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Creating a Data-Driven Manufacturing Culture with RoviSys

Bryan DeBois, data-driven manufacturing, digital transformation in manufacturing

Bryan DeBois, data-driven manufacturing, digital transformation in manufacturing

Are you tired of hearing the term digital transformation? That’s probably because it’s been misused and widely misunderstood. Manufacturers are under the impression that they must make large investments to be successful. But what ends up happening is they just waste time, resources, and money.

A successful manufacturing digital transformation effort starts small and grows from there. In this podcast episode, we uncover what’s behind successful digital transformation initiatives, how tools and technologies can help, and the common pitfalls to avoid.

Our Guest: RoviSys

Our guest this episode is Bryan DeBois, Director of Industrial AI at RoviSys, a leading automation and information solutions provider. Bryan has been with RoviSys for more than 20 years in various roles, including programming, software development, and software group manager. Today, Bryan is focused on implementing advanced technologies like artificial intelligence into the manufacturing and industrial space.

Podcast Topics

Bryan answers our questions about:

  • (4:34) What digital transformation means for manufacturers
  • (7:16) The benefits manufacturers are looking to achieve
  • (11:29) The state of digital transformation in the industry
  • (12:19) Why manufacturing digital transformation projects fail
  • (14:46) How to successfully embark on a digital transformation journey
  • (18:08) Where artificial intelligence comes into play
  • (22:19) How to get all stakeholders aligned
  • (25:35) The role technology plays in digital transformation

Related Content

To learn more about the digitalization of industrial operations, read Demystifying Digital Transformation for Manufacturers. For the latest innovations from RoviSys, follow it at Twitter at @RoviSys and on LinkedIn at RoviSys.

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Transcript

Kenton Williston: Welcome to the IoT Chat, where we explore the trends that matter for consultants, systems integrators, and end users. I’m Kenton Williston, the Editor-in-Chief of insight.tech.

Every episode I talk to a leading expert about the latest developments in the Internet of Things. Today I’m talking about digital transformation in manufacturing with Bryan DeBois, the Director of Industrial AI at RoviSys.

Honestly, the term digital transformation has been so overused in the last few years that it’s hard to know what it even means. I want to get Bryan’s perspective on questions like: what’s the hype and what’s the reality of digital transformation? Why is digital transformation taking so long? And how can you get your team to buy into your efforts?

But first, let me introduce our guest. Bryan, welcome to the show.

Bryan DeBois: Yeah, thanks for having me.

Kenton Williston: Bryan, tell me, what is your role at RoviSys, and what does RoviSys do?

Bryan DeBois: Yeah. So, I am our Director of Industrial AI. In that role I’m focused on the application of advanced technologies to this manufacturing and industrial space that RoviSys focuses on. My background was software. I’ve been with RoviSys for about 20 years now. And we look at—when we say industrial AI, we look at that as a very broad brush; so yes, you’ve got the AI and machine learning. But then my division gets involved in things like computer vision.

We’ve got drone projects going on. Really anything that’s advanced technology. And, again, the manufacturing world tends to be five to seven years behind most other industries in terms of technology. So some of the stuff that may be somewhat old news in other industries is just coming into the manufacturing and industrial world right now.

Kenton Williston: Got it. And, out of curiosity, what did you do before your current role?

Bryan DeBois: I’ve spent my whole professional career at RoviSys, but before I was in this role, I was focused on our information solutions and MES projects. For some of your listeners, if you’re not familiar—because I may use this term again—MES is manufacturing execution systems. These are big, complex software systems. They’re comparable to ERP-type of rollouts. They sit below the ERP in a manufacturing stack, but above the plant floor. And I was involved in those projects before moving into this industrial AI role.

Kenton Williston: Got it. Now, you’re touching on a, I think, really pertinent point that I’m sure we’re going to get into in our conversation today, which is that a lot of the technologies that you’re dealing with are unfamiliar to various groups within your customers. What I mean by that is, on the manufacturing side there’s all this industrial-automation-specific technology and MES systems. What does that acronym even mean if you’re an IT person?

And then, conversely, the IT systems have all this stuff going on that are kind of foreign territory to a traditional manufacturing expert, for example, typically working with systems that are super isolated in an industrial-automation context, and you don’t have things like security patches or. . .

Bryan DeBois: Right.

Kenton Williston: . . . anything like that going on. It’s like, “Just leave it alone. Let the IT guys keep doing their CI/CD approach. We’re going to just have that sit here for 20 years and be super solid, and please don’t touch my system.” It’s like a totally different worldview on the two sides.

