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Connectivity, Smart Software and Machine Learning

IoT efforts can be complex, but breaking them down into piecemeal projects makes these efforts far simpler.
Chad Jackson Chad Jackson

Today, the landscape of the Internet of Things (IoT) is awash with new and exciting technology that can empower miraculous product capabilities. Collecting and analyzing the right data can yield insights that may hold the power to transform a company. Building the right intelligence and automation can transform an industry. The visions are grandiose. Yet, most companies today struggle with a short and simple question: How do we get there?

Most people know the basic technical steps. You have to collect the data. You have to analyze the data. You have to act on the data. All of that is easy enough to understand—at a high level. Drilling down into the details, however, is not so easy. In fact, most companies don’t follow a straight line from here to there. There is a lot of meandering, quite a few mistakes and much learning.

Collecting the data is often one of the simplest yet most technical aspects of an IoT initiative. You can instrument a product with tons of sensors. You can stream those readings somewhere on the cloud. Likewise, taking action also can be simple. Once you have some correlation between sensor readings and some event, catastrophic or marvelous, most know what needs to come next: avoid it or repeat it. But the data analysis bit? Well, that can be terribly difficult. This part is where we are trying to find the needle in the haystack. How do you manage that?

In my time working with manufacturers, I’ve seen successful companies take one of two approaches to analyzing data. Both approaches share a common trait: controlled scope and focus. As you may or may not know, getting overwhelmed with Big Data can be a project killer. So, let’s look at each in turn to see how these strategies keep things under control.

Boiling the Ocean… in R&D

The first successful approach I’ve seen employed is ambitious in some ways, yet focused in others. Here, companies instrument a product or prototype extensively. However, they don’t do this in a production environment—at least not initially. They put this instrumented product through the paces as they would a prototype in testing. They expose it to a variety of cases and collect everything.

Once they have some critical mass of data, they don’t try to analyze the data themselves. Frankly, there’s just way too much of it. They turn machine learning software loose on it. They might feed it their initial hypotheses about what they think are the potential correlations. But by and large, they are looking for the software to bring them the key findings.

Companies in this mode are looking to learn. They are in the midst of discovery, and they know it. They kick off this kind of effort, but keep it under wraps in an R&D department. They aren’t looking to disrupt current development projects. Findings from this kind of project will be applied in future projects that likely have not even been started yet. However, many findings from this kind of effort could have wide-ranging impacts across the company. There is great potential here.

Proving or Disproving a Hypothesis

A different approach comes in the form of an organization that is looking to achieve something very specific. They have a particular event with some kind of key business impact associated with it that they want to be able to predict. Furthermore, in this case there is a technical team who has a few ideas on what sensor measurements could be correlated to that event. In general, these organizations aren’t looking to learn in a broad sense. They are looking to prove or disprove a hypothesis.

In this case, there is very limited and specific instrumentation. They are only looking to capture the data that will let them verify their idea or not. Because of that, there is a limited amount of data and the data analysis is simpler.

Findings from these kinds of efforts can be applied relatively quickly, depending on the complexity of the development project. And, they can provide value to the company quickly. Yet, their value is highly dependent on that single hypothesis.


There is no reason that a company couldn’t pursue both of these kinds of projects. The first might even feed into the second. In fact, a company might run multiple projects of each type. But in both cases, the scope is defined and controlled.

IoT efforts can be terribly complex, but breaking them down into piecemeal projects makes these efforts far simpler.

Chad Jackson is president of Lifecycle Insights ( Send email about this commentary to

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Chad Jackson

Chad Jackson is president of Lifecycle Insights ( Send email about this commentary to .(JavaScript must be enabled to view this email address)

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