Using IoT to Maximize Efficiency

06/15/20 • 24 min

In this episode, we talk with Ed Kuzemchak from Software Design Solutions. Ed digs into the ways companies can use the Internet of Things (IoT) to increase efficiency. He shares advice on how to identify areas of opportunity to implement IoT and strategies to make the most of an IoT investment.

“I think the most important part for a company is to look at systems they have today and say “what part of these systems that we have, can we make more efficient or more cost effective or higher performing if we had better information?’ Cause that's really all that IOT is all about. It's about gaining data where you didn't used to have data or you couldn't get good or up-to-date data. You know, if you had to wait until the reports came back from the field, from your field sales tech or your field service techs on machine failures, you might have a two week lag on machine failures. And the data that you're looking at is always two weeks old. Well, what if it was only five seconds old?”

Connect with Ed Kuzemchak on LinkedIn.

Connect with Ed Kuzemchak on LinkedIn.

Previous Episode

How Salesforce’s Einstein Analytics Tool Helps Manufacturers See the Big Picture

In this episode, we sat down with Kyley Darby from Mountain Point and Skye Reymond with Terbium Labs. Kyley and Skye explore how manufacturers can leverage descriptive, predictive, and prescriptive data to optimize business outcomes. They also dig into the ways Salesforce’s Einstein Analytics can help companies better plan for the future.

“‘To move forward and look beyond the “what has happened,” manufacturers need to start pulling data together in a centralized manner — to switch from seeing what has happened to “what could happen, what could we change?” I think having data all over the place is something that holds them back.” - Kyley Darby

“I’ll add to that, Kyley. In the past, a lot of these methods have been really technical and if you don’t have access to the technical talent that’s necessary, you can find yourself following a predictive model that’s incorrect. This can cause the business to lose a lot of money, time, and effort. That technical talent that can utilize predictive and prescriptive analytics has historically been hard to find. But, fortunately, with things like Einstein, Salesforce is making this skill more accessible to everybody. So I think in the future, you’re going to see more of that, where you don’t need an entire data science team, but a good understanding of Einstein, if you’re a Salesforce user, and what those results are going to mean for your business” - Skye Reymond

Connect with Kyley and Skye.

Next Episode

Demystifying Serverless Machine Learning

In this episode, we sat down with Carl Osipov with CounterFactual.AI and the author of Serverless Machine Learning In Action. Carl shared some real-world use cases for serverless machine learning and identified strategies to get the most from a machine learning investment.

“One of the things that happens at the beginning of a machine learning project — and this is a well-known problem for data scientists and machine learning practitioners — is spending way too much time cleaning up their data sets and focusing on things like data quality instead of actually building out machine learning solutions. I think, as practitioners, machine learning developers and engineers have created a set of techniques over the past few years to help formalize and accelerate this process. But it’s still a concern, especially if you think about scenarios that are common to manufacturing where different data silos have to come together for a machine learning system. This also happens in the scenarios where manufacturers acquire companies and then integrate data and use that data for machine learning systems. What happens is that if companies don’t actually have a rigorous approach for transitioning their machine learning systems code into operations, they find themselves in a situation where data scientists and machine learning engineers actually end up doing a lot of operations involved in putting machine learning systems into production. So what I’m describing here is what I call an ML ops trap. This machine learning operations trap, where these highly compensated practitioners are essentially spending their time working on something that’s not their core competency.”

Connect with Carl on LinkedIn.