Bryan DeBois: You’re absolutely right because, now with this IT/OT convergence, now we’ve got a whole new audience of folks who have not heard of any of this. And, frankly, these are not technologies that the typical IT person has ever been exposed to—has ever had to manage.

The second point there that you made, about just something as simple as patching and life cycle management of an operating system in the plant floor—has to operate differently and has to be on a different cycle than what you can typically do in an IT space.

When people talk about this IT/OT convergence, it’s the traditional IT roles, technologies, the servers, the governance, the security—all of that coming into this OT world, which, as you can imagine, has been a good and a bad thing. But it definitely has been a little bit of culture shock there from both sides in that invasion of IT into the OT space.

Kenton Williston: Yeah, for sure. And I think this has been something that’s been an ongoing challenge just in general, as everything gets increasingly digitized—how folks can work together in this very much not shared context.

And so that brings me really to the topic of our conversation today, which is digital transformation. It’s interesting to me—this idea has certainly been prevalent in the IT space for a few years. And, in fact, I’d say it’s gotten to the point where it’s been overused, so it’s hard to even know what it really means. But it is still a relatively new concept in the OT, plant-floor kind of space.

So, what does digital transformation mean to you in a manufacturing context? And why is this something that folks should be paying attention to?

Bryan DeBois: In the manufacturing world we’ve gone through a number of these digitalization movements, we’ll say. So, I’ve been around long enough that Industry 4.0—but then even before that we had smart technology, and smart manufacturing was kind of one of the buzz words. And these are great concepts and they’re important concepts, but ultimately so much gets kind of hung on these terms that they start to lose all meaning. Because if they mean something different to everyone, then they don’t really have any meaning at all.

The way I look at it is that there is still so much room to grow in manufacturing. I’d say there’s so much value that digitalization can bring to manufacturing still. Because, like I said, we’re typically behind the times—we’re typically five to seven years behind the times in terms of adoption of technology. But then, also unique to manufacturing is this idea of legacy equipment.

You have equipment—we regularly see equipment that is 15, 20, 25 years old. One of the stories that I like to talk about is, we’ve got a customer right now that is operating in their powerhouse. They’re operating a generator that was actually installed by Thomas Edison. So that was a 100, 105 years, something like that, that this generator has been operating. It actually operates at a different frequency—it was before we standardized on 60 hertz, so it has to be stepped up to 60 hertz. But it’s still there, it’s still running, and we’ve instrumented it and we have control over it. So we’ve got modern tools around it, but there’s just not really a compelling case to replace that generator for that customer at this point. And that’s the case we see in a lot of customers.

So the combination of legacy equipment, the combination of the—our industry is highly risk averse. So they’re not going to adopt the bleeding-edge technology. All of those things combine to bring about a great opportunity for digitalization. I don’t want to downplay the importance of digital transformation. It is something that—that we’ve been working on, again, for a long time, at trying to—sometimes kicking and screaming—bring the OT space and OT customers into this modern era, into the 21st century.

Kenton Williston: And why is that? What benefits are you looking to achieve?

Bryan DeBois: I know for me, personally, I do believe that manufacturing and production is a world-changing process, that I think that manufacturing is the lifeblood of any economy. But more so than that, the fact that we have so much available to us that’s of higher quality—and it’s cheaper and it’s prevalent—is a direct result of our ability as humans to manufacture things and produce products at high quality.

And so the more that we can increase these manufacturers’ efficiency and the more that we can—yeah, it makes them profitable and that’s all great and everyone wants to make money, but I really see it as a deeper need as a society, because that’s what raises the bar and the quality of life for everyone, frankly.

And so I really believe strongly that digitalization is the path to make these manufacturers more productive, more efficient. And, frankly, also to make the lives of those folks who actually operate these plants better.

Kenton Williston: It’s interesting. So I think there’s a couple of things there that really caught my attention. So, one I want to come back to in a minute is that it’s not just about the dollars and cents, right? It’s about the environment people are working in and the company culture. I think there’s a lot to talk about there.

But the other thing that I’m hearing is that fundamentally digital transformation is about using data, but you’ve got to instrument things to get the data and then apply intelligence, i.e., AI—if I want to use too many abbreviations next to each other—you’ve got to apply that AI to do something with that data, and then of course the human beings are engaged in a way that allows them to—like you said—operate at a higher level of abstraction, do higher-level work that’s more meaningful and more valuable.

