Beyond Big Data with Darren O’Reilly

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: welcome everybody to the Making Better Decisions podcast. Today’s guest is a capable and dynamic engineering leader with vast experience in operations and capital equipment. Looks to expand his experience in growing technical product business in innovative and high velocity environments. He also has a strong technical background with significant customer development experience.

Ryan: Please welcome. Senior Director of Software and Technology at JBT Automated Systems. Darren O’Reilly.

Darren: All right. Thank you.

Ryan: So I want to start [00:01:00] off with the same question that I’ve been starting everyone off with. Which is what is one thing you wish more people knew about using data to make better decisions?

Darren: All right. Well, that’s the loaded question of the, of the, uh, millennium here. Right. Um, I, I think for me, just off of my experience and, um, and we’ll get into it later, but I’ve, I’ve worked in a lot of, uh, different industries throughout my career is that, uh, more data isn’t necessarily. Translates to more results.

Darren: It’s about understanding truly the, uh, the right data and, uh, understanding what you’re trying to actually figure out as a problem and, and, and how to, uh, you know, really draw conclusions from that. Um, I think in my experience, it’s been, you know, as, as like this whole, uh, IOT revolution and, uh, big data has grown.

Darren: It’s that, [00:02:00] you know, certain customers come to you and they say, Well, we need all this data from you. And they don’t necessarily know what they’re gonna do with the data. They just want it. And, uh, so I think the real challenge is, is really helping to work with the customers to really understand what they’re trying to do with the data and, uh, help ’em be successful with it.

Darren: ’cause I think the big thing is, is that if you can help your customers be successful in their business. You’re going to be successful in your business.

Ryan: 100 Yeah, I I like that a lot. I I Both in my own consulting experience and from talking to folks here on the podcast There are kind of a couple different categories of places right where people are using data so they may just be I’m going to pull something together to make a business decision for myself or it might be i’m building something for You You know, the sales team, so they know where to go, you know, to target people.

Ryan: I really like getting to discuss with you the [00:03:00] use case of actually providing data and reporting to clients, kind of across that boundary. And, you know, from having done this for a little while, I’m totally with you. Having 500 reports is almost the same as having no reports. You know, I think you really hit the nail on the head.

Ryan: Like, what is the customer’s goal? How are they trying to succeed? And if they can’t quickly and easily get the information that they need to do that, then, you know, they’re kind of in a, uh, a tough situation. So, you know, based on some of your experience drawing from having been in a lot of different types of jobs, what are some of the places that you’ve seen data make a real impact for folks?

Ryan: Hmm.

Darren: Um, you know, I’d say like, uh, one, one thing that just kind of comes to mind is when I, uh, work for a company. In the, uh, semiconductor capital [00:04:00] equipment business, you know, 1 of 1 of the things was, is that really trying to, um, understand, like, you know, actuations of certain parts and really tracking, you know, when these parts are going to start to fail and really kind of starting to.

Darren: Dig in and, and, you know, look at it and apply like, uh, machine learning type algorithms to it to really extract, you know, things like, you know, anomaly detection, uh, onto this data and actually really, uh, understand, uh, what, what’s really going on and what’s, what’s really relevant as far as, uh, When we’re going to start seeing failures happen before they happen and stop the catastrophic failures of things like, you know, pumps and stuff failing on a wet process, uh, semiconductor tool.

Darren: Uh, so, and again, that was one of the things that was, I was able to do with a couple of partners [00:05:00] in the mix is like really understand like

something that was going from, you know, 500, some major parameters coming in and actually understanding that it was about 13 or 14 that were actually relevant for determining what was going on in the system.

Darren: And and so that’s just 1 example, but, you know. It, it boils down to what I do today and in the AGV world as well, where we have a lot of big customers that, you know, have a big push to get lots and lots of data, but it’s working to really understand with them, you know, what are, what are you trying to really get out of this?

Darren: Is this something that you’re trying to do a. You know, optimization of some sort. Are you trying to understand, uh, where your failures are? Why, why are you failing? I mean, it’s like one of those things where you’ve got to go and work to, uh, drive intelligence and how you evaluate things. And some of it might be as just in the way that you provide diagnostic data and [00:06:00] that, you know, it’s not every diagnostic is equal and that driving from just Uh, counts of diagnostics to actually really narrowing down to what are the diagnostics that actually matter and that are actually going to be a catastrophic failure or show that there’s something major going on.

Darren: That’s not just a nuisance.

