Dave Wagstaff - CTO at Bsquare

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(Joel Beasley at 00:00:31) Visit us and ask us all the questions that you have at leaderbits.io. Now get excited because today we are talking to Dave Wagstaff, the CTO at Bsquare, and we discuss innovation in the IoT and machine-to-machine space, the importance of effective team communication, and solving a business problem before getting enamored with the technology. Podcast. Here we go. This is the Modern CTO podcast.

(Joel Beasley at 00:01:13) Excellent. Where are you calling in from today?

(Dave Wagstaff at 00:01:16) From Bellevue, Washington, just on the other side of the lake from Seattle.

(Joel Beasley at 00:01:19) Oh, nice. So it's bright and early for you right now.

(Dave Wagstaff at 00:01:22) Yeah. I tend to be an early guy anyway, so it's nice. But what's really nice is the weather is actually cooperating today. It's not raining.

(Joel Beasley at 00:01:31) Nice. Yeah.

(Dave Wagstaff at 00:01:33) Yeah. So I actually live in Southern California. I commute up here, so I get the benefit of the nice weather down there, at least two days of the week.

(Joel Beasley at 00:01:42) So you split your week?

(Dave Wagstaff at 00:01:43) Well, it's not really a split. I'm up here for the five days. I'm here Monday through Friday, and then I'm back home basically Friday through Sunday.

(Joel Beasley at 00:01:52) How sustainable is that? How long have you done that for?

(Dave Wagstaff at 00:01:55) You know, I've done it for five years. So the better question is what level of insanity do I have, right? It's a little bit of an easy flight. Right? It's about a two-hour flight. It's a nonstop, so it's not a big deal. Then I've got an apartment here that I just walk to work. So if I look at it objectively, I commute twice a week and I walk the other part. Now that could be rationalization, by the way. I could be trying to rationalize it. I don't know.

(Joel Beasley at 00:02:24) Well, that's awesome. And so you're an early morning person?

(Dave Wagstaff at 00:02:27) I am. I am. For the longest time I've had a deal with international teams, and so it's almost like it has to be that way.

(Joel Beasley at 00:02:35) So what time are you getting up?

(Dave Wagstaff at 00:02:37) So I usually get up around 3:30 to 4:00. Oh, you beat me. Yeah. Are you an early guy too?

(Joel Beasley at 00:02:43) I do a 5:30 run. So I'm up at 5:15, I'm on the side of the street by like 5:30. But everyone's like, "You're crazy, you're crazy," but here you go, you got 3:30.

(Dave Wagstaff at 00:02:54) Yeah. Well, you know what, at least you're doing exercise. I'm up on the phone not really doing anything good. So I'll give you credit. You win that one.

(Joel Beasley at 00:03:05) So right now you're still up early because you're working with other time zones? Tell me about that.

(Dave Wagstaff at 00:03:14) Yeah, so we have an office just outside of London, actually on the west side of the island, a town called Trowbridge. And then we have a Taipei office. And so, you know, it's nearly impossible to get a meeting where everybody's involved. And so what we tend to do is we schedule them for one team and a repeat. The UK guys—you know, that's why I'm up early. And then on the Taipei side, I have to stay a little bit late.

(Joel Beasley at 00:03:40) Got it. So I am curious to know, so we can set some context. You're the CTO of Bsquare, correct?

(Dave Wagstaff at 00:03:48) Yep.

(Joel Beasley at 00:03:48) And what is Bsquare?

(Dave Wagstaff at 00:03:52) So Bsquare—it's a public company. Been around for 24 years, actually. Actually started out life, we were one of the co-developers of Windows CE. So the Windows version that used to run on those small portable machines. We actually built a lot of that infrastructure and mainly the compiler. So in the early days with Bsquare, there were really two lines of business. One was we would resell Microsoft licenses to use that OS on hardware. Then we'd go back to the customer and ask, "Well, what are you doing with that license? Do you need some help in building some custom solutions?" So we built a service organization around that. So we sold licenses along with services for custom applications, work-for-hire companies.

(Joel Beasley at 00:04:36) That's pretty great. Yeah. I remember the CE.

(Dave Wagstaff at 00:04:39) Yeah. Yeah. It's one of those things. I know I had an old HP little iPAQ thing that had the thing on there, and I thought it was the greatest thing in the world.

(Joel Beasley at 00:04:50) And then so what have you morphed into?

(Dave Wagstaff at 00:04:53) So what we did is we said, you know what? What we built was a lot of knowledge around connected devices—how to connect devices up, how to work on embedded systems, and how do you integrate that with the cloud. And so about five years ago, and that's when I joined the company, the board of directors said, "Well, you know what? Let's take that knowledge and productize as opposed to making work for hire." So the idea was to pivot into the IoT space, use that knowledge, build a set of components and platform pieces that we could reuse to build solutions. And we came up with basically three different components. There's the collect piece, which is where we attach to devices and collect the traditional kind of M2M space where we're connecting devices using whatever protocols are available. There's an analyze piece that says, "Okay, once I've got that data, what kind of insight can I draw from that?" That's machine learning and our rule engine. And the last component is, okay, now that I know, for example, the truck is going to break down in the next 24 hours, what do I do to operationalize that within the organization? Right? So we took those pieces and said, "Let's build a platform to support those models." And then on top of that, we built some, I'll call it more vertical-specific solutions, like predictive analytics for transportation is one of our verticals. Another one is understanding oil and gas and the kind of problems they run into that maybe a connected device solution could help.

(Joel Beasley at 00:06:19) So you've got to help me though. Whenever people say something I don't understand, I ask. What is the M2M space?

(Dave Wagstaff at 00:06:28) So machine to machine. I think, you know, prior to IoT, M2M—I came from a background where we actually did, we called ourselves M2M. But the focus was different. So on M2M, the focus was getting connectivity between some kind of a device into a back-end system. And that was the focus, and it kind of left it from there. I think IoT is acknowledgment that, well, that's a great thing to do, there's a whole lot more you've got to do. So IoT is maybe an extension of it. So I always look at it as we started with M2M, got smarter, moved into IoT. And I think even IoT will eventually get renamed because really, the function is not Internet of Things. It's solving some kind of business problem with connected devices.

(Joel Beasley at 00:07:13) And then, like, how do you measure success? Like, are there success stories of IoT?

(Dave Wagstaff at 00:07:19) Yeah. You know, and that's a great question. If you look around, there isn't as many as you may think, right? There's a lot of folks talking a lot about IoT and what the capabilities are. There's a lot of talk about the art of the possible. But folks really—there hasn't been a lot of good use cases that say we've actually done something there. I think you're going to be seeing a lot of those come up now because I think the industry has realized you've got to take that connected data, run it through some analytics, and some of them are machine learning, which is relatively new and kind of a niche market. And then the key part of this is once you know that, how do you operationalize it? How do you get that into the enterprise to drive something? So I'll go back to the truck example, because this is one that we're actually working on. Imagine if you can predict that this truck going down the road is going to fail in the next two hours, and you know that I'm carrying a load of perishables that I need to deliver. I've got to make a decision here. Should I make that delivery and then fix the truck, or is the truck going to be a big problem and I need to get into service right now? And the only way to do that is you take that knowledge, you integrate with your business systems to say which service days are available, what parts are available, do I have the right skill set available, and then generate a service ticket automatically for that truck, and then notify the driver, "Hey, your route has changed, go here first." So that's, I think, what the promise of IoT can do for you. And I think we're in early days there because it requires all these different skill sets—data science, protocols, you know, subject matter experts, which is just another thing that it's not technology. It's a lot of it is subject matter expertise to get that integrated in. And how do you take that knowledge in someone's head who understands how a truck operates and codify that in a repeatable model?