Bryan DeBois: Mm-hmm.

Let’s talk about the human factor first, because one of the questions I get a lot—I’m a company that consults and implements projects to bring technology to manufacturers. Well, one of the most common questions I get, even at parties and things like that when people find out what I do is, “Oh, so you’re putting people out of work.” Let’s address that head on. I’ve literally never seen, in my 20 years, one of our customers do a workforce reduction because of one of our projects. And, as we’re seeing right now, they don’t have enough people. They need more people than are available right now; in every single case those people are typically reallocated to higher value tasks like you’re talking about, and, frankly, tasks that they’d rather be doing. Nobody wants to compile Excel reports. If I can put in logging there that automatically records all those values—besides the fact that you’re going to get more accurate data out of that equipment—that’s just a better situation for that operator. They can stay focused on what they typically like to do, which is to operate the machinery.

But the other thing you talked about, that’s so critical to digital transformation—ultimately what we’re trying to create is a data-driven culture. And so a great example of that—we worked with a company that presses aluminum wheels, and they described the situation before we came in and did a digital transformation project. They were focused on continuous improvement—which was great, and that’s one of the things that I think separates some of the great manufacturers from not—is those that focus on continuous improvement. And so they had dollars allocated every year for continuous improvement.

However, where those dollars went, oftentimes, would simply be the loudest voice in the room. And so they said that after we were there and after we did this project, that they now consider themselves a data-driven culture. And those funds, those limited funds, would now go to whoever had the best data story. And so your ability to go into the system and find the data that supported your particular project—those were the people that won.

Kenton Williston: What would you say is the state of affairs? It sounds like some of the folks you talked to have been—you mentioned sometimes folks will come in with a big sales pitch about digital transformation and AI—you kind of promise the moon and the stars, and it’s just not very realistic.

Bryan DeBois: Yes.

Kenton Williston: I’m sure you encounter people who have been disappointed, and I’m sure you’re encountering people who are just trying to figure out what all this means. What would you say, overall, is the state of digital transformation in the industry?

Bryan DeBois: I definitely think that we’re past the hype cycle now in digital transformation, and we’re unfortunately starting to see some of these projects fail. The good news is that there’s still lots of projects that are successful in some form or another. But, yeah, I think that there’s definite trepidation around this.

And then, kind of right in line with that, as you’re defining those smaller projects you need to be tying them to use cases. As you mentioned, there’s been a lot of vendors—there’s been some really big consultant companies that have come in and promise the moon and said, “Give us 10, 20 million dollars and we’ll transform you digitally.” And without a real clear strategy on how that’s going to be done.

Kenton Williston: Yeah, absolutely. So, on that point of the misfires that folks have experienced—what do you think are some of the biggest pitfalls that have caused these projects to not succeed? Or at least not succeed to the extent that manufacturers were hoping.

Bryan DeBois: So, those pitfalls are also the things that, if you want a road map on how to do it right, these are the things to watch out for. So, the first one that I would say is, is that they didn’t start—and so we are very big on: walk before you run, phased approach—whatever you want to call it—small projects, small wins, and having that momentum.

Then roll you into the next project and the next project. That’s where we’ve seen the most value. That’s where we’ve seen the most progress. As you’re defining those smaller projects, you need to be tying them to use cases. One of the advantages, too, to leading with a use case is that it solves the problem of pilot purgatory, right? Or POC purgatory. We hear this all the time where, “Well, we did a couple of pilots on that, but then it didn’t really go anywhere.”

Typically, the reason why is because you didn’t have a clearly stated use case; you didn’t have this clearly stated goal as to a business case typically tied to financials. That was, “Here’s what we’re going to do or save because of this project.” So, what we do is, we put the problem first and so we prioritize that. We say: “What’s the most important problem that we can solve with a certain budget, with a certain constraint on timeline,” and things like that. And then we go and solve that problem with technology.

So, now that customer has a proven solution to a problem. Now the rollout part—the part that’s past pilot purgatory—that part’s easy because you have a working solution to a problem. Where else do you have that problem? “Well, we have it at five other plants.” Great. Let’s roll it out to five other plants. It’s a whole different way of thinking about these types of things than the traditional: “Well, let’s do a POC, and then why don’t you give us $2 million or $5 million in licensing, and we’ll roll it out everywhere.” That doesn’t work. So, leading with use cases and making sure that we’re starting with that is so critical as well.

