Riding the IoT Wave Without Drowning in Data

Your journey starts with a foundation in business intelligence, data analytics and data science.
By Dave Wagstaff

Data Science
Data science has a foundation in applied mathematics. This does not mean that data science needs to be complex; many of the simplest approaches are often the most effective. But it does mean that there is real math being done on the data, and that the outcome of this approach ties the data output to your business needs.

This gets tricky when you want to ensure that the mathematical equations are representative of the actual business requirements, such as improving overall system efficiency to increase production rates, for example. We start by asking business questions and doing some initial research on the most appropriate approaches and methods.

The end result is a data analytics output that can reveal keys to improved business performance and actually make the output actionable. The challenge is in making sense of collected data and unlocking its value, and that is where both predictive and prescriptive analytics come in.

Most people recognize predictive analytics. Through machine learning, it is possible to model how a piece of equipment operates and identify the factors that influence its behavior. Once you understand the leading indicators to a failure event, you can then monitor for those conditions and take proactive action (i.e., the prediction). This is a powerful tool when applied to mission-critical equipment where unexpected downtime has a serious impact to the business.

Less attention has been given to prescriptive analytics. Leveraging the same data models that are used to understand equipment behavior, you can establish a desired baseline of performance and then compare that to real-time operations in the field. Not only does this enable you to identify where equipment output does not conform to a baseline, but this technique can provide a prescriptive remediation plan for bringing that equipment into compliance. With the right systems in place, you can take real-time actions to the equipment through automation and help reduce or eliminate unplanned downtime.

When thinking through your IoT strategy, using data science and analytics to make sense of the massive amount of data being generated will help you achieve your business objectives. It will maximize uptime and performance of critical corporate assets, help reduce operational expenses and increase business efficiency. This is what we call "The Business of IoT."

Dave Wagstaff is the chief technology officer of Bsquare, an IoT solutions provider. In this role, Wagstaff drives a comprehensive and integrated strategy for all Bsquare products, including DataV, the company's flagship product. He has held a number of senior and strategic technology positions, including chief architect of advanced solutions for Lantroni; director of engineering at Lantronix; director of software development at Open Text; and software development manager at both Gauss Interprise and Diebold.

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