Navigating IoT’s Future: Insights from Warren Jackson of Dell Technologies
Welcome to the Innovators of Things podcast, a profile series series about the builders and leaders of connected devices powered by hologram. Today's guest is Warren Jackson, edge gateway specialist at Dell Technologies. Technologies. And now to our host, Jillian Kaplan.
Speaker 2:My name is Gillian Kaplan, and I am here with Warren Jackson who is an innovator of things. Right? We're talking about innovators of things here on the podcast. So, Warren, thanks for joining us.
Speaker 3:You're welcome. You're happy to be here. Excited about having this conversation with you today.
Speaker 2:So I wanna start out talking about IoT use cases because you've been in the business of IoT a little while. How long?
Speaker 3:A long time, actually. I mean, IoT, I think, as a term or a thing has probably been around for about a decade or so. And, yes, I think I've been in this space the whole time.
Speaker 2:Where were you, like, where were you before that? Or was it called something different?
Speaker 3:No. I think it started out as IoT, and now probably the more accepted term is edge. You know, IoT isn't as accepted. But when I was with GE, they coined the idea of IIoT, which you put an I in front of that, and it's industrial Internet of Things. Right?
Speaker 3:So, yes, GE was one of the originators of this concept back in the day.
Speaker 2:So I IIoT is more like b to b, whereas IoT is more like consumer devices in your Yeah.
Speaker 3:That's a good way of looking at it. Like industrial factory manufacturing is the IT and IoT is kinda like Fitbits and Nest thermostats and that kinda stuff, I would say. That's the way I look at it.
Speaker 2:Okay. Fair. We've I had a conversation with the last guest about, is the term IoT dead to your point? It's like now we're talking about edge Edge. Which, you know, is, in my opinion, really just an on prem cloud, but we have to call it something different because it's tech, and that's the way it is.
Speaker 2:So back to IoT, even though it's dead or maybe not. We don't. Still undecided whether that term is dead. I don't think we totally made a decision on the last podcast. What do you think is the most useless IoT use case you've ever seen in your 10 years from the inception of IoT until I mean, I think this particular one
Speaker 3:that we looked at, it never saw the light of day. I mean, I think this particular one that we looked at, it never saw the light of day, although it probably should. I would say it's an interesting idea. Typically, with IoT, you put the term smart in front of it. So smart factory, smart restaurant, those kind of things.
Speaker 3:Right? So this this one particular application, this was before the current job that I'm in, the it was a maintenance organization that their responsibility was to go around to all the rest areas on the interstates and maintain those rest areas. So they came up with the idea of a smart bathroom. So the idea of it is it would be sensors in the bathroom that told when the last time it would be cleaned and when it needed to be cleaned again. And even, you know, I see these charts, these pieces of paper in even the gas stations that say when the bathroom was last cleaned.
Speaker 3:Right? And, also, I've seen recently even in consumer, like, the Kohler the plumbing companies have come up with that idea of a smart bathroom as well. So I don't know if it's the dumbest thing I've ever seen but it's probably something that's not necessarily the best use of IoT, I I think. But You
Speaker 2:think it's you think you're you're better off checking the sheet, like, than having a alert? Is that your point?
Speaker 3:Yeah. Yeah. I mean, it would be an interesting concept. I'm not sure why it hasn't seen the light of day because they've been talking about this for, you know, 6 going 6, 7 years now. But it probably would you know, when we look at these kind of applications, anything that you're doing on paper right now would be possibility for using IoT for that or edge computing or something like that to give the person some alerts or information about, you know, taking some kind of action.
Speaker 3:So Okay. Interesting concept or Let me go through it.
Speaker 2:Something that you feel like because that actually sounds like semi useful. Right? Yeah. Like but what what would you say, like, someone put something out in market that you're like, but why? Like, that isn't useful.
Speaker 2:Can you think of anything that, like, IoT wise feel like doesn't make sense?
Speaker 3:Not really. I haven't seen one that Okay. Wow, I wouldn't absolutely buy that around manufacturing or retail kind of applications. I mean, most of them that are out there have been fairly fairly decent or or well received. So I I don't I can't think of one off the top of my head.
Speaker 3:I would say the smart smart restroom one is definitely one that hasn't seen the light of day yet but probably should.
Speaker 2:Interesting. For the bathroom cleaners of the world, make things a little bit easier. Very cool. Okay. I I actually hadn't heard of that one, which maybe to your point, there's a reason if it if it's stuck wherever it is.
Speaker 3:Isn't it a good idea, though?
Speaker 2:It is. Well, that's why when you brought it up, and I was like, what's the silliest I'm like going right there. At that. I'm like, I don't actually think that's a silly one. I think that one makes perfect sense and perfect logic.