(Joel Beasley at 00:09:05) That's amazing. All right. So I'm over here hooking up like my Philips Hue to my alarm clock.

(Dave Wagstaff at 00:09:10) Yeah. Yeah.

(Joel Beasley at 00:09:13) Like I've been a master craftsperson. And, by the way, I was very excited because I did that this week. I bought the Philips Hue starter kit. It came with two lights, and I plugged them in. And I coordinated with this app I have called Sleep Cycle that has an integration for it. And now, 30 minutes before my alarm will actually hit, it goes from like 0% to 100% brightness, and it's like I got like Arctic Sunrise going on.

(Dave Wagstaff at 00:09:41) Oh, wow. Yeah. Yeah. It's great when you can do that stuff. You know, your story is kind of similar to mine. I have a wife who doesn't quite understand technology. You know, I'll come up with this great thing, like I brought it home. It's like, "Look what it can do." And she's just like, "Yet another thing that's not going to work correctly." What changed all of that is I got one of those smart Honeywell thermostats. And when she—again, she looked at it and she goes, "Why in the world did you do that?" Now, her life changed though when I showed her on her phone that it could detect 10 minutes before you get to the house and it'll preheat the house or precool it before you got there. And now she got it, right? She got, "Okay, this connected device thing actually benefited me in a very significant way." Right? So that's again, it's just a restatement of start with the business problem first and then use technology to solve it.

(Joel Beasley at 00:10:30) Yeah. I'm laying in bed and I can't turn the lights off. This is a problem. Philips Hue. And I can. And if she forgets to turn the thermostat off—she doesn't listen to the show, so I can just say it. I'm going to get her—she doesn't care. I'm going to get her one of the Nest things because she's pregnant with our—we have a child, a small girl.

(Dave Wagstaff at 00:10:49) Congratulations.

(Joel Beasley at 00:10:50) And she's pregnant with our boy who will be here in April.

(Dave Wagstaff at 00:10:54) Yeah.

(Joel Beasley at 00:10:54) So we're very excited to have some, like, back-to-back kids. But, you know, I would say once or twice a week she forgets to turn the thermostat down and she's laying in bed and she's pregnant. She's like, "Can you do it?" And I'm like, "Nope. But Nest can."

(Dave Wagstaff at 00:11:09) But, you know, the next level is take it to the Alexa, right, where you're using your voice now to control. And now even if you're laying in bed, you just yell out, "Turn off the lights," and all of a sudden things happen.

(Joel Beasley at 00:11:20) Awesome.

(Dave Wagstaff at 00:11:20) So it's yeah. It's amazing stuff. But the problem with it today, and I think this is the same problem we have in IoT, is it takes a very technically savvy person to stitch that all together, right? It's a bunch of piece parts. The integration's a little bit difficult. There's not a good way to capture these business rules that you want. You know, like a simple one would be, if I leave the house and I leave for longer than one hour and my heater's turned on, probably turn it off, right? How do you codify that and make that usable in a way that mere mortals can do?

(Joel Beasley at 00:11:59) I guess, yeah. Also, connectivity within the integrations. So for example, the Sleep Cycle app I mentioned, it will only, for some reason, like turn on one scene of my Philips Hue. So I actually ended up disconnecting it from the integration, you know, after I paid for the premium. I disconnected it from the integration and then just coordinating my Philips Hue to come on with what time I know I'll wake up. Like, because there was more options in the actual Hue app.

(Dave Wagstaff at 00:12:25) Yeah.

(Joel Beasley at 00:12:26) And so I was like, I would like them to be synced together, but there wasn't like all the features that Philips Hue could do wasn't supported inside of the integration. So now I've got to wait for them.

(Dave Wagstaff at 00:12:34) Yep. No, I—and that's exactly—you know, I happen to be, I am kind of geeky in those things. But I took my Hue system and then there's a cloud offering called If This Then That. Yeah. And so I did—and one of the integrations I did sounds silly, but it's really useful. Because I commute every week up here, what I did is I have it where I order Uber and, you know, it drives me home to the airport. The idea is when the Uber is within a minute of getting to my house, my lights up so I can be prepared for it, right? So, again, the promise of, if you get these systems integrated, there's a tremendous amount of things you can do.

(Joel Beasley at 00:13:13) Yeah. So when you guys—this truck story you mentioned—by the way, my background is like engineering programming, right? So when you were mentioning all that, I was like, come on. You're going to be working with all of these teams, the team of this software to get the data and the API calls ready, or you get teams of that.

(Dave Wagstaff at 00:13:27) Yeah.

(Joel Beasley at 00:13:28) And so I love it. So you will meet with a business that has that business use case, and then through your services, you'll facilitate the orchestration of all of that engineering so that the scenario that you so easily spit out about—when you were spitting out that scenario, I was like, are they factoring in the cost of the spoiled load? Like, is there a threshold? If what they're carrying is worth more than X dollars than reroute? Do they actually—

(Dave Wagstaff at 00:13:48) Oh, yeah.

(Joel Beasley at 00:13:48) Yeah?

(Dave Wagstaff at 00:13:54) Yeah. You're very smart and you catch that. And this is why these rules become pretty complicated because you make a decision and it's a fact-based decision. Maybe it's cost that's driving you, right? Or maybe it's some other piece, but you've got to factor that into—so again, just because you've detected something, you've got to assign a score to it of how valuable it is to do it.

(Joel Beasley at 00:14:16) And you can get infinitely complex because you could say, the place where I'm going to deliver, look up their customer history profile.

(Dave Wagstaff at 00:14:22) Mm-hmm. Mm-hmm.

(Joel Beasley at 00:14:23) Do a sentiment analysis of their comments and feedback, and if they're x angry and their lifetime value is x, then that actually is going to determine how expensive the load needs to be before it gets rerouted. Then you go, oh yeah, you go crazy nerd with it.

(Dave Wagstaff at 00:14:35) It could be crazy. Yeah, you should be on our sales team because you're doing a fantastic job of doing that. Well, you know, the other thing that occurs though is there's agility that has to go along. The first rules you write around this stuff will be very, very simple.

(Dave Wagstaff at 00:14:47) Yeah, if my oil pressure drops below a certain amount of time, I better go do something, right? As you become more sophisticated, you'll put additional conditions on that. Maybe it's related to, maybe my oil pressure is low because my RPMs are low, so I want to get rid of that false positive.