Kenton Williston: Got it. So, you’ve partially addressed this question, but I want to dig a little bit deeper on how RoviSys approaches this digital transformation journey, and how you help companies navigate the path. And it sounds like a key part of that, like you said, is starting small. And I suspect just from what I’m hearing you say that there’s another part of it which is that you’re coming in without an agenda, so to speak, right?

Bryan DeBois: Yes.

Kenton Williston: So, maybe you could expand on that, and then if there’s more, you could tell me about just the overall process of how you would help a manufacturer embark and successfully navigate this digital transformation journey.

Bryan DeBois: Absolutely. So, two parts to that answer. The first part that you touched on is so important, and this isn’t unique to us, but most systems integrators—they don’t have to push any one particular product. We are independent. So, we make that very clear to all of our vendors. We are ready to simply look at the problems that you have, and then we’ve got a whole toolbox of products and platforms that we can implement to solve that problem.

The second part of your question is, how do we go about solving that problem? So, we do have kind of a—I wouldn’t even call it a playbook, but I would call it kind of a loose path that we typically follow that’s proven and seems to be the right way to approach these digital transformation projects.

So, just a quick glimpse into it at a high level. The first thing we’re going to look at in a digital transformation project is we’re going to look at their OT data infrastructure. I mentioned historian before. We think historian is such a critical foundational part of these projects. That’s typically the very first thing we ask is, “Do you have a historian? Is it comprehensive?”

“Yeah, we got a couple.”

“What kind of coverage do you have in your historian? Is it capturing 100% of your data? 50%, 20%?”

“Well, you know, maybe 20%, 25% of our data is being captured in the historian.”

So, one of the first things that we’ll do is we’re going to try to expand that coverage so that they’ve got more data to work with. The other thing that historians do is they become that OT data infrastructure.

They become—the thing that everyone’s asking for is: “We’ve got all these different vendors and the plant floor, and this plant was an acquisition.” Whatever. “I want one system I can query to get to that process data.” Well, that’s the historian. So, it becomes that common platform to query. As part of that, there’s typically networking upgrades that have to happen too.

And then the next thing we’re going to look at is, we’re going to look at OEE and visibility. So, OEE has been around for a long time. It’s availability, throughput, and quality. And while it’s been around for a long time, there’s a reason why it’s lasted as long as it has. It’s one of the easiest ways and most approachable ways to start on a digital transformation journey. So we oftentimes will reach for that next in our toolbox.

And then, along with OEE, comes just visibility. So many of these projects, once we’ve put in a historian for them, and we’ve got some displays that visualize their process—that’s transformative. Sometimes that in itself can lead to so many wins. But now that you’ve got that data maturity, you’ve got OEE as a KPI that’s talked about, and you’ve got this data-driven culture—now you can start looking at things like OT data warehouse. Now you can start looking at combining all that process data, other sources of data on the plant floor, MES—there’s relational and transactional sources of data on the plant floor. Now you can look at combining all of that into a single data warehouse and starting to query that. And that oftentimes is the carrot that’s dangling in front of IT—that’s where they want to get. They want that OT data warehouse that makes it easy for them to unlock all of the value of that data on the plant floor.

Kenton Williston: So, with that all in mind, one thing you haven’t mentioned yet that I’m very curious about is where AI enters the picture here. Because, of course, that’s your title—you’re the Director of Industrial AI. We haven’t really talked about where that factors in. So, could you tell me a little bit more about that?

Bryan DeBois: Yeah. So, I talked previously about the carrot that we dangle for IT, and that’s unlocking this data on the plant floor. So AI then becomes a further carrot. Sometimes I will say it’s a little frustrating being in the role that I am, because you walk in to customers, and they’re super excited to talk to you about AI, and they’ve got big vision about what they want to do around analytics. And, unfortunately, then you take a look at their data infrastructure, and you’re like, “Uh, you’re not anywhere near where you need to be in terms of OT data infrastructure to even be able to take advantage of some of this stuff.”

One of the things that AI needs to feed on is data, and it needs lots of data. And not only does it need a ton of data, it needs very, very clean data. That’s just not what we find.

And so I tend to have to be the bearer of bad news that there’s a lot of foundational work that we’re going to have to do before we can really take advantage of some of this AI.