Ryan: I like that. That, that spawned a couple of questions for me, um, that I wrote down, but before we go much further, I’m familiar with the AGV market a little bit, but could you help the listeners understand what that is?

Darren: Sure. So, um, for simplification purposes, it’s effectively. Uh, robotic, uh, movement systems to handle inventory of some sort. So what we produce at, uh, JBT, uh, it can be in a variety of applications and they are different [00:07:00] robots, but they’re basically robot vehicles. That interface into a customer, uh, system to accomplish a movement of Inventory, right?

Darren: So it can be everything from, you know, a warehouse moving, uh, from, from a, uh, you know, a semi truck, uh, bed to a shelf or in, you know, several applications. It can be moving a, um, a food cart in a hospital from the cafeteria to the hallway on floor six in a hospital. So, um, it’s, it’s all about automating the movement of inventory.

Darren: And, and with that, it’s about setting up systems that are. You know, able to cohabitate with, um, humans in their interactions so that it is a safe operation, um, but to help [00:08:00] support, um, maybe, uh, removal or

adjustment of, uh, you know, certain, uh, types of, uh, manual tasks that would have gone on at certain operations.

Darren: And then, you know, as an output of that, you, you can have, uh, potentially a more efficient operation and, and you can have a safer operation because you’re, you’re taking out that human element.

Ryan: Yeah. Now what’s actually like interesting about you guys at, at JBT and this stuff, like I hear like, you know, whether it’s like a warehouse, a hospital, whatever, like, uh, Hey, like I have like a totally automated robot run warehouse, like to someone out on the street or like me, I’m like, holy cow robots, right?

Ryan: Like this is like cutting edge. Like, you know, I just have the one annoying robot that gets in my way at the grocery store, but you guys have actually been in this game for a while. Like this is not. You know, came out yesterday, stuff for you.

Darren: No, right. Yeah, no. So [00:09:00] we’ve, we’ve been in the AGV business for over 30 years and, uh, it’s, uh, yeah, it’s, it’s, it’s significant. Yeah. Honestly, like some of the bigger challenges that we’ve had is just more of. You know, evolving with like, you know, as the technology stack has evolved, just because we’ve had systems in place for a long time.

Darren: And, and, you know, we have a lot of customers and stuff like that, that have been in operation for 15 plus years and the technology has evolved and we have to get them upgraded. So just as things, you know, go obsolete and are harder to support from a hardware and, uh, and software standpoint. Right.

Ryan: when I think of a lot of the data processes that I’ve interacted with, they’re all fairly human, right? With the exception of the, the IOT space that you talked about. And when, when you start going [00:10:00] to this place of, you know, robots, much higher levels of automation, using IOT for everything, that, that’s, that’s Puts you kind of in this situation where, you know, if I’m a mom and pop store, I have my inventory, I have my point of sales, I have my cost information, you know, that kind of stuff.

Ryan: Like that’s a very manageable data size. Whereas for really like any type of, you know, big capital project or any type of like fully automated operation, the quantity of data that you have to sift through in order to find the right word. You know, if I can steal Nate Silver’s book title, right. The signal out of the noise gets harder and harder.

Ryan: So how are you guys handling, you know, the quantity of data that it takes to do that stuff well?

Darren: Yeah. So that, that, that’s really driving a lot of the, uh, technology stack migration that we’re in the middle of right [00:11:00] now. And, and just to even give you a little bit more light to that is that we’ve gone from systems like maybe 15 years ago, we’d be seeing 10 to 15 robots in an operation. And now it’s, it’s, it’s.

Darren: You know, we’ve got several operations where there’s 100 to 200 plus robots all being coordinated within a system. And so, um, so which, which challenges to, you know, how, how we’re structured within our, our databases and, and how we handle the data and what data is actually relevant. And obviously some of that stuff is like going, getting into.

Darren: You know, time series type database interfaces and being able to use a lot of the modern tool set that’s out there, uh, that enables us to get into like, you know, some of the, Advanced features of, uh, AI and ML that, um, you know, that we may have not have touched in the past. Um, so yeah, I mean, it’s, it’s about having that scalable architecture and it’s also [00:12:00] about, um, really making some decisions where, you know, I, I can say, you know, I’ve had discussions here with, with the overall technical team of things that were made as a decision, like maybe 15 years ago.

Darren: That doesn’t actually translate to a lot of value for the customer. And that we were always transmitting this huge amount of data to really kind of help facilitate, you know, extremely, extremely high fidelity information all the time and really kind of scaling it so that, you know, in the, uh, future we’ll have top level visibility.