(Dave Wagstaff at 00:15:02) So you have to support a system that supports that agility where you learn a little bit more and you can quickly adapt the system to give you more information and you can change to it. So these waterfall systems where you build it and then deploy it six months later, and then you get some feedback, and twelve months later you do some, just don't fly anymore. You've got to have these quick turnarounds.

(Joel Beasley at 00:15:22) No, we have a product here that came out of the show, and we built an API. And our first version, like, there was just one call to the API. And I was like, I just want it shipped into production so we have it.

(Joel Beasley at 00:15:33) Yeah, you know, once you have the documentation, right, there's a documentation, you can do an example call, you can get a temporary key. Like, once you get that whole foundation laid, then we can only focus on just like what data, you know, what data do we need to extract or make accessible.

(Joel Beasley at 00:15:48) And now it's just an easier conversation versus piling up the, you know, I see some of the remnants of the waterfall stuff even happen inside of more modern projects, and it's really about the people and like, keeping it simple is hard.

(Dave Wagstaff at 00:16:01) It is really hard and you hit it on the head. I mean, you know, what we try to think here is start small but think big. So the idea is build on something small and achievable because you've got to have a success to drive you forward. And then you have this whole concept of fail fast. If you're going down the wrong path, realize it early on, right, to do that stuff.

(Dave Wagstaff at 00:16:21) Because, I mean, it's a weird world now. It used to be when I first started, and I've been in this space for, I'm coming on thirty years, you know, the waterfall, you were used to that, where you had long design cycles, and that was okay because the technology wasn't really changing that often and all that kind of stuff. Now there isn't a week that goes by that somebody hasn't thought of something new that you should be thinking about, how does that integrate in. So microservice-based architectures that we're looking at and the ability to have graphical business users can use, that you don't have to be a developer necessarily to control them, become now really, really critical.

(Dave Wagstaff at 00:16:59) And how do you do that, right? Otherwise, this technology is a bunch of piece parts. You know, it's like telling me, give me all the parts to a car, I'm not going to be able to put a car together.

(Dave Wagstaff at 00:17:08) If I did, I wouldn't want to drive it. So I don't know how to do that, but there are skilled craftsmen that can. And, you know, now that you throw in data science, which now I think is part and parcel with a lot of these solutions, that's a really different skill set. And how do you integrate those folks in?

(Dave Wagstaff at 00:17:23) They talk a different language than, you know, developers do. And how do you get those two teams to work together?

(Joel Beasley at 00:17:28) Yeah. I was having this conversation, I think, like, about two weeks ago. We were saying you have to be a nicer person now because before you could, like, own the entire development lifecycle because there's so few parts. Like, you just did a web app and it just had a database. But now with the amount of specialties there are, it's like you cannot do it yourself.

(Joel Beasley at 00:17:49) To be competitive, you have to have a bunch of great people who can work together.

(Dave Wagstaff at 00:17:53) Yeah. And that's, you know, it's in many regards, it's a lot of craftsmen, a lot of artists working together, and they have different opinions. And, you know, there was an old HP thing that I think also Kmart also uses, it's called management by walking around, which is really what they're talking about is communicating, right?

(Dave Wagstaff at 00:18:10) You need to be able to communicate. You have to have some kind of an adjudication cycle because you can't have everybody have their own opinions because you never get anything done. At some point, there has to be, you know, we might have a motto here where it's show and convince. So the idea is, it's up to the team to demonstrate that they've got a good idea and that it actually is workable, and then the team gets convinced of it, right? It's kind of standard practice, but I think it's an art that, I agree with you, you didn't have to do before.

(Dave Wagstaff at 00:18:39) Because R&D teams used to take a market requirement document, spend six months, spit it out, and say, here it is, and then move on to the next thing. That no longer works.

(Joel Beasley at 00:18:49) I love it. Show and convince.

(Dave Wagstaff at 00:18:51) Yeah.

(Joel Beasley at 00:18:51) That sounds really good. Another one I heard last week from CTO of Dailymotion, like a video site.

(Dave Wagstaff at 00:18:59) Okay.

(Joel Beasley at 00:19:00) Yeah. They have, you know, a couple hundred engineers spread across the globe, but he said it was centralized vision, decentralized execution, or centralized strategy, decentralized execution. And when I heard those words together, I was like, that is poetic.

(Dave Wagstaff at 00:19:16) Yeah. It is. It is. It is. You know, and it's very, very true.

(Dave Wagstaff at 00:19:19) I mean, I think we're finding, um, you know, teams are now decentralized simply because of skill set issues, right? I mean, data science people tend to live in certain parts of the country and embedded programmers are somewhere else. And so you've got these disparate teams, and how do you share a common vision and a common goal of how to do that? So it's back to communication.

(Dave Wagstaff at 00:19:41) So, you know, management by working around doesn't work as well there. But, again, it's not the walking part. It's the communication piece that I think you need to get focused on.

(Joel Beasley at 00:19:50) Management by slacking off.

(Dave Wagstaff at 00:19:52) There you go. You know what? Maybe that is a new version of that particular thing because, you know, I think there's a tremendous amount of tools out to do that. But, I mean, it all boils down to first principles lots of times is having a common goal that you're all striving to, and what can you contribute to that, right?

(Dave Wagstaff at 00:20:12) How can you move that forward?

(Joel Beasley at 00:20:13) Oh, I actually have a recurring calendar event in my phone that gets triggered every month that says, think first principles. Because the moment I saw that, I was introduced to it through one of Elon Musk's talks, and he was referencing something in physics, right?

(Joel Beasley at 00:20:27) First principle. Yeah. Yeah. And, you know, reasoning up from logic. And so I said, I never want to forget this.

(Joel Beasley at 00:20:34) So I put a recurring event for, like, my lifetime and like forever into my phone. It just reminds me to go back and read the first principles concept.

(Dave Wagstaff at 00:20:44) Well, you know, and that is a great idea. You know, I had learned a while back that, you know, there's celebrations, like, if you look at in the Bible and so on and other areas, there's celebrations that occur. And it isn't because of the celebration. It's because it's the remembrance of what that celebration or whatever meant. So the point is that sometimes we have to remind ourselves because we get in the trenches, we forget where the flag is that we're going after, and then you're just working hard.

(Dave Wagstaff at 00:21:09) You're not working productive. You're working hard.

(Joel Beasley at 00:21:12) Yeah. It's, you've got to toggle that high level, low level, that beautiful, like, rhythm of super detail, big picture, super detail. It's hard, but it makes life interesting though.

(Dave Wagstaff at 00:21:24) It is. It is.

(Joel Beasley at 00:21:25) It makes life interesting though.

(Dave Wagstaff at 00:21:27) It does. It does. And then I think the other thing that has changed in this world, especially on the technology side, is you can't assume you went to school, I learned something, I will now use that knowledge forever and ever.