The other piece that’s important to remember is that they oftentimes, again, have been sold a bill of goods from some of these big IT vendors that: “Well, we can build a model for you in a week.” And they can, but there’re some caveats there. First off, that model is going to be built with CSV exports. So it’s all going to be data that was extracted and manually cleaned by somebody—a data science scientist typically—on the vendor’s team. That’s what they’re going to build their model off of. And they’re going to build a model, and it’ll have predictive qualities, but they won’t have any idea of how to actually deploy that model and put it into operation on the plant floor.

And one of the key aspects of that is hooking it up to those real-time data sources—the same ones that they manually export data from and manually cleaned all that data. Those are the data sources that have to feed that model then in the future for it to actually be able to predict anything.

And one of the other aspects of AI that I like to emphasize is that all of that work that I just described, all of that—you’ve made no money at this point off that; that was all invested dollars. So, until an operator, a supervisor, someone on the plant floor is making decisions based on the predictions of that model, you haven’t seen a dime from your investment. Everything up to that point has been a giant science experiment. And so it turns out that operationalizing the AI is actually the hard part.

And that’s the part that we’re focused on. We’re trying marry, in our division, 30 years of plant floor experience and information-solution experience with the AI and the data science. And that, I think, is where—and we’re not the only ones doing it—but that I think is where we’re going to move the needle, is focusing on how we actually put those AI systems into operation on a plant floor that has to run 24/7 with operators, that oftentimes may not trust that AI versus their own eyes, ears, and smell that they’ve developed over many, many decades.

Kenton Williston: Yeah, absolutely all good points, and I think, again, just so much of this is about data. Going back to our top-level point here—that digital transformation equals data-driven culture—well, you have to have the right data, right place, at the right time, even the right format, right? You’ve got to get all those things right for this data to be in any way useful towards driving a difference in how you operate.

But I think there’s another piece to this, too, that I really wanted to come back to, which is the cultural part. And you touched on a little bit of that just now with the operators who’ve got their own ideas about how to run things, which are well founded, by the way—they know what they’re doing; that’s the reason to trust their eyes, ears, and nose, and all the rest. Because they’ve got the experience.

So you’ve got that perspective, and then you’ve got, of course, the engineers who were building all the industrial-automation equipment, who have a totally different perspective. And then, of course, the IT folks, like we talked about, have yet again another perspective, and last but not least, the people-who-hold-the-purse-strings perspective as well. Right?

Bryan DeBois: We’ve got to respect their perspective too, yeah.

Kenton Williston: So, how do you get all these stakeholders aligned on the digital transformation goals and process? Where do you start first, if you’re going to start small and walk before you run—what does that first thing look like? How do you achieve that alignment?

Bryan DeBois: If you’re leading with use cases, you’re going about it the right way. So, if you’re starting with: first, identifying what the problems are, and then prioritize those problems that are the right combination of achievable in a certain amount of time that are maybe least expensive to try to bite off but have the biggest impact. We want to identify those pretty quickly, pretty early on. And we’ve got certain workshops and things that we do to bring all those stakeholders together, to generate those ideas and then prioritize them. And so those are great opportunities right from the beginning to try to get everyone engaged.

So the second thing that we do is, we’ve got ideas about what projects are going to have the biggest impact. So now we take a couple of those off the top, and we go around and we do an assessment.

And so that process—we talk to all of those stakeholders, we talk to maintenance, we talk to management, we talk to operations, we talk to everyone involved—and we talk about those specific couple of use cases that we’re going to pursue. We talk about, what data do we need to make that project a success? What data are you going to—can your systems contribute to this? And what kind of organizational change management is going to be required to change the way that we operate in this future state? So we capture all of that into an assessment. And then, typically, we’re getting in front of those purse string holders that you talked about, and we’ve got all the documentation now.

We’ve got a pretty clear plan of how to get from A to B. Again, we’re focused on specific solutions—forget digital transformation and all of the buzzwords. Here’s a problem you have, and here’s a road map for a solution that would solve that problem, and here’s roughly what it would cost. And so when you lay it all out like that, it’s actually pretty easy to get everyone on board, to get everyone excited, and to get those purse string holders to say, “Okay, yeah, let’s do it. Let’s try this first one.”

Kenton Williston: One thing I’m curious about here. We haven’t really talked about the underlying technology, and I think there’s some important things happening there. So, for example, we’ve talked about AI, and how are there some cases where it’s been oversold, and I think this is still a very young technology. I think just from the perspective of how well AI techniques have been developed there’s been a lot of progress recently, and I think that progress is continuing. So, there’s more and more use cases where AI can actually deliver real benefits.