Speaker 2:Right? So I don't know. That was
Speaker 3:There's a there's another one that stuck as well that in this space, they're really trying to innovate, and that's what I talked about before, the smart kitchen kind of idea. There are a few really innovators in this space. I'm not gonna mention the names of the but you can probably think of them as the ones that are super busy. And it's not necessarily things around the consumer piece of it. It's what you don't see in the back.
Speaker 3:So Mhmm. How often to change the oil in the fryers or, you know, what's happening out in the the front side of the restaurant as far as the lines and how quickly customers are moving through and stuff like that. That's definitely a space where there's a lot of activity right now, where the where the companies that are lagging are trying to catch up with those who are real innovators in this space. A lot of activity in that fast serve restaurant area.
Speaker 2:Like, what are some of the specific use cases? You touched on, like, lines. Like, what else do you see that improving in the restaurant space?
Speaker 3:The the equipment monitoring for sure is a big one. The one that I can think of off the top of my head is, you know, the fryers, but also the the refrigeration units as well. So food storage kind of things keeping track of your inventory and how product through and just being able to sensorize the refrigeration units and the storage units, the the fryers, the ovens, those kind of things. I think you're gonna see a lot of those fast serve restaurants driving innovation in that space.
Speaker 2:So, like, talking about that, you don't normally think of fast food restaurants as tech companies, but IoT really bringing them into the world of technology, which is interesting. And, you know, a lot of these companies have had to hire IT people, which is crazy.
Speaker 3:A lot of people go through the drive through of this large coffee chain. And I don't drink coffee. I don't go through the drive through. But I was in inside the the restaurant the other day, and something kinda caught my eye that they have screens that the people who work in the chain can see performance metrics. And something that's I think is always very important, you know, Stuart, from our working together, is when you create user interfaces, make it very simple so that when the user looks at it, it's not a busy screen very quickly in 5 seconds.
Speaker 3:They can look at that screen and see what behavior needs to be changed. So at this particular chain, they had a couple of very large screens with dashboards on them that had metrics on them that they wanted to measure. And I'm guessing the metrics were wait times or something along those lines, whatever their KPIs are, key performance indicators. And it was just color coded. Green, yellow, red.
Speaker 3:Very simple. Mhmm. People who work in the restaurant can see whatever metric I'm measuring here, yeah, it's green. I'm doing well. Whatever metric is, it's doing yellow.
Speaker 3:Okay. We're fine, but we need to be concerned about a little bit. And red, yeah, there's something wrong here that I need to take action on. So this particular chain, I thought that was very innovative that they had these digitized screens for their drive through people to look at to see what their performance was.
Speaker 2:Yeah. Making it, like, really consumable for people. Right? Like, so that they can actually measure themselves without having to have, like, some data scientist interpret it. You know what I mean?
Speaker 3:Yeah. And it drives for sure I'm sure drives behavior of the employees of the restaurant. So maybe the the chain gives there's a dashboard that says, you know, how how the employees are performing, that kind of stuff. For sure, I'm guessing the reason why they did that is to drive the behavior of their employees. And it worked.
Speaker 3:I've watched them looking at these screens.
Speaker 2:Okay. That's
Speaker 3:awesome. We've seen this behavior in some of the projects that you and I have worked on where I put much larger versions of these screens in manufacturing operations, and it drives people's behavior since they start looking at the screens to see how the equipment is behaving. Right?
Speaker 2:Yeah. Yeah. No. It's true. It's interesting.
Speaker 2:So as we move into the future of IoT Edge, whatever we wanna call it, we're gonna have to, at some point, make a decision on this podcast series if the term IoT is dead. I feel feel like I should probably ask every guest that. Yes or no? But no one's gonna answer me yes or no. There's gonna be a long explanation.
Speaker 2:But as we move into the future of this, I'm gonna ask what I feel like is the question on everyone's mind, which is around artificial intelligence. Right? Like, how do we apply artificial intelligence to IoT use cases? Is there anything you can think of that when you think about future IoT use cases, you feel like AI or generative AI is gonna be really applicable to? Like, if you could dream up something amazing, how would AI apply?
Speaker 3:So we talked at the beginning of this discussion around how long I've been doing this, and it's kind of since the inception. Right? And I this is one of my core philosophies and mantras around working with customers and trying to innovate with this space, and I wrote it down. I was at a conference for General Electric a decade ago, a long time ago. And one of the presenters put this slide up there, and it really resonated with me and said, oh, that's what IoT or why we're doing this is all about.
Speaker 3:I wrote it down and it said, quote unquote, if you knew the state of everything and can reason on top of that data, what problems could you solve? So if you think about what we're doing here, especially that's that quote is 10 years old.
Speaker 2:You still have the piece of paper sitting there? Yeah. Okay. Cool.
Speaker 3:Anyways, but that's yes. That kind of ties into what we're doing. Okay. Reasoning on top of that data was at that time, artificial intelligence was just in movies. Right?