(Dave Wagstaff at 00:21:39) You're constantly relearning now. Now it's a matter of, okay, what did you know last week? Because that's maybe relevant, maybe not for this week, depending on how the space changes. So it's now a bunch of course corrections that you're having to do.

(Joel Beasley at 00:21:51) Yeah. And it's also interesting because it's like, if you look at, I never understood, well, for a long time, I didn't understand the concept of, like, knowledge loss, like, with employee turnover and things like that. But after I became a business owner,

(Dave Wagstaff at 00:22:07) I was like, oh.

(Joel Beasley at 00:22:08) Oh, that's my, oh.

(Dave Wagstaff at 00:22:09) I, yeah, absolutely. Well, have you heard, there's a term that I heard about a year ago, and I love the term. I don't like the fact, but it's called gray two K. And the idea there is that, so one of the verticals we're in is oil and gas.

(Dave Wagstaff at 00:22:24) And there are guys that have worked in the oil and gas maintenance stuff for thirty years, and they can put their hand on a pump and tell you exactly what's going on with that thing. That's knowledge in their head. As they transition, as they retire out, there are not, you know, the younger folks tend to, you know, I'll try maintenance for two weeks, see how it works. They don't get that knowledge. So to your point, that knowledge is leaving the enterprise and not being captured anywhere.

(Dave Wagstaff at 00:22:52) It's gone, and it's really unfortunate.

(Joel Beasley at 00:22:54) Yeah. There was even, somebody shared a story with me, so I'll keep it ambiguous. But there was a water facility that provided water for a few million people. And the engineers said it was a closed system. I'm sure you probably understand more about the industrial systems languages.

(Joel Beasley at 00:23:09) But, essentially, the story was they came to their company and said, hey, we don't know about how any, there's no documentation on this code. We have no idea how the plant is running or any of the systems, the control system. We don't know anything about it. And everyone has since retired and either passed away or just retired.

(Joel Beasley at 00:23:27) So basically, they don't know how this facility is operating. And we provide cleaning water for, like, 2 million people in the city. So will you come and, you know, look at our systems and document them and figure out how to upgrade them so that if something happens, we know what to do. And I was like, that's like the most incredible call to get from like a city, right?

(Dave Wagstaff at 00:23:48) Oh, can you, yeah, you can reverse engineer our water system for us? Yeah. Yeah. That kind of reminds me of the old, uh, the Y2K stuff, right? Where you had to pull people out of retirement because no one knew how to do Fortran and how these systems were actually put together.

(Joel Beasley at 00:24:02) Yeah. And that's why I feel good because I'll be able to be homeless, like, with a little paper that says, will write SQL, in, like, 2060. And, like, nobody understands because they're all sitting on top of these overly engineered, like, ORMs or whatever it may be. These wonderfully engineered ORMs. They don't know how to write low level code, and then we end up, you know, writing SQL for them.

(Dave Wagstaff at 00:24:24) Well, you know, and I think that, you know, that's the, that's interesting because you're right. I think I look at folks now, you know, I was brought up in SQL, and that's, you know, relational databases, and that's the way you look at the world, right? Very rectangular, and now you look at NoSQL, and you're going, okay. Now everyone's moved to that.

(Dave Wagstaff at 00:24:39) But at the end of the day, there are still cases where a true relational system works fine. So, again, it's using the right tool for the right job, and understanding that just because it's old doesn't mean it's necessarily bad, right? There's still some good stuff out there.

(Joel Beasley at 00:24:53) It's amazing some of the stuff that's still on these old mainframes that they're converting out of. There's large companies that are still converting these older companies out of these massive mainframes. And I'm like, to me, it was like mind boggling that that's still happening.

(Joel Beasley at 00:25:10) But at the same time, I think about from a business perspective, if everything was functioning, and they were just realizing the fruits of their investment, right, because the it was operating and their business was happening, then, you know, they're thinking there just wasn't a need to do it, and now there is, right?

(Dave Wagstaff at 00:25:28) Absolutely. Yeah. It's maybe economy. I look, you know, I look at the, you know, the airline reservation system from Sabre, right?

(Dave Wagstaff at 00:25:33) So that system was built, which, it's like an amazing thing because it was built at a time when, you know, CPUs and computers and stuff weren't, you know, necessarily reachable, but it ran quite well. And it scaled at amazing levels, right? So I think achieving a system like that today would even still be a challenge, but they were able to do it on far less than what we have today. So it's amazing what you can do with effort.

(Joel Beasley at 00:25:57) I think you're really qualified to help me with this. Edge and IoT, can you talk a little bit, like, what's the difference? How are they separated? Where?

(Dave Wagstaff at 00:26:07) Yeah. Absolutely. Absolutely. So the idea would be that the edge is where the ingress of the data comes in from, and you can think of it as sensors that are connected up to a pump or something like that. So it's literally the compute that runs very, very close to the device you're trying to capture the data from.

(Dave Wagstaff at 00:26:26) Typically, resource constrained either through memory or CPU, but it can run some logic on there. And the idea is it has some kind of connectivity to either a gateway or the cloud, because that's where you do the heavy lift stuff like analytics, machine learning, some of the rule-based things. I think one of the unique things that we do at BSquare that we think about is sometimes, so the traditional model is you take the edge device, you take its data, and you just blindly throw it across the wire onto the other side. That tends to be fairly expensive. There's latency involved.

(Dave Wagstaff at 00:27:02) There's a number of things that prevent you from necessarily getting all the data you want. Think of, so in the truck space, we are actually monitoring trucks, are like a mini refinery, by the way. They're just amazing how much stuff they have in it. But we're monitoring 384 sensors on that truck. And some of those sensors are generating data at twenty millisecond intervals.

(Dave Wagstaff at 00:27:20) There is just no way you can take all that data and transmit it up. So one of the rules, one of the monitors we have here is sometimes it's cheaper to shift the logic rather than the data. So the idea would be you would use the cloud to do the machine learning, understand the model, how the device behaves. You compress that model and you run it now, what we call inference at the edge. So you learn the model in the cloud, but you run the inference at the edge.

(Dave Wagstaff at 00:27:47) So now the device can have 100% access to all the data from the sensors and only send back relevant information back to the cloud, so you minimize your cost.

(Joel Beasley at 00:27:55) So you're training the model in the cloud, right?

(Dave Wagstaff at 00:27:58) That's correct. That's correct. Yeah. And that takes a heavy lift operation, requires a tremendous amount of power.

(Dave Wagstaff at 00:28:04) But the actual inference, we're actually using that model to make decisions, that requires far less information. Well, I'm sorry. It requires far more information, but less compute power.

(Joel Beasley at 00:28:14) Okay. So you're taking the model.

(Dave Wagstaff at 00:28:17) And then...

(Joel Beasley at 00:28:18) You can wirelessly update it too. And so you're calling the... when you take the model and transmit it down to the truck, the device, you're calling that the inference of the model.

(Dave Wagstaff at 00:28:29) That's correct. That's correct. The inference is using the data, consulting the model to see if it has an opinion, if there's a problem or not. So we're taking these 384 sensors, we're throwing it at the inference, and it's coming back and saying, "No, everything's good. Don't worry." So then we don't send anything to the cloud. Or it may come back and say, "Oh, looks like your fuel pump is about ready to go." It transmits that to the cloud to do something.