Darren: But if they really want to get into the nuts and bolts of it, um, we’re really kind of having like a secondary, secondary viewing that allows them to get into that, uh, high fidelity data, especially, you know, and that’s typically more needed for when the customers are going through like a debug type operation or, or, uh, optimization, you know, normal day to day [00:13:00] stuff, they’re not, they’re not really looking at that.

Darren: So it’s about maybe removing that massive amount of data. You know, storage and transmittal when it’s not necessarily needed. And it’s, it’s about thinking about it because the easy answer for data is always just save it off. Who cares? Computational power is cheap. Data is cheap. Well, you can find a way to max things out, even with, you know, modern architectures and

Ryan: Yeah. 200 robots. Yeah. That’s yeah.

Darren: Right. Exactly. And the other thing though, too, is like, as the stuff, as the stuff gets bogged down. You know, if you don’t have the right data coming through, um, you, you’re not able to really truly sense a true problem in an operational problem. We don’t want to have. Excessive information that is being presented to the customer that, um, translates to confusion.

Darren: I mean, one of the things that we’re really working to hear at JBT now is to, um, [00:14:00] move away from, um, some of this stuff where it would sit in a control room and we’d have an application that is showing the movement of all the robots going on and that you see one robot jammed up and. It has like 10 robots behind it.

Darren: Well, you don’t actually necessarily know where all that stuff is, uh, what, what the root cause of it is. And so it’s about driving to having more, you know, okay, Hey, we know what these particular scenarios, that means robot one is causing the jam up of all these other 10. So if you didn’t have to look at robot one, you weren’t going to see where the problem is.

Darren: And. Really giving context to that data so that the customer can then go and say, hey, oh, I better go out there on the floor and fix that problem with robot one. And then the rest of these problems will go away. So that’s a lot of what we’re trying to go to is rather than, you know, needing to have somebody with like a computer science degree or electrical [00:15:00] engineering degree to be able to go through, figure out what’s going on.

Darren: That the con the system’s really given more context and then there’s a less, uh, you know, complication that people have to go through.

Ryan: Yeah. You know, what’s, what’s cool to me about this is like, as if like, you know, uh, a robot warehouse isn’t like futuristic and cool enough. I love like hearing that you guys are like constantly thinking about how to keep it more cutting edge and how to like upgrade everything, how to incorporate the new stuff from, from AI and ML, you know, incorporating the new, you know, kind of, Ways of thinking about data mining, especially as like the quantities of the underlying sensor data get out of control.

Ryan: Um, that’s, that’s really cool to me. I mean, obviously. With, you know, Chachi, BT and LLMs, you know, I think like AI has really blown up in public discussion. My experience from [00:16:00] a data perspective is, yeah, that’s,

that’s awesome. This is super cool, but like, Hey, there’s all of these other categories of artificial intelligence out there.

Ryan: Like all, you know, we have, you know, random forests and, you know, clustering and, you know, all these, like even just. Regressions and things like that. There are just so many other cool tools in, in the toolbox. Um, how are you guys kind of reckoning with the AI revolution and what are your thoughts about how, like even separate from JBT, like how can an organization approach thinking about this stuff?

Darren: Um, well, I mean, there’s, yeah, so I think like the whole chat GPT thing is its own beast. And, you know, internally we, we, we have a path for that. And I think there’s a bunch of stuff that you can do, um, in particular for, uh, servicing. Vehicles and stuff like that, where it can have more intelligence. Uh, Like server type [00:17:00] bots that you type in and say, okay, I have this problem.

Darren: What could it be where it can really understand your literature and be able to extract out, you know, answers for that. I think that’s separate from, you know, where we see like, um, things like, um, AI and, uh, ML. applying to, uh, you know, our overall operations. So for, for example, I’d say, you know, today we, we have some, you know, we’re, we’re in the process of a major, uh, technology migration right now, uh, platform wise, um, as that gets more complete, we’re going to be able to take on a little bit more in that space.

Darren: So I’d say right now. We’re in a migration path. And again, this is typical of what happens when you have a organization that’s been doing it very well for 30 plus years that, you know, that, that you have tech that, that you have to evolve from. But that said, I think. [00:18:00] You know, where, where I think the big wins in that, uh, data mining space and really applying the, uh, artificial intelligence is like understanding, um, predictive where, uh, you know, definitely similar to what I was talking about with the semiconductor world that I’ve dealt with is that there’s definitely Uh, some parallels with that.