Speaker 3:Now it's real. To tie that back to your question, something I see all the time in my current role is the camera video as a sensor. So, traditionally, sensors have been analog or discrete sensors, on or off, or they're measuring flow or they're measuring some type of performance metric. And, yeah, I'm coming from the manufacturing perspective there. Right?
Speaker 3:Now you have this idea of the camera as a sensor. Right? And using artificial intelligence AI models to see some type of defect or action or bad behavior or something like that and to reason on top of that video data and send out an alert or make somebody aware of, okay, I'm monitoring this video stream and I see something that's out of the ordinary. Traditionally, that's been if you think of I'm gonna use an example of casinos or people who are monitoring stores or something like that. Some person is actually sitting there watching those video feeds.
Speaker 3:Right? Yep. So now what can AI do for us? What we see now is AI models that will do what a human can do that determines that defective behavior or, you know, behavior that's not the norm. Right?
Speaker 3:Does that make sense?
Speaker 2:Yeah. Yeah. And so you're, like, applying that more to manufacturing. And what, like, secure when you're talking about, like, casinos, like, you think, like, security? Like, could they, like, monitor when, like, you know, people are looking suspicious or, like, their body temperatures get hot?
Speaker 2:Maybe they're gonna fight. I don't know. I guess AI could do that too. Right?
Speaker 3:Yeah. So this is an I probably deal with a customer application like this particular scenario at least once or twice a week. And I'll I'll describe to you kinda what it is. So this particular one is it's, overseas, but it was a kiosk where a person was going to pick up a rechargeable battery or a motor a motorcycle. So this think of the motorcycle as a power tool.
Speaker 3:Basically, take a battery out, go to this kiosk, you put the battery back in, you take a new one out and you keep going. What was happening was these kiosks were getting vandalized. People were spray painting on them. People were taking batteries, not paying for them, all that kind of stuff. So, the the challenge was to put some camera feeds around where the kiosk was because they can't monitor or have somebody watching 10,000 kiosks around the city.
Speaker 3:So the AI algorithm was, oh, this person is walking up to this kiosk. They're pulling a can of spray paint out, or they're acting a little bit nefarious. Right? They're shady. This is a shady character.
Speaker 3:I want to be able to come up with an algorithm that determines, oh, this person is a shady character. I want to send out an alert to have somebody go and take some action there.
Speaker 2:So Interesting.
Speaker 3:Yeah. That that type of application that I'm talking about there, analyzing video feeds so somebody doesn't have to be looking at it and have the AI do that, is, I think, gonna explode over the next 2 to 3 years.
Speaker 2:And that's really security play, right, which is needed across a lot of industries.
Speaker 3:It is. Another one that dealing with looking at obstacles in railroad crossings, or, you know, possible collisions of vehicles with pedestrians, that kind of thing.
Speaker 2:Yeah. Yeah. Really interesting. Yeah. So Security play is
Speaker 3:I I feel like the whole smart city thing has been a little bit hyped, but those kind of applications with smart city are definitely beneficial.
Speaker 2:Yeah. Yeah. I I like, probably 5 to 6, 7 years ago maybe now, I started looking at, like, smart city stuff. And there was, like they were they were testing it at a particular intersection in Boston where they, like, had a lot of pedestrian crashes. So they were, like, trying to see where the cars were going versus, like, the bike lane and what was causing it.
Speaker 2:So they had sensors there. But it takes a ton of sensors. Like, if you think about, like, the size of a city, just the amount of equipment you need to be able to monitor everything is overwhelming. You know what I mean? So I don't I don't know how you solve for that, but something we'll definitely need to figure out as these get more mature, right, versus because you can't have sensors every foot in a city.
Speaker 2:You know?
Speaker 3:Not sensors, but cameras that the idea that we talked about at the beginning, the idea of a camera as a sensor. So I know that particular concept or idea is near and dear to your heart, like monitoring collisions between cars and pedestrians and crosswalks. That is a really beneficial thing. If you can save somebody's life by putting a camera and just monitoring that intersection and say, oh, this is a potential for a crash. And I know in the city that I live in, what they've done now is because you'll see cars that just blast through crosswalks.
Speaker 3:Right? They they don't even know they're there. But in certain high traffic crosswalks, what they've done is they've put a big yellow flashing lights, big signs so they know in this particular crosswalk, there's a lot of people that cross here. So we're gonna make it super, visible to people who are driving that, hey, if there's a person in this crosswalk, you need to stop and not blow through it. So there's barrels and all kinds of different visibility things in those kind of high traffic crosswalks.
Speaker 2:Yeah. Makes sense. Like, they're trying to prevent you can't, you know, you can't. We don't have autonomous vehicles yet. Right?