(Joel Beasley at 00:28:54) That's amazing. And then... and then, how... so then, where do you... I'm sorry, I'm gonna get nerdy on you here.

(Dave Wagstaff at 00:29:02) Oh, no.

(Joel Beasley at 00:29:02) So where do you get... how do you get that original large set of data to do the training on the cloud-based model?

(Dave Wagstaff at 00:29:10) Yeah. You got all the right questions, by the way. But what we actually do is... so there's a concept of different modes of communication. So while the truck is, let's say, moving down the road, it's connected via the cell, which has limited bandwidth and very expensive. But when that same truck pulls into the service bay at the end of the day, it has Wi-Fi connectivity, which is cheap. So we use store and forward. So the idea is we maintain that data on the truck, and then when we pull into a cheap level of connectivity, we dump that data back into the cloud.

(Joel Beasley at 00:29:40) That's amazing.

(Dave Wagstaff at 00:29:42) Yeah. It's fun stuff. And again, the rules could be written in a way on the truck to say, you know, if it's a "don't care" event, wait until I pull into the service bay at the end of the day. If it's a really important thing, like the truck is ready to fail or there's some impending safety issue going on, I don't care if you're on cell. Still transmit it because it's important.

(Joel Beasley at 00:30:04) Now do you build interfaces or a process and system in place to sort of, like, post-store and forward, manipulate the data in case if there was, like, a poor sensor? Like, a false sensor, you would have to go correct that data or somehow tag it to say that we had a human look at it and it was a false.

(Dave Wagstaff at 00:30:23) Yeah. Absolutely. You know, if you talk to most data scientists, they'll tell you that 70% of their time is in the data cleansing and transformation space. So we realize that. Again, we want to be able to rebuild a product. So we built what we call our data pipeline, and our pipeline is all about managing that. So the idea is, how do we take data, strip out the stuff that's bad, maybe replace it with something that's kinda good because we don't want to lose that data? We have this concept called data enrichment where we take data in, and maybe we have to consult a third-party system to get information. For example, maybe the truck gives us its GPS coordinates, but it's more important for me to understand what state it's in. And that's called data enrichment, pulling that piece in there. So the data pipeline, again, we have a very graphical tool. We can put in false positives and thresholds and certainty factors and that kind of stuff. And then in the machine learning model, it can come back and say, "You know what? 90% of the data from the fuel pump is always bad. You ought to go figure out what's going on there." Right? So we can identify... we can accommodate the problem, but we can also identify that it is a problem so we can go back and fix it to understand, is it a vendor problem? Is it a communications problem? Is it just a bad sensor?

(Joel Beasley at 00:31:33) Dude, this is a really cool company.

(Dave Wagstaff at 00:31:36) Yeah. I think so. I think so.

(Joel Beasley at 00:31:38) When we move into organics, we'll be having this sort of device, like, for our human selves.

(Dave Wagstaff at 00:31:45) I know. You know, it's what's funny about this stuff. Like, we talk to a lot of subject matter experts both in the transportation space, and they give us a lot of good information. So that bootstraps the system. So they give us a bunch of rules, and we say, "Great. Let's start with those." And then we augment that with machine learning. And what's interesting about that is we find out things that they hadn't thought about. You know, there was one where we were looking at a battery problem. And they were just replacing the batteries because they would always drop below... they had a rule that if you drop below a certain level, it just had to be replaced, the battery. Well, what it actually turned out, it wasn't a battery problem at all. The machine learning figured out it was a problem with the alternator, that it was actually not spinning fast enough. The gearing on it wasn't correct. So when these trucks would go down... that wouldn't be up... so this problem only occurred on trucks that, it turned out, run in the city, because the engine RPMs are fairly low, and the alternator would not spin up enough to charge the battery. So, and that was done through machine learning. So you would never figure that out. You would've just continued to replace batteries, not finding the root cause. The root cause was not the battery itself, it was the alternator was not spinning fast enough.

(Joel Beasley at 00:32:56) So who is your customer? Is trucking companies your, like...

(Dave Wagstaff at 00:33:00) Yeah. So it's interesting. I can't talk... there's lots of times we're the middle man. In other words, we deliver a service that someone else delivers. So...

(Joel Beasley at 00:33:08) Oh, okay.

(Dave Wagstaff at 00:33:08) In this particular case, we actually deliver the service to the manufacturer, the OEM of the truck.

(Joel Beasley at 00:33:14) Okay.

(Dave Wagstaff at 00:33:15) But the ultimate customer is the fleets, the guys that actually run the trucks. They use this service to understand when to schedule maintenance and that kind of stuff.

(Joel Beasley at 00:33:25) Oh, that's cool. So does it give the manu... so, like, me, if I was the manufacturer of the truck, I could say when I'm selling my fleets to the customers, I could have the value add of all of this technology?

(Dave Wagstaff at 00:33:38) Yeah. Yeah. And you hit it. That's another interesting, I think, trend that has been going on is you have traditional vendors, like in the white space, in the white goods space, or something like that, where they used to sell things, you know, like a truck or a refrigerator or a smoothie machine. Now they're finding out that they've got to add services on top of that because they want that recurring revenue. So that's exactly the model, right? The idea is, I'm gonna sell you this thing, I'm gonna sell you some services that you can best operate this guy in the most efficient way possible, and you get that as a bundle.

(Joel Beasley at 00:34:07) And then I don't have to do that expensive consulting concept of retrofitting my entire fleet with some third-party system I have to buy. Like, I buy the fleet, it just comes with it.

(Dave Wagstaff at 00:34:19) Yep. And it just... you turn it on. And, you know, and it's a decision of whether I want to pay that monthly charge or not. That's the decision you have to make. And our goal when we sell our product is when we talk to a CFO of an organization, I'm gonna charge you a dollar, but you're gonna save $10.

(Joel Beasley at 00:34:34) Yeah.

(Dave Wagstaff at 00:34:34) Then it becomes an easy decision, right, to do that.

(Joel Beasley at 00:34:36) Saving that money.

(Dave Wagstaff at 00:34:37) Yeah. Well, you know, and you got a good point. There are two things to think about. Either you're gonna try to save money, which is what a lot of folks think about initially. The other one, which I think is more compelling, is what new revenue streams can I generate that I didn't have before? Right? As a refrigerator maker, maybe I can tell you that whenever Southern California Edison decides to have a "save energy" day, if I can make that refrigerator automatically reduce its power consumption and save you 20%, maybe that's something I can sell as a service to you to save that.

(Joel Beasley at 00:35:10) That's interesting. I'm learning so much.

(Dave Wagstaff at 00:35:13) Well, good.

(Joel Beasley at 00:35:14) I'm gonna keep leveraging your intelligence. Business intelligence versus data analytics.