Darren: I mean, capital equipment’s capital equipment, you know, it’s, it’s all about making sure that it stays up and that the customer is able to make money. And then also on the, um, artificial intelligence side is that I think, you know, for now, for, you know, when you think of like an AGV system, when it’s moving around in a warehouse, for example, it’s really like, you know, Part of the deployment is spending the time to lay out the train tracks, for lack of a better term, of like, really doing all the path planning that is the same and that is predictable, which also makes it one of those [00:19:00] systems that it’s, you

know, where the robot’s going, so, you know, you’re not going to go jump in front of a, you know, a forklift that’s fully loaded.

Darren: You shouldn’t be going right in front of a, um, AGV that’s fully loaded, right? Uh, it’ll stop, but you know, there’s physics that you got to fight. So, so the, the, the interaction with that, there’s, there’s certain levels of safety that we have to consider within, uh, the operation. But that said, um, as far as optimization, I think there’s a lot of opportunity of applying some of the, the AI algorithms to really say, Hey, what is the most efficient way of moving around the pieces of inventory in here?

Darren: In terms of maybe space optimization or, uh, time of flight for the operations so that you don’t have, and reducing the number of moves of inventory. So there’s, uh, there’s a lot of stuff that we’re, we’re currently kind of just You know, cracking the shell on, but, uh, we’ll, we’ll definitely, uh, be, [00:20:00] uh, making this, uh, something very robust and, uh, and viable for, for our end customers.

Ryan: Yeah. That’s At least to me, I think a great use case that’s very generalizable to so many different areas of business. You know, like you talk about, okay, I’m, I’m imagining, you know, I keep using the warehouse. I know you guys put them in lots of places,

Darren: Yeah. Yeah. That’s all good.

Ryan: you know, um, okay. So like I have a warehouse and I have one robot, Really easy with train tracks to be like, what’s the shortest path for me to be.

Darren: Right.

Ryan: But now when I have 200 robots and I’m trying to figure out how to totally optimize that whole system and they all can’t be in the same place and running through one another at the same time, this turns into the type of logistics problems that everybody has everywhere.

Darren: Right.

Ryan: [00:21:00] And it’s the type of problem that, as you said, there are like really cool, um, You know, models that can be built that are, you know, it’s still AI. It’s not chat GPT. It’s not a large language model, but there’s so many cool tools that are out there. When I hear people talk about AI, that’s kind of 1 of my, my big takeaways is like, yeah, that’s great.

Ryan: And look at all this other cool stuff that can be done. So that’s, it’s really exciting that you guys are, are moving down, um, that route.

Darren: Yeah.

Ryan: If I can, if I can pivot a little bit away from the AGV workspace, you know, you talked about how kind of capital equipment is capital equipment. And I’ve, I’ve, I’ve done, you know, quite a few projects, whether it’s, you know, building, uh, you know, refineries or, uh, purchasing large pieces of equipment and then putting them into use.

Ryan: Like you really [00:22:00] talked a lot about how. Uptime and productivity are kind of your key factors. So how have you seen the availability, quantity, quality of data, change your ability to achieve those goals over time? Yeah.

Darren: yeah, I mean, it’s, I could, you know, I, I worked for a Caterpillar for 15 years. And I was heavily involved with a lot of the, uh, IOT type initiatives there and really driving to get, um, extraction of, uh, of data from equipment. And a lot of it was in the space of, uh, retrofitting technology onto existing fleets. And one thing I could, you know, say that was like, you know, fairly interesting is like, when we introduced [00:23:00] a fuel consumption measurement system in that space, we were really able to, and we were able to retrofit that on to, um, you know, old mechanical engines and stuff that wouldn’t have normally had that type of data available, um, really help a lot of customers understand Where they had wasted money in terms of like, where the engines were operating, whether or not it was in, you know, something like a generator set application or, or, you know, even just like oil and gas type application, like, uh, frack rigs or well, service, work over eggs, really understand where the waste is and also as a side note to being able to actually detect things like theft.

Darren: So, um, some really, uh, interesting, uh, you know, problems that you can help solve because, and that’s with a relatively low amount of data, a low bandwidth of data coming back, you know, it’s really just more of like how much fuel just got used. So it’s really interesting that if you [00:24:00] narrow it in with a very specific, um, you know, problem, you’re going to be able to, uh, extract some meaning out of that, you know, and it’s about like really understanding where the data that has value is.

