Connectivity, Smart Software and Machine Learning

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?

Chad Jackson is an Industry Analyst at Lifecycle Insights and publisher of the engineering-matters blog. With more than 15 years of industry experience, Chad covers career, managerial and technology topics in engineering. For more details, visit his profile.

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