(Dave Wagstaff at 00:35:22) Yeah. Yeah. So I think there's... that's a good thing there. Business intelligence is all around tools and people. Right? So the idea is you take data, you bring it into the system, you run tools like Excel or Tableau, and you look at that data and you present it in graphs. Right? And then a subject matter expert would look at the data and make a decision based on that. So a lot of heavy lift on the human side. You have some pretty competent people, and it presents kind of a challenge in a repeatable model. Right? So data analytics says, "Well, let me use the data to help me make decisions instead." And not to replace the human, but to augment what they have. And so the idea is data analytics is great. So you hear a lot about machine learning, all the wonderful things it can do, and it can. But by itself is not as important or not as useful as if you combine it with that business intelligence and then a subject matter expert along with rules. So, you know, again, I think even though they sound very similar in what they do, the focus is a little bit different. Right? So in one case, you're looking at charts, and in the other one, you're saying, "I don't even know why we're making this decision, but the data tells me that that fuel pump is gonna go bad in the next two hours. I don't know why, but I need to be able to do that piece of it." And we at Bsquare don't think that either one is 100% of what a business solution should be made up of. You use both of them. So in machine learning, there's this concept of an ensemble model, which basically says, "Don't use one model. Use a bunch of them and do a consensus vote." We think the same thing is true when you do the analysis of the data. You need both the analytics and the data analysis along with that subject matter business intelligence thing to move through it.

(Joel Beasley at 00:37:10) That's... thank you. Clear explanation. I like it. You're very smart.

(Dave Wagstaff at 00:37:16) Oh, no. Thank you.

(Joel Beasley at 00:37:17) Thank you, David. How many people are you currently leading in your engineering side over there?

(Dave Wagstaff at 00:37:24) So we have about 25 folks in our straight engineering team. We have a data science team, which is about five folks that, you know, they're strictly looking at analytics and data and how do we productize those kind of things. And then we have about six or seven folks on our QA team.

(Joel Beasley at 00:37:44) So all these products that we're discussing right there, you've got under 50 people that, like, are...

(Dave Wagstaff at 00:37:49) Yeah. Yeah. And the beauty of how we can do that is we're really, really smart about what things are we gonna choose to build and what can we use that's already out there. We work a lot with AWS, and they have some fabulous infrastructure, some amazing things that we leverage and we add our secret sauce on top of it. And then we allow our customers to even add stuff on top of it. Right? So with their subject matter experts. So we're really focused about what we do well, and we invest in that. And we have no problem with this "not invented here" mentality. There's a lot of tools and a lot of neat things out there, and we leverage a lot of open source to help us build out the stuff. But I think the art of what we bring and the value we bring is, how do we integrate that altogether in a cohesive system that business users can use?

(Joel Beasley at 00:38:37) Yeah. How do you bring value to the market? Because without that...

(Dave Wagstaff at 00:38:41) Yeah. Without a doubt. And I think the problem with IoT and... you know, the IoT space is people get enamored with the technology. Right? It's the old Gartner hype curve, right, where you get to the peak of disillusion where it's, "Okay. Great. I can connect this stuff up, but what was the reason I'm doing this for?" Right? So we always start with the business problem. Right? And we're really honest with our customers. If the business problem they're trying to solve is not something that we can help with with a connected solution, we back away. Right? But the point is the goal isn't to connect things. The goal is to drive some business value. Something... you know, maybe it's a new revenue stream, maybe it's to, you know, get a better remaining usable life out of my systems, whatever it may be, but that's the goal, and you need to drive to that. I think folks kinda forget about that a little bit. It's easy to do.

(Joel Beasley at 00:39:32) Yeah. A few people were talking with me about the data lake craze that happened.

(Dave Wagstaff at 00:39:36) Yeah. The data swamp is usually what it is.

(Joel Beasley at 00:39:39) The data swamp or people drowning in the data swamp. "Oh, we can help sales if we can connect these two points." 75 points later, sales still hasn't gotten their data.

(Dave Wagstaff at 00:39:49) Well, no. That's exactly right. Well, you know, the reason why we have data lakes and data oceans now is because people know that the data is important, so they don't want to lose it. But they don't know what to do with it yet. So you get these... you know, it's great for the guys who are cloud service or storage providers because we're just gathering tremendous amounts of data now. But we're not really doing a whole lot with it.

(Joel Beasley at 00:40:08) It's like owning the self-storage facility. You love it when you're in a town of hoarders. Right?

(Dave Wagstaff at 00:40:14) Yeah. Exactly. Well, no. That's exactly it. Because, you know, it's important, and it is. Data is such an important asset to most enterprises. So you don't want to throw it away, but you don't know yet what to do, and you don't know how much of it to store, and so you just keep it all. Right? Which, you know, in the scheme of things, it's probably a good idea. But the point is I think we need to focus on, okay, what am I trying to drive as far as a business? Now that's an interesting question, though. And I love this saying. So Henry Ford used to have a saying that if he were to ask his customers what they wanted, they would have asked for a faster horse. They wouldn't have known anything different. Right? They wouldn't know what the art of the possible is. Part of my role here is when I talk to customers is, "Hey, that dream you have, maybe you need to reshape it a little bit because here's how you can maybe achieve that, and you may not have thought about that before." Right? So a lot of it is, again, external communication of what the art of the possible is.

(Joel Beasley at 00:41:08) That's interesting because I was actually writing, like, a blog... like, some blogs... I write these, like, blog post starts. I don't know if they ever, like, come out and are gonna be finished, but I always start writing. I flew to Boston, like, last week, and I was writing on the way there. I was like, "I don't believe that's true." Like, I think if you would have asked intelligent customers what they wanted, what they would have said is they wanted a horse where they could ride it in a... they wouldn't get wet on their journey.

(Dave Wagstaff at 00:41:39) Yeah. Yeah. Yeah.

(Joel Beasley at 00:41:40) Or they wanted one that they didn't have to feed all the time. Right?

(Dave Wagstaff at 00:41:44) Yeah. Right.

(Joel Beasley at 00:41:45) One that lasted longer. Right?

(Dave Wagstaff at 00:41:48) And all of... or ran faster.

(Joel Beasley at 00:41:49) Yeah. Or ran faster. But it would be this collection of things that they wanted. And if you look at what happened, they got those items.

(Joel Beasley at 00:41:59) It's just the way that they got them was the car. And I guess the point I was making in that article was that we never have this concept of innovation being massively different. It's completely false. It's never true. You can only take these small baby steps and improve. Because when the car first came out, it was not very awesome.

(Joel Beasley at 00:42:23) No. They just made that small improvement.

(Dave Wagstaff at 00:42:26) Well, and again, that's think small and then, you know, start small and then think bigger. Right? Because you'll evolve and do quicker. I like your analogy though, because you're right. I think what they would have asked is a bunch of things thinking that you could implement that in a whole horse somehow. But somebody else said, you know what? Wait a minute. You're not even talking about a horse. The horse will never get you there. Here's what you need to think about. Right? That's a great way to look at it.