Darren: So, and so it’s, it’s, it’s just, it’s fascinating when you really get into it. Cause you can go, you know, it’s all about, um, you know, simplicity, you know, the, that simple answer is going to really, in a lot of cases really be where

the root of it is. But sometimes you just, sometimes there’s value in, uh, doing a little bit of a shotgun approach, feeding a ton of data into it and see what you learn from it.

Darren: And you might just, and that might point you to the direction of where you actually saw something simple. Be in the answer, but this actually helps you validate that,

Ryan: Really cool use cases. Um, I, most of the time I always think of, you know, a lot of the IOT stuff as being like [00:25:00] temperatures and positions and pressures and, and all that stuff. But like making it so tangible is just like, cool. Yeah. You have this huge fleet. Like how much fuel is it all using? Um, you know, that’s, that’s the type of analysis that like, you know, you put that up for a week and it’s like, bang, bottom line impact immediately.

Ryan: That’s so cool.

Darren: Right. Exactly. Yeah. It’s like, yeah, it’s not just a simple sensors. There’s a lot of stuff that you can get through. And then also it’s, it’s sensor fusion, right? So you can go and take multiple sensors. And be able to understand, um, what, what’s happening here. Is it, is this something that we’re starting to see, uh, based off of, you know, several sensors, pressure and temperature sensors in a hydraulic system?

Darren: Um, are we, um, starting to see a, uh, uh, you know, a pump that’s nearing failure? And, you know, stopping that, uh, catastrophic, uh, repair that you can go and say, all right, if you take the downtime now of a half a day [00:26:00] for your equipment and replace it, it’s better than, you know, being down for, uh, six weeks when everything blows up and, uh, spreads through your entire, uh, system with contamination, you know, like that, that type of thing.

Darren: And that can be pump, pumping, uh, You know, hydrofluoric acid in the semiconductor industry, or it can be hydraulic fluid, it doesn’t matter. The, the, the, the types of problems are similar.

Ryan: Yeah. Yeah. That’s. That’s very cool. I mean, you know, like, like you said, right, you got to do it. You know, I’m imagining some kind of big piece of equipment and it’s like, it’s always going to break down in the least convenient location. And it’s like, all right, well, shut the whole place down. Let’s get a crane in here and get it out of there.

now, it’s like, oh, well, if we know it’s going to fail, we’ll Drive it over to the maintenance

Darren: Right, right. And it’s, it’s all about, yeah. So yeah, for, it’s all about productivity, right? So if you can really get that planned maintenance and you can optimize the maintenance, right. It’s about getting to understand where [00:27:00] rather than, you know, just going by the X number of hours or X number of miles of, uh, operation that we, we service this.

Darren: You can go and say, Hey, look, maybe you can get more, um, life out of your oil by understanding what are the conditions that it, it faced during its operation. And then maybe you get double the lifetime out of it. And that, that, that translates to less downtime. Cause that’s one less oil change. That’s less chemical being wasted.

Darren: There’s a lot more from a sustainability standpoint, you know, that’s very important today in today’s world that, you know, we’re doing whatever we can so that we just, we, we stop waste.

Ryan: Yeah. Yeah. That’s very cool. It’s what an awesome answer, man. Like that’s such like a win, win, win. You know what I mean? Like

Darren: Right.

Ryan: the clients are winning. You’re winning. The environment’s winning. You know, people are making like, it’s just like all [00:28:00] around better when you have clear, complete information and you’re capable of making good decisions.

Ryan: Holy cow. Like, look how amazing everything gets.

Darren: Right, right. Yeah, exactly. And you’re, and you’re, and you may see something that you thought was not optimized before, maybe actually more of a true value operation than you ever thought before, because now you actually have true data to correlate it with rather than kind of top level operational. Oh yeah.

Darren: That looks like that’s okay, but Oh, you know what? I need a bigger piece of equipment running there. And it may not be the case that that translates to something more efficient. Right.

Ryan: sticking with, with the title of the podcast, Making Better Decisions, I have encountered a lot of people who know that they have decisions to make, um, but it’s, it is really cool to, like, hear use cases like that and then also to have, you know, kind of work through the process with some of those people and you find out how, you know, Of [00:29:00] course, like, sure, it will at the team lunch.

Ryan: Should we have, you know, ham sandwiches or chicken salad? Right? Like, well, that’s the decision, right? But then, you know, like, really realizing if we get structured around, like, this data driven decision making mindset, how massive the impact can be now, there’s, um, there’s 1 thing that we can’t go past without me asking about it.

