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

06/01/20 • 26 min

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.

Connect with Kyley and Skye.

Previous Episode

Beyond Academics: The Practical Applications of Machine Learning

In this episode, we talk with Bastiane Huang with OSARO. Bastiane digs into the practical uses of deep learning and machine learning. She explores beyond the academic applications of machine learning and details some real-world scenarios, including the ability to expand the use of robots in less structured environments.

“We use machine learning to allow robots to react to changes in the environment, learn to handle a wide range of different items, and have a range of different tasks. And more importantly, to learn, “Oh! This task [required] minimum human supervision.” So this way, you can really save a lot on human costs and on a lot of the surrounding systems,. These kinds of surrounding systems are usually more than four to five times the robot costs, so it's really significant. And lastly, it also enables robots to be used in new use cases. For example, you don't really see robot arms being used in warehouses right now. Because in a typical warehouse that has millions of different products it’s not feasible to program a robot. You're able to deal with a million different products in a million different ways. So now, because of machine learning, robots can be used in this kind of less structured environment. ”

Connect with Bastiane on LinkedIn and Medium.

Next Episode

Using IoT to Maximize Efficiency

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.