(Joel Beasley at 00:42:52) It's like, let's extract the value it brings to the customer and make list of what they desire as improvements, and then figure out what we can make out of that list of improvements to hit a few of them and how that would look. Because if you started sketching and spitballing ideas from a list of all those improvements, pre a car existing, a car is going to come out of that.

(Dave Wagstaff at 00:43:23) Absolutely. Yeah. Absolutely. Well, and that's why I love what you're doing here because I think making things known to folks is something we've gotta do a lot more of because there are things out there that can really help people, but they don't even know they exist. Right? So they continue on with the way they've always done things. So it's difficult. And so being able to communicate that, I think, is super important.

(Joel Beasley at 00:43:45) Within your team, how is your culture on a scale of one to ten?

(Dave Wagstaff at 00:43:49) It's good. And I think one of the things that we do is we empower teams, and this whole show and convince principle, it comes with a responsibility. Right? You can't just declare something without proving it out. And so we have a concept called the day in the life of, where we actually take some of our folks and we not force. Force is probably the wrong word.

(Joel Beasley at 00:44:12) Encourage.

(Dave Wagstaff at 00:44:13) Encourage. We encourage them to actually take the business function of the end customer, work in their life for a day to see what kind of drama they have. And by the way, your tool, if you used it, would it help or would it not help to move that piece of it? So the sense of empowerment is really important. The other thing is we kinda follow a little bit of the Google model, not a hundred percent. But, you know, a portion of time that they can take is to learn new things, because you have to be able to do that and move through it. And the good old, you know what? We're humans at the end of the day. That attaboy, when you acknowledge somebody for doing something very, very significant, goes for space. I mean, you have to correct people too, but, boy, always remember to keep that, you know, you did a good job and that was really hard to do. Thank you, and you delivered something. Lots of times that comes from our customers, which is probably the best, to be honest. When a customer comes back and says, gosh, this would have taken me eight months, and maybe I would have done it, but I was able to do it in a month with your stuff. That is a huge value, and that just really absolutely does that. And you have to temper that because the work we're doing is hard. It's leading edge. Things don't work as documented. Right? You look at an API set, it's supposed to do this. It doesn't because it's early days. So there's a lot of hard work here. So, you know, we have to be able to acknowledge this whole fail fast, understand that you failed, but you need to be the admin to detect that and move on and move forward with it.

(Joel Beasley at 00:45:41) You touched slightly on something that I wanna amplify a little bit. This concept, especially, is true for new managers. I get a lot of feedback from the audience and stuff. But you can be tough and good. Like, you can care deeply about your people, you know, understand them as humans, let it get personal. We're working within five feet of each other. There's no such thing as separation. We're humans. It happens. But the fact that you can't get close to your people because you may have to change their job or they may go or whatever undesirable thing you could imagine may happen. I'll notice people will stay back and say, oh, I'm not gonna get close to them because of that. And that's just because it's an initial instinct as an unexperienced leader that you can't be close to someone and also be tough and good with them.

(Dave Wagstaff at 00:46:35) Yeah. Absolutely. Well, there's mentoring and there's acknowledgment that you're not the smartest guy in the room. Right? You may have some idea. So being able to and then again, it falls back to communication. Being able to receive and as a team, you know, nobody has the right answer, but as a team, I think we do. Right? And if you can get a team to understand that and work together in that, and to your point, we're all human. So there gonna be bad days when we're gonna be the bad guy in the team. And as long as it's not a consistent theme. Right? That's the hard part.

(Joel Beasley at 00:47:06) Yeah. And I look at my team like we're a bunch of computers. It's like, I would rather have five of us processing potential outcomes and solutions than just one computer processing them.

(Dave Wagstaff at 00:47:19) Yeah. No. That's exactly right. And we bring different strengths and different experiences to the table, and let's leverage that. It's tragic when people are that way. You know, one of the, I worked at a company, OpenText. It had a fantastic program. It was the first company I worked for that—

(Joel Beasley at 00:47:35) You were OpenText?

(Dave Wagstaff at 00:47:36) Yeah.

(Joel Beasley at 00:47:38) Oh, wow. You were OpenText? That's pretty cool.

(Dave Wagstaff at 00:47:39) Yeah. I did. I did. That was my first enterprise experience. I worked on the accounts table, so I have some funny stories around that. One of the things they taught me, and it really is that a lot of companies have a single track, career track. You start maybe technical, and then if you wanna get in the higher echelons, you've gotta move into the managerial side. And that's tragic in many regards because you have certain folks that are very technical that aren't gifted or don't have the right skill sets to be a manager. Right? And so what they've kept is always two tracks. There's a technical track, manager track. They are equal as far as pay and seniority and all that kind of good stuff. But it was acknowledgment that there are certain folks that have skill sets. Don't be tragic and kill it off and then try to make them something they're not. And I think that's really true today. I mean, I look around my team. There are certain folks that I would put in front of customers, and there's others I would not. Right? It's just a different skill set.

(Joel Beasley at 00:48:37) I like that. I like that they had the separate tracks.

(Dave Wagstaff at 00:48:40) It is. Yeah.

(Joel Beasley at 00:48:41) And some people could and once you have separate tracks, then you can actually make the decision if you wanted to move. Because I was heavy engineer, and I ended up wanting to move into the creative side of things. Because while I was good at engineering and I enjoyed it a lot, I also am an individual that I climb a mountain and then I look for the next one. It's like I got to the top of engineering where we're all arguing over the top three or four things. It's like, okay, I'm at the top here. What now? And then I was like, let's go conquer the human side of things.

(Dave Wagstaff at 00:49:11) Yeah. Absolutely. Well, then you gotta have enough safe harbor. Right? So the idea is, let's say, you wanna make that leap from technology into managerial, and because you think you wanna do it, and then you get in, oh, this really not my cup of tea. You need to have the ability to go back again. Right? And I think OpenText supported that model really, really well.

(Joel Beasley at 00:49:26) Yeah. Also, myself supported that model. Because what I said was I can go over here, and if I don't enjoy it, I've built my skill set up enough to where I'm valuable enough in the marketplace where I can go back to that. And I was very comfortable with it because I was like, I could still go back and I could still enjoy my life and still do stuff. But, yeah, I pushed through. I got it.

(Dave Wagstaff at 00:49:49) Well, no. You know what? You sound a lot like, you know, in my, I kinda went on CTO side of it is a little bit more managerial stuff. But, boy, I tell you, I still code. I still get my hands dirty and stuff, and partly because I like it, to be quite honest. But second of all is to understand what the team has to go through and what they're dealing with and keep my hands close to the metal of what's changing. So so, again, it's that drive inside of you that you gotta have.

(Joel Beasley at 00:50:16) Yeah. Being able to send HTTP requests and have a light turn on will never not make me smile.

(Dave Wagstaff at 00:50:24) You know, there's something about taking an inanimate thing like a program and having to do something physical. Isn't that magical? I mean, I'm glad you said it. Again, my wife doesn't believe that, but I absolutely do that there's something magical. And that's why I like this embedded programming piece of the world because usually you have some physical thing that touches this logical thing.