Ryan: So I know that. You know, Cowpillar sells a lot of different types of equipment, but when you said theft, I’m imagining somebody stealing, like, one of those, like, Gigantor dump trucks or something like that. Like, does that happen? Like, someone driving a backhoe down the highway to try and get away with

Darren: Sure. Yeah. Yeah. Yep. That has, that does happen. Yeah. Um, you know, obviously I don’t think someone’s going to take out a, uh, it’s as easy to take out like a 400 ton mining truck, but, um, yeah, no, that, that is, that, that was a common problem in [00:30:00] that industry. Yeah. Especially, especially more of, uh, in the building construction product space.

Ryan: yeah, that’s, uh, that’s wild. But again, data to the rescue. I’m glad that, you know, sensors and, you know, accurate lower latency reporting has made that less of an issue.

Darren: yeah, well, and, and, you know, even to that aspect too, is that, you know, there’s been aspects and, uh, you know, other industries that I’ve worked in is that you really got to have, you know, some, a certain amount of tamper detection too. Because data is powered if you have something that happens at a particular site where the capital equipment’s being used, whether or not, you know, it’s a prescription fill in IV robot, um, or a, you know, a large scale freeze dryer, you know, if certain things aren’t being done correctly, you really need to have that traceability so that you can really understand, okay, is this actually, you know, A machine operation problem, or is this [00:31:00] something that, um, is due to maybe malintent or maybe a misunderstanding or lack of training, right?

Darren: So there’s a certain amount of things you can get out of that too. It’s like, you see, as these people use this equipment, you’re, you’re seeing that, okay, one operator has this particular amount of consumption of product when they, when they, which translates to a certain amount of product output, the next person comes through and there’s like 20 percent less efficient.

Darren: Why is that right? And that, and that, that you can get a lot of interesting, uh, information from an insight from it that before it would just be more of like, okay, you know, person a is, is just, just happens to be better at operating that machine. now we can kind of go and say, well, why, right. Or, or is there something.

Darren: That we need to change from an operation standpoint with the way that we operate this equipment to get that true efficiency. And so there’s a whole bunch of [00:32:00] stuff you can get in that space that, um, I don’t think people were looking at even, you know, five, 10 years ago.

Ryan: Yeah, that is unbelievably cool, um, as a use case, you know, just thinking, you know, you know, what I immediately thought of as I was like, wow, that sounds cool for me. Like, I would love if, you know, like the things that I’m doing, like they could tell me how to improve. And then I thought about, I was like, you know, we really need that for, we need to put that in everybody’s car.

Ryan: And when they’re driving crazy, it pops up and it’s like, you need to go back to driving lessons.

Darren: already a certain amount of that, right? I mean,

Ryan: Yeah.

Darren: um, you know, there’s like rental cars, even my own car. Like you, if you’ve been driving on the road for a road trip for like an hour and a half and. You know, it notices that you’re, you’re adjusting away from the lane detection a couple of times. It’ll pop up and say, Hey, time for a break.

Darren: I mean, there’s, there’s things like that, it’s all showing up. But yeah, I [00:33:00] mean, and that’s, that’s really the question here. And, and then we get into like kind of the, you know, the, the, the data privacy concerns and stuff like that, that kind of come in that space. But, um, you can navigate that effectively.

Darren: William. And make sure that people are really aware of what’s being done from a data standpoint. Um, there’s a, there’s a lot of great things that can kind of come out of it. And, um, and yeah, yeah, potentially we can go in, uh, detect when people are potentially going to be hurting, hurting someone, you know?

Ryan: Yeah, that’s that’s very cool. That’s also a very interesting point that you bring up between kind of the, you know, the trade off between value and security. Um, you know, really is, you know, this was a handful of years back now, but, you know, I think it was, you know, it was really starting to, like, come out, like, just how much of.

Ryan: You know, our personal information is stored on the internet and is stored by all these companies. And you know, I think, you know, there was a lot of, you know, [00:34:00] public talk about it. And I remember listening to, I think it was a podcast that, um, you know, an executive at Google, like one of their, you know, privacy and security.

Ryan: Types there was came out and made this statement that kind of like totally flipped it on its head. Right? Like I was expecting him to just kind of be like, Oh, we don’t have any of your data. Right. And he was like, we totally do. Right. We have, you know, we have Chrome, we have Android, we have your search history.

Ryan: We have all of these things. And he said that our goal is to only use that information to provide you value. And so what we want is we want to be transparent with you and we want to make sure that. You feel like you got a suitable or an exceeding amount of value for the amount of privacy that you were willing to give up for us. And you know, I, I thought that, you know, the idea that just like no information is going to be on the internet, like I think that ships sailed. That’s never going to happen.

