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How Analytics Will Enable a Better, Smarter IoT

Get ready for far more intelligent devices.
May | 1 | 2017


May | 1 | 2017

I’m old enough to remember the rise of the Web. Twenty years ago, bullish industry experts and thought leaders portended the end of just about every brick-and-mortar business: commerce, food delivery, and even currency were supposed to go the way of the Dodo. (For a trip down memory lane, click here.)

Of course, we know what happened. Ridiculous dot-com valuations crumbed and very few of those original entrants remain. For every Amazon, Google, and eBay, thousands of companies have perished.

In retrospect, though, some ideas were truly terrible. Still, some early startups vying to disrupt traditional industries might have failed not because their models were “wrong” but because they were too early. Big difference. Indeed, a look at today’s landscape reveals no shortage of companies using technology, data, and analytics to do things that simply weren’t possible even a decade ago. It’s not hard to imagine primitive 1998 versions of Uber and Airbnb. For all I know, a few college kids were planning on hatching ride- and home-sharing websites when the bubble burst.

Today’s Dot-Com Analog: The IoT

Today, organizations can go beyond merely fixing servers, trucks, and airplanes after they break.

I often think about original dot-com companies in today’s context. In a parallel vein, I’m hardpressed to think of a more overhyped concept than the Internet of Things. Its potential is nearly impossible to overstate, yet relatively little of it has arrived yet—and that is very much the operative word. Twenty years from now, I suspect that we’ll look back at 2017 through a similar lens: the dots were there but few companies and industries connected them.

Rather than wax poetic about high-level trends, consider the following simple question: When will equipment fail? As Doug Bonderud writes:

For IT professionals, the use of data to address tech problems is nothing new. What’s changed is the amount and quality of this information. Ten years ago, companies were stuck in reactionary mode: Lacking the tools to analyze and act on real-time data, IT experts were forced to wait until software or services failed and then use the resulting data to address specific issues.

This is no longer the case. Put differently, waiting for the shoe to eventually drop seems so 1998. Today intelligent people and organizations can go beyond merely repairing servers, trucks, and airplanes after they break or fail. Expect continuous monitoring to become commonplace. To this end, I’ve heard the term predictive maintenance more in the past two years than in the past two decades. At a high level, though, how does this happen?

The short answer is that, thanks to increasingly smart sensors, we’ve got more data and better analytics than ever. As anyone with a modicum of statistical experience knows, better data leads to better predictions and, ultimately, better business outcomes. For instance, professionals will know that a given vehicle has a 20-30 percent chance of failing in the next two weeks.

Simon Says: Better data often yields better analytics and outcomes.

Of course, this isn’t always the case. With greater noise, it can be difficult to find the signal. Still, I’ll bet on the eventual success of organizations that turn raw data into meaningful analytics and insights.

IBM paid me to write this post, but the opinions in it are mine.

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