(Joel Beasley at 00:50:46) I feel very much like when I hook something up or write some code now. Right? I feel very much like a caveman making fire. I'm like, you start to see it work. Like, is it working? It's working. And it's on fire. Like, yes. It's working. I did it.

(Dave Wagstaff at 00:50:59) Exactly. And then you move on to the next thing. Well, now I can build a fire. Let me go see if I can build a fire extinguisher. So we move to the next piece of it. So it's a never ending story. And the good news is technology is allowing us to do things that would have been science fiction just even five years ago.

(Joel Beasley at 00:51:16) How did your role change from chief architect, VP, right, to CTO? Were there big differences, small differences?

(Dave Wagstaff at 00:51:24) A little bit. I think the CTO role, which is what attracted me. CTO, it's really that customer facing is focused. CIO is more internally focused. CTO is more externally focused. The chief architect role allowed me to design and come up with things and work with smart teams to build things. And then you kinda threw it over the fence, and you hope that you got a good response. I think in the role of CTO, I can actually go out and talk with folks to try out ideas. In fact, the first year I was here, I burned a lot of jet fuel flying out to existing customers. We had the benefit, we had existing customers, and asked them, when you think about this connected device space, what do you think about? What do you have any ideas and that kind of stuff to move it back again? So I think the role is much more customer facing. It's bringing that into the product team to help them understand what customers are asking for, and then understanding, technically, could we even build something like that. Right? If someone asked me, you know, what I want to do is really solve world hunger, I have to kinda back off that one because—

(Joel Beasley at 00:52:29) Well, check their budget first. Because I'm good on that one.

(Dave Wagstaff at 00:52:35) Yeah. You know what? That's a good point. So I think that's really the goal. And so it's almost the best of both worlds, to be honest. I really enjoy the role because I can craft an idea based on some educated knowledge, and then shove it out there and see if they like it or not, right, and quickly iterate over it. So I think that's the biggest change for me is I think I still work with the same set of folks. I think, again, it's that customer focus that makes what to me makes a lot nicer.

(Joel Beasley at 00:53:04) Yeah. Definitely, that's one of the reoccurring areas of top three areas where the C-level technologists are spending their time. Customer with your top customers. And that's just really important because even if the CEO is interfacing with your top customers, having the technology side interface with their side, it just, you know, we're people. You gotta spend time with people.

(Dave Wagstaff at 00:53:27) You got to and I can't tell you how much I've learned. I knew nothing about trucks when we started working with this. And now I think I, you know, I couldn't fix one, but I could at least I know how they work. And, you know, oil pumps, I had no idea what kind of complexity was involved, but I learned a lot. So it's just neat. And when you talk to people that are really smart in that space, it's amazing what kind of knowledge you get.

(Joel Beasley at 00:53:49) Yeah. Maybe you'll get to work with one of the Tesla fleets. That'd be cool.

(Dave Wagstaff at 00:53:52) I know. You know? Or, you know, maybe there's some maintenance that has to go with SpaceX. Right? And I gotta work on a rocket somewhere.

(Joel Beasley at 00:53:58) I think it's gonna have more than 352 sensors, though.

(Dave Wagstaff at 00:54:01) I think so. I think so. And you know what? And to be honest, I don't know if I can fix it while it's in flight. So I think we've got a different problem there. Right.

(Joel Beasley at 00:54:09) So as we wrap up, what are you most excited about? What project, what's going on at the company on a day to day that you're most excited about?

(Dave Wagstaff at 00:54:17) Yeah. So I think we, you know, some of this stuff we have to keep under, I have to be a little bit not evasive, but a little bit—

(Joel Beasley at 00:54:27) Ambiguous.

(Dave Wagstaff at 00:54:27) There you go. Is that I think we're working on a project that is around cities and municipalities to make life for people a lot better based on connected data. Because I think cities are amazing how much data they're collecting. There are some cities, and Bellevue is one of those, that are really collecting a lot of data. Like, they use the sensor data from the truck or from vehicles to understand what kind of light patterns to do for the signal lights. So we're working in that space to actually deliver value, not in the sense of revenue, but it's a better quality of life for folks. So, you know, the example I gave around energy management that, you know, I live in Southern California, and Edison will, every once in a while, will send me a, hey, if you cut down your energy use by ten percent, we'll give you a twenty percent break. So we're working with that to drive some information into the meters of the house to be able to control devices. So, like, maybe I'll dim your lights by five percent.

(Dave Wagstaff at 00:55:28) You won't notice it, but that enough will give you some savings. So, you know, using those kind of things. And by the way, if that 5% did bug you, you would tell the system it would learn and it would say, okay, I won't do that again.

(Joel Beasley at 00:55:39) Yeah.

(Dave Wagstaff at 00:55:41) So that to me is exciting because now, while I love controlling physical devices and I think those things are cool, I think if you can do a little bit better for humanity that we're actually driving some making someone's life a little bit better. And maybe that little bit better means I'm saving a little bit on my energy bill. You know, energy is an area that I think we need to be concerned about. Water, you brought up water. That's another one that I think is really interesting.

(Dave Wagstaff at 00:56:03) Can we do some stuff there to really help out? And I think now that we've got so much connected data that we're collecting, I really truly believe that we can do something there in a major way.

(Joel Beasley at 00:56:13) I love it. So you're making the smart cities a little bit smarter?

(Dave Wagstaff at 00:56:16) Smarter. Yeah. Well, you know what? The goal, you know what's funny about that? We talk about smart cities, smart cars.

(Dave Wagstaff at 00:56:22) The goal isn't to necessarily make the city smarter. It's to take the smarts to make my life better. Right? It's to make my life and, hopefully, at the end of the day, the citizens will benefit in some way or another due to that smartness. Right? If you just make the city smart, that's great, but you want to do something with it.

(Joel Beasley at 00:56:39) Yeah. I just like that buzzword, the smart stuff, because it was a word that caused cities to put money into technological infrastructure, which then can allow us to do things like this.

(Dave Wagstaff at 00:56:51) Absolutely. Absolutely. Totally agree. Totally agree.

(Joel Beasley at 00:56:55) Dave, we did it.

(Dave Wagstaff at 00:56:57) Awesome. Yeah. I am. You are just a very enjoyable person. I just, you're great.

(Joel Beasley at 00:57:03) Alright. Back at you. This is awesome. If I'm out in your area traveling, speaking, I've got like a big conference circuit for this next year. If I'm around, I'm gonna message you.

(Dave Wagstaff at 00:57:12) Please do. Yeah. Yeah. Look me up. I would love to contact you. I think you just have a fascinating, you're just fascinating in many regards, and so I'd love to catch up with you.

(Joel Beasley at 00:57:22) Excellent. Will do.

(Dave Wagstaff at 00:57:24) Fantastic. Thank you, Joel.

(Joel Beasley at 00:57:25) Thank you, Dave. You have a great day.

(Dave Wagstaff at 00:57:26) Okay. Same to you. Bye. Alrighty. Bye bye.

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