Darren: right? Yeah. It’s, it’s there. [00:35:00] Right,

Ryan: You know, like you, like you were talking about that. It’s, it’s really just about saying, okay, I realized that you gave me something.

Ryan: You’re giving me some of your information. How can I add value to that? So that it’s worthwhile for you.

Darren: right, right.

Ryan: That’s, that’s very, very cool. Now. I also want to, you know, in addition to getting like some of the best data use cases that I’ve heard on the podcast so far, I also want to give everybody an opportunity to get to know you a little bit.

Ryan: So tell me a little bit about yourself. What do you, you know, what’s your background? What do you like doing outside of work?

Darren: All right, well, um, I, I’m, uh, well, I maybe can give you my life story here, or,

Ryan: All right, cool. Fire away.

Darren: so, yeah. Um, so I’m, I’m a Canadian citizen. I, uh, grew up in Alberta, Canada. Went to the University of Alberta for my electrical engineering degree. Career wise, I’ve [00:36:00] worked on, like I alluded to before, uh, every piece of, uh, capital equipment, I think, in the world at some point, um, I, uh, I, I started out early, early on in my career, uh, working, uh, on, uh, unmanned aerial vehicles, so worked for a startup in the, uh, Pacific Northwest area that eventually got, uh, Purchased by a Boeing for military application.

Darren: Um, then I, like I said, I spent 15 years working for Caterpillar, doing a bunch of stuff, starting out in electro hydraulic controls, uh, transmission controls, do a lot with oil and gas application, transmissions, frack rigs, well, service work over rigs, and then, um, went over to, uh, I was based in Australia and Asia Pacific for three years and, um, covered a lot of, uh, custom, uh, non competitive equipment to Caterpillar.

Darren: So everything from, uh, salt harvesting [00:37:00] machines to, uh, uh, sugarcane hauling locomotives to, uh, foundation drills. Uh, and, uh, even had, like, uh, I even worked on a, uh, a rail, uh, service, uh, locomotive in Indonesia that was used by the Ministry of Rail there that actually had a full karaoke system in the middle of it.

Darren: So, uh, so you could actually run a karaoke while you’re heading down the track and inspecting the track. We did that, um, you know, then came back, uh, to the U. S. and ran like a, uh, retrofit and upgrade group, uh, relative to kind of helping deal with, uh, legacy electronics and, and, and getting, you know, current technologies up there for things like a lot in the, uh, generator set space.

Darren: And then also to getting on, uh, IOT type sensor technologies [00:38:00] onto, uh, customer equipment to really extract information. Um,

from that, worked in the, uh, semiconductor industry for a while, was, uh, engineering manager of, uh, a group that did, uh, batch wet process, uh, equipment. So, uh, uh, so basically, uh, spray acid tools, spray, uh, ozone tools and spray solvent tools primarily used for the creation of like, uh, MEMS sensors, LEDs, power electronics.

Darren: Uh, a lot in the 8 inch wafer space, which has kind of got a resurgence as a lot of technologies have become more relevant again. Um, and then, uh, the last little while before I came to, uh, JVT, uh, working in the biomedical world, worked on, uh, large scale, uh, lyophilizers and, uh, vial fill finish lines for a while.

Darren: And then. a period of time worked on a system that was automatically prepping IV bags and syringes [00:39:00] for hospitals, uh, pharmacies. And then, but like I said, it’s all the need.

Ryan: an example you just pulled out of the air, man. You

Darren: Yeah. Yeah.

Ryan: everything, dude. Yeah.

Darren: that is a product that, uh, um, I, I, I did, uh, um, manage a team, uh, development on, on that.

Darren: So, um, but, uh, yeah. And then. Uh, and you know, then kind of coming into the AGV world, but then, you know, um, you know, from a personal standpoint, uh, I love to travel. I’m a, you know, being Canadian and filling the, uh, the stereotypes. I, uh, I play hockey at least a couple of times a

Ryan: Yeah. There you go.

Darren: No big, big fan. And, uh, obviously I’m a.

Darren: I’m a huge Oilers fan, so it’s, it’s been a rough few years. So, uh,

Ryan: You got, you got, you got an opportunity this

Darren: Yeah, I got an opportunity this year. Um, yeah. And then, uh, outside of that, uh, I have a, I have a 10 year old daughter who, uh, is obsessed with

figure skating. Uh, but, uh, [00:40:00] uh, I tried to get her to play hockey and it just wasn’t going to happen.