And there’s also, of course, the ongoing merge of technology from the hardware side. Personal note here, that this is an Intel-produced podcast, so of course I’m going to be biased towards all the amazing things Intel’s doing. Nonetheless, it’s true on both of the sides.

I’m thinking about things like, on the software side there’s this OpenVINO architecture that is great for many reasons. One of the things, like I mentioned, there’s ever more use cases that are available out of the box, so to speak. The algorithms that are predefined that you can use as a starting point to help accelerate things. Then, of course, from the hardware side, obviously the hardware doesn’t just continue to get faster, but there’s more and more specialized AI features built in. So, how do you see technology helping these digital transformations along, whether it’s AI or any other elements?

Bryan DeBois: It’s been really exciting to see the advancements that have happened even in the last five years in this space. And Intel, of course, is leading the charge on a lot of that. When we talk about digital transformation, one of the projects that we oftentimes will do—our MES or MES-light types of rollouts—and those oftentimes require new kiosks at each of the different work cells. So each work cell now needs its own kiosk so that it can give work instructions to the operator that maybe were on paper before, and so that it can record information about the operations that operator did at that work cell. All of that requires now industrial PCs. So you’ve got smart PCs that have to be rolled out—25, 50 different industrial PCs that need to be rolled out across all these different work cells where it didn’t exist before.

So there’s a ton of value that these industrial PCs bring to the equation. Obviously you’ve got the edge play, the IoT play, more and more smart devices everywhere. But then, specifically from the AI perspective, it’s been really exciting to see these chip vendors focus on specifically AI workloads. One of the interesting things—that the focus right now seems to be on vision. And so there’s a lot of value that computer vision can bring to a manufacturing facility. But what I’m excited about is what’s next. Whereas there’s definitely problems that computer vision can solve, we see just a whole lot of data that is coming from the plant floor that is not vision related, but still has a lot of value in AI applications.

So we’re excited for what’s next, where we start to see some dedicated hardware for processing AI workloads outside of vision, because that’s going to be really exciting.

And then of, course, you’ve got—when we talk about historian rollouts and MES rollouts and things like that—these typically require a good amount of hardware, and so, the Intel servers and things like that that we would typically roll these things out to.

And then, finally, you have the cloud. So there’s definitely a lot of customers that are saying, “Look, we’re about to make this big investment in a comprehensive enterprise historian, but, frankly, we don’t really want to allocate all of those server resources on site and have all of that IT footprint on site.” And so they’re looking to the cloud to roll out these really big OT deployments. And so, of course, I know that Intel makes a big impact there, too, across the different cloud providers.

So, yeah, I think that there’s impact all across with Intel and the other hardware providers. I think that they are really pushing the envelope on what’s possible.

Kenton Williston: Well, Bryan, I’ve really enjoyed talking to you, and we’re getting close to the end of our time together. So I just wanted to give you a chance if there’s any kind of like key takeaway you would like to leave with folks who are thinking about embarking on a digital transformation effort—what would that be?

Bryan DeBois: Yeah. So my pitch has always been: involve OT early. A lot of these projects nowadays are being driven almost exclusively by IT, and that makes a lot of sense for a number of reasons, but it’s so critical to get OT to the table early in these projects. And then I would take it a step further and say: and also involve an OTSI.

We have a great amount of knowledge about the different technologies and platforms and things that are out there; and can definitely help guide during the ideation process; can guide the conversation on what’s feasible, what’s not; where the lowest hanging fruit is; and all of that kind of thing. Start small, focus on use cases, and build that business case early on and get those wins. Build that momentum, and start to develop that culture for digital transformation.

Kenton Williston: Got it. Well, with that, let me just say, thank you so much, Bryan, for sharing your thoughts with us today.

Bryan DeBois: Absolutely. Thank you, Kenton.

Kenton Williston: And thanks to our listeners for joining us. To keep up with the latest from RoviSys, follow them on Twitter at @RoviSys and on LinkedIn at RoviSys. If you enjoyed listening, please support us by subscribing and rating us on your favorite podcast app. This has been the IoT Chat. We’ll be back next time with more ideas from industry leaders at the forefront of IoT design.

About the Author

Kenton Williston is the Editor-in-Chief of insight.tech and served as the editor of its predecessor publication, the Embedded Innovator magazine. Kenton received his B.S. in Electrical Engineering in 2000 and has been writing about embedded computing and IoT ever since.

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