that the driver might be tired. Combine that with personal data—that there’s a Starbucks two exits away based on the driver’s cell phone location tracking app, for instance—and it’s possible for the auto to employ technology to “suggest” that the driver might want to stop there. Maybe it even knows from other data it has collected that the driver likes flat whites and it can place a mobile order for one so that it’s ready when they get there.

“The car figures that you need coffee around data points,” McDonald said—such as coffee “likes,” spatial awareness and so on. “Those data points don’t matter much on their own,” but it’s a different story when they’re assembled together.

Auto manufacturers haven’t taken things to that level because consumers don’t want them to, he said. There’s concern about that data being available to third parties—maybe to a nearby hotel that could use it to pitch a room, for instance, or to a police department that could use it to prevent an accident, but also have the opportunity to write up a traffic violation. “So, you shut it off,” he said.

But there is a way to use machine learning data to train a model so that only the smallest amount of live data gives value, he said, which can be of true help to a person rather than cause for concern. For instance, if the car’s data shows that it’s early in the morning, it’s not unreasonable to conclude that the person driving the car, whoever they are, might need coffee. “The models can be built with anonymous data. There’s no need to know specifically that you like coffee; it just uses data to infer that you might need coffee,” he said.

Make it a Winning Proposition

A problem, he said, is the industry is not really enfranchising consumers in the value of data-sharing to their own lives. People might not have a smart thermostat because they are worried that its manufacturer might be tracking when they leave the house and when they come back, for instance. That’s not valuable to them. But it might be a different story if that company sent a letter to the consumer every month that thanks them for their data—and encloses a check for the value of it.

“The issue is returning to the source of data value from the data itself,” McDonald said. “If you can trace back to how the data point was used, including where it came from, you can re-enfranchise all the data in value down the line.”

In the very near future, expect business value no longer to come just from what they do—whether that’s making or moving or growing things—but in what they do with data. “It’s a whole new great creator and destroyer of business value,” McDonald said. “It provides a company of any size a chance to completely change the game.”

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