Darren: So I, I took the consolation prize on the, uh,

Ryan: least she’s still on the ice.

Darren: She’s still on the ice, still loves it. So I can’t, can’t complain on that. So, but, uh, yeah, no, that’s, that’s kind of, that’s kind of me in a nutshell, but I mean, I’m, I’m. I’m always interested in, uh, you know, uh, interesting applications and, and how to solve, uh, hard problems.

Darren: I mean, I think, you know, for me, my, my personal health or, uh, career wise is to be in a position where it’s just the same thing day in, day out, and it’s just turning the crank. You know, it’s so, uh, a lot of where I’ve come into is, is, you know, being in that incubation state for an organization or helping, uh, grow and, uh, modify.

Darren: An existing business to, to help them get to the growth stage. So, and, um, yeah, so that’s, that’s what excites me about, uh, JVT. I think there’s a ton of opportunity here and, uh, a lot of, a lot of [00:41:00] growth prospects here and, uh, a lot of, uh, great fun and exciting customers here. So it’s a great place here to work.

Ryan: Yeah, you did. Even, even from talking to you, definitely have that, that problem solver engineer mindset. And it’s, you know, honestly, like you really have worked on just about everything out there. Like very, very cool. You’ve gotten to tinker with a lot of cool stuff. Um, very interesting. And, um, I also thought that it was very cool how you were able to kind of like intertwine all of those to talk really generally about how, you know, those processes have evolved and how they’re starting to use data.

Ryan: Um, yeah. You know, talking, talking hockey just a little bit. We, uh, the Bruins had a good game in game one for the, uh, the second round. So, you know, hopefully we see you guys soon. That’d be a good game. Yeah.

Darren: like the 1990, uh, Stanley Cup final here, you know, and, uh, yeah, the, yeah, [00:42:00] it’s kind of wild, but, uh, and then I, you know, obviously I have to deal with all my fellow Canadian, uh, Maple Leafs fans out there that have, you know, continued to suffer, but, you know.

Ryan: Yeah, we, uh, we took care of them.

Darren: Yeah, I did take care of them.

Darren: So I don’t, I I’m not complaining, but,

Ryan: They put up a heck of a fight though.

Darren: yeah, absolutely. Yeah. And, uh, yeah, the, the, uh, the shot off the rear, uh, boards sometimes works out.

Ryan: yeah, absolutely. So obviously you have, you know, a ton of really cool experience and like a brainful of kind of awesome use cases and all that stuff. If, if a listener wants to, to reach out, how, what’s the best way to get in touch with you?

Darren: Um, yeah, I’d say just, uh, shoot me a message on LinkedIn. Uh, add me, I, I, I don’t mind if you got some, you know, crazy idea, you want to run past me, I’m all, I’m all ears and, uh, you know, and whatnot. And, uh, yeah, kind of reach out. I’ve, [00:43:00] I’ve, I get all sorts of, uh, interesting people that, uh, contact me and, and, you know, we have some conversations and, and see where it goes.

Darren: I mean, uh, you never know.

Ryan: Yeah, yeah, absolutely. I love that. Um, Darren, thank you so much for sharing the time and some of your experience and thoughts. It was, um, really, really very cool to get the opportunity to learn a little bit from you about all this stuff. So thank you so much for coming on.

Darren: Yeah. Well, thank you for having me. This is, uh, it’s been an experience and, you know, obviously I Get a break in my, uh, pod podcast, uh, you know, uh, my microphone here in my, uh, office. So, you know, and all my coworkers can stop laughing at me. And although I’m sure they’ll, uh, ridicule me after this, uh, podcast, uh,

Ryan: I don’t think so, man. You sounded pretty smart to me.

Darren: don’t worry. Uh, you know, uh, the one, one thing about me that you, you may not know is like I, I, I like to, uh, bust people’s chops a little bit so they, anytime they get an opportunity to, to get a [00:44:00] jab in at me, they take it.

Ryan: Yeah, absolutely. Well, that’s, that’s, that’s good fun, man. I like that. I also, I also want to make sure that I thank the audience. Thank you guys so much for listening. If you like something, you laughed, uh, you want to tell Darren that he should pick a different hockey team to be a fan of, please make sure to tell somebody else to come listen to this podcast.

Ryan: Give us a light, give us a rating. Hopefully one of the five star ones. Darren, thank you again so much for coming on, man. And this has been another exciting episode of Making Better Decisions. Thanks.

Darren: Thank you.

That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make [00:45:00] better decisions. Thank you so much for listening. We’ll catch you next week.