Speaker 2:So the car can't automatically stop. But at least knowing, like, this is a potential based on the sensors and the video and the whatever we've taken to know this is a high collision spot, we're gonna put up extra warnings is is super key. I actually have an app on my phone now. I just changed insurance companies where they, like, track how I drive, and I get rewards if I'm not speeding, not distracted, not whatever. It's very interesting.
Speaker 2:So we'll see how that plays out. It's been it's it's brand new. But I like looking at it and being like, oh, I scored 99 on that drive. Right? So that
Speaker 3:So there you go. That's an example of how a dash
Speaker 2:board Yeah.
Speaker 3:Drives behavior. Right?
Speaker 2:Yeah. Exactly. I'm like, I did so good. I didn't speed at all during that. But in this instance, I think, like heartbreaking speeding, phone use, and I don't know.
Speaker 2:I have to look at other things. But one of the downfalls is, like, if I'm a passenger, it thinks I'm driving. And, obviously, my behavior is much different when I'm a passenger than if I'm driving. Right? So I've gotta, like, go in and mark it as passenger.
Speaker 2:And they say that, like, oh, they'll start to figure it out. Like, it there's a generative AI component, I guess, a learning component. But so far, there's no figuring out on my side. So we'll see how the generative plays into that. Right?
Speaker 3:Another side comment on the topic of IoT that falls into that category, the IoT, the consumer piece of it is the this whole proliferation of Fitbits and garments and, you know, those kind of fitness measuring devices definitely drives people behavior, right? And I was one of the early adopters of that. I had a Polar watch measuring heart rate and steps and all that kind of stuff 15 years ago. Yeah, you have it, right?
Speaker 2:Yeah.
Speaker 3:And it really, it's really good in that, okay, one of the other core philosophies that I have besides the one that I just told you is another simple one. And this one I didn't write down. I have it memorized. If you don't measure it, you can't improve it. That applies to everything, whether it's IIoT or whatever it is in your life.
Speaker 3:If you don't measure it, you can't improve it. You have to have a baseline, right? And for sure, with your fitness level, that's super important, right? You have to know what your base heart rate is, how fast you can run a mile, how fast you can cycle 15 miles, all that kind of stuff. And I feel like now even the to tie the AI piece of it into it, these companies that manufacture these products will will use generative AI and AI algorithms to see where your baseline is as far as your fitness level and suggest workouts that you can go through to improve those fitness levels.
Speaker 3:Right?
Speaker 2:Yeah. Yeah. It's so funny you say that because, like, my watch is at 8%, and I have to charge it, and it gave me a warning. But, like, I don't wanna charge it when I'm gonna be walking because, like, do my steps really count if I get it on? So, yes, it drives my behavior a 100%.
Speaker 2:So this was, like, a really fun discussion about cool IoT use cases. Do you have any closing closing thought thoughts before I do our data dash where I'm gonna ask you, like, quick hit? 2 two choice questions.
Speaker 3:No. Let's go let's go for
Speaker 2:it. Alright. Here we go. Ready? Data dash.
Speaker 2:Dogs or cats?
Speaker 3:Dogs. Easy.
Speaker 2:Bike or run?
Speaker 3:Wow. That is hard.
Speaker 2:I know. I figured that was gonna be
Speaker 3:Bike is my happy place. I am peace at the world when I'm on 2 wheels, so bike.
Speaker 2:Okay. Boat or swim?
Speaker 3:Swim, definitely.
Speaker 2:And this one is especially for you. This is gonna be a really hard one too. Mountain Dew or Red Bull?
Speaker 3:Wow. That's an insider question from Jillian. They both have caffeine in them. It's it's Mountain Dew, but I have to preface that. As I've gotten older, I've had to switch to the diet.
Speaker 3:But in my college days, I drank way too much of the, what I call, full octane that
Speaker 2:we're doing. Warren loves he doesn't he preff he said in the beginning he doesn't drink coffee, but he certainly gets enough caffeine. He always has some sort of caffeinated beverage.
Speaker 3:Though. Jillian and I were at an event recently where I was sucking down Red Bulls, and I said, I'm having heart palpitations. And Jillian said, oh, you need to stop drinking.
Speaker 2:Yeah. Come with the red bull. We're done. And then there's me. I can't do coffee either, but I do very little caffeine or I'm, like, off the wall.
Speaker 3:Yes. If you Jillian on caffeine, I would like to see that sometime.
Speaker 2:Yeah. Yeah. Would not be great for anyone. No one. Thank you for joining me.
Speaker 2:This was super fun. Excited to connect with you in the future.
Speaker 3:Yeah. Same here. As as always, it's great talking to you and spending time to you. It makes me very happy.
Speaker 1:Thank you to our guest Warren Jackson. Please make sure to subscribe to the Innovators of Things on your preferred podcast platform and keep up to date with the show at innovatorsofthings.com. Join us on our next episode with Rob Tiffany.