Representative End User Clients
Representative Automation Clients
Representative Software Clients
In a recent podcast, ARC’s Jim Frazer spoke with Andrew Soignier of Uptake Technologies about how companies can 
translate underutilized data into insights that make industrial operations even smarter.
It may sound simple but organizations fail at it every day. In fact, industrial facilities use as little as 1% of their own machine data productively.
Jim & Andrew explore Uptake’s ability to deliver real-time insights in an intuitive UI from your PP&E, in context of deep industrial intelligence, so you can make better business decisions, that translate to a healthier bottom line.
And, since Uptake has been purpose-built for heavy industry, their products are simpler to use, easier to scale, and faster to return value than other standard AI/ML solutions.
To listen to the entire conversation please click this link: Andrew Soignier of Uptake Technologies.
Some insights from the conversation…
Jim Frazer - Greetings, let's get started with a simple foundational question. Can you tell us a little bit about yourselves and how you got started on your path to the world of industrial analytics?
Andrew Soignier - Yeah, you bet. Thanks. been doing this a little bit over 25 years, about 10 years in automation and OT technology as an engineer and rotating equipment and process safety. I then actually got recruited into enterprise software back in 207. And have never looked back. And every time I've kind of taken on new challenges. The gravity has always brought me back to energy and manufacturing and chemical oil and gas around the latest technologies. And over the last seven plus years, that's evolved into digital and all of the advanced technologies associated with it.
Jim Frazer – So, let me ask for those of our audience members who might not be fully versed in industrial analytics, what is industrial analytics?
Andrew Soignier – So, I think the simplest answer that that I've seen lately is being able to solve problems and answer questions across the enterprise. And being able to do that in a way that is ultimately facilitated with the technologies, some of them that we use in the consumer world.
Jim Frazer - So let's then look at what challenges might there be, and it might, it might even be in the subcomponents, so I'm thinking data, probably need a data repository, maybe different sensors. But what are the challenges of deploying an industrial analytics system that gives you the ability to solve those questions that?
Andrew Soignier - Yeah, I think the advantage that that uptake has is that we've been solving this problem across multiple verticals. And that's allowed us to come up and understand the pattern is really, it's not industrial analytics by itself. The cornerstone across every market is data and the uniqueness of data across all the verticals, and, and suburbs. And so at the end of the day, oftentimes, as many of the customers have shared, is even over long, you know, they've been at this journey in this process, data is still not only the biggest challenge, but now I think with the last couple of days, you really see and post COVID, the embrace of data as a cornerstone versus just a challenge. So for each one of our verticals, we've had the advantage now of uncovering the patterns, and finding the commonalities across really the asset intensive sectors.
Andrew Soignier - Yeah, I think that's really accurate. And I think at the end of the day, when it comes down to a big motto that we preach is connecting data people and machines. And you know that what we're trying to solve for data people machines, is irrelevant. If we're displaying a windmill, tractor trailer turbine, a pump, the right side of the screen has to still display what is what is the activity of that specific asset. What are the insights that we can give to the company that is going to allow them to make a different decision. You know, with uptake versus not having uptake. And I firmly believe that every machine is better if you have uptake. Because I think that the way we display the information and the way we can bring these organizations together across the enterprise, I think we do it better than anybody.
Jim Frazer - So before you deploy your solution, you need that data set. So what are the challenges in acquiring that data? How do you go about that? You know, what are best practices that that domain.
Andrew Soignier - So I think to put it in layman's terms, each customer is their own cybersecurity challenge. And so, you know, as we're kind of all familiar and in everything kind of happening out and about in the world, if you just look at every customer. And while they follow a lot of, you know, common approaches and patterns, they all have different sensitivity around their data. And so this kind of data conundrum or data problem, it really stems back to while there's a lot of layers of technology, due to the way things have been built the last 25 years. It's ultimately a security protection and isolation problem around how they CAD managed, how they grew up, and work through the original analog to digital paradigm. And now they're working through the woods now traditional digital paradigm to kind of full on digital that we see today.