How Data Infrastructure Supports Sustainable Growth

Charles Link [00:00:00]:
Like oil, it's very important that you have a means to not only obtain it as raw material, but to refine it and then distribute it. Because ultimately what's in the wells has to get to the gas pumps and the gas pumps have to get into the cars. And if you don't have that entire supply chain well managed, you're not going to succeed. Because no matter how advanced that car is, without good gas, it's not going to perform.
Steve Swan [00:00:28]:
Welcome to Biotech Bytes where we chat with folks in the IT industry about their thoughts and feelings around different technology and different trends. Today I have the pleasure of being joined by Charles Link, senior director at Reworld of Data and Analytics. Charles, welcome.
Charles Link [00:00:46]:
Thank you very much Steve. Really appreciate being here.
Steve Swan [00:00:48]:
I love chatting with you, you know, especially in the field that you're in now. You know, like we were just kind of talking about, we talked about in the past, I mean data seems to be more prevalent and analytics and AI, right. And data science more prevalent now than it ever has been. And I don't think that's going away, you know, and you guys are really pushing the envelope on that. So tell me real quickly, tell me a little bit about Charles and tell me how Charles got to where he is and then we can dive in more into the, the data and some of the analytics kind of stuff.
Charles Link [00:01:17]:
Well, sure. I started my career well originally they it was called a marketing analyst. I would have been called a data scientist today but that job didn't exist in 1990. So I just say how far back I've been at this. But I went from being a marketing analyst, I went into finance a little bit to dabble there and got. And that gravitated me more towards IT type projects to automate. And if I were looking at a common theme that ultimately led to today is that it never deviated from how do we use data to get a better outcome. But where the IT piece of it came in was well, if you're going to use data, you have to have a good supply chain of data.
Charles Link [00:02:01]:
So that became a persistent theme throughout my career, regardless of what function I was in. Which then gave me a bit more of a perspective that everybody can use data and analytics insights no matter what you do, for what industry, what company to better your outcomes. It's just fact based decision making. Now with AI we get more automation of that, that they can process more data faster to help you make better decisions. I started out at Dun and Bradstreet and then I went into Merck Medco, the pharmaceutical Business for a little bit. And from there I went over to L'Oreal on the consumer goods side. And from L'Oreal I went up to KPMG for a bot a little bit for the Y2K conversion. And then from there I went over to, I jumped over to, ultimately to the law firm.
Charles Link [00:02:51]:
And I was there for a while and stood up there. HR technology, but again, it was all using data to drive things. We were using that to advertise their attorneys on their website and help people pick out their attorneys and. But then from there went over to Oracle where I was a talent management practice director and from there went into aig, and AIG ultimately led me to here. But each step of the way, AIG is where I formally got into data and really took on that official role. I wound up being the Chief Data Officer for the Consumer Services division. And then when the desire to move me to Houston didn't align with my desire to live there, I jumped over, I jumped over to reworld and it's been an incredible ride because it was Greenfield when I got here. They knew they needed a program.
Charles Link [00:03:48]:
They had very little, just ad hoc power, BI reports, spreadsheets, a couple databases, nothing really cohesive. And we've stood up a very impressive, award winning data fabric over the last four and a half years.
Steve Swan [00:04:02]:
Amazing. That's great. What a journey. Yeah. And I feel like your career has really followed the progression of the importance of data and how we've gotten to where we are today. Like you just said, with AI and everybody trying to automate or stand up AI, it's just, it's incredible. You know, the folks that I talk to on my podcast and how they're talking about, really, I mean, you know, and I've said it a million times in my podcast, I'm about to say it again. You know, data's the engine, I'm sorry, the gasoline that drives that engine, you know, and without the oil of the modern day.
Steve Swan [00:04:35]:
It is, it really is. You know, and, and everybody knows it and everybody's learning it and they're, they're, they're running fast to catch up if they're not there, or they're running fast to stay ahead, you know?
Charles Link [00:04:48]:
Yeah. And like, like oil, it's very important that you have a means to not only obtain it as raw material, but to refine it and then distribute it. Because ultimately what's in the wells has to get to the gas pumps and the gas pumps have to get into the cars and that's. And if you don't have that entire supply chain well managed, you're not going to succeed because no matter how advanced that car is, without good gas, it's not going to perform.
Steve Swan [00:05:17]:
Right. Yeah. And I've had so many folks tell me, I mean, more than more, several, about, you know, the success rate of these AI projects is in the neighborhood of 25% right now. Just because lots of it has to do with that. Right. They get the car before the horse, they, they put the engine together, they put the Ferrari together and then like, wow, we can't get the gas in the tank. Like you just said, our supply chain's not there, we don't have it. And then they're like, oh, now what? Right.
Steve Swan [00:05:43]:
You know, so, yeah, and we, we've.
Charles Link [00:05:46]:
Run into that ourselves. I mean, we, we are well understanding of that need for that good supply chain. So we, with our AI initiatives, we have been careful not to promise delivery until it aligned with the delivery of the information. So having the technology and the capability is, you know, when we say so, we, I would say we have a slightly higher success rate, maybe closer to 50. But that's because we know when the data is going to be available. We built the framework to get it available. Then it's just a matter of getting it into that supply chain.
Steve Swan [00:06:21]:
A lot of the guys that I'm talking to about, you know, from the pharma world are saying we could, you know, get more information out of some of the data that we don't tap or others are saying, you know, like at larger companies are saying, you know, get your data ready, start from the beginning. But, you know, if you're already behind the eight ball and you're a big company, you got a lot of catch up to do, you know, and you really got to work on it. You know, there's so much data you.
Charles Link [00:06:43]:
Do, but you don't have to solve every problem. I mean, you know, if the, I would say even like, let's look at the success of electric vehicles, it's dependent upon those charging stations. But their focus on where they put those charging stations in is where are people most likely to buy and use an electric car. So they're not going to stick those charging stations at some, at some, you know, truck stop in the middle of the country because there's just, you know, you're getting truckers. That's not where your market is. If you focus on solving a particular problem or an answer to the problem with the idea that someday we're going to scale this to be everywhere, then you're going to have enough success to help self feed the investment required as you start to have these successes. And that's been really our secret sauce. Every project we've done is a very specific business initiative.
Charles Link [00:07:39]:
It was how we did it that aligned with the data and analytics strategy that made it. That defined that it was going to be in support of or build on. You would be able to build upon that to deliver future value.
Steve Swan [00:07:52]:
So you knew going into each project that you had the supply chain, the infrastructure to handle that request. And if you couldn't handle that request, let's not go here, guys, because, you know, we would need this and this and we don't have that just yet. Right? Let's go here. We can get this one done. This one not yet. Okay.
Charles Link [00:08:10]:
Yeah, exactly. We knew that and we also, I mean, we defined what our strategy roadmap was up front. That was the first thing I was tasked with doing when we got here. So we knew where we wanted to go. We didn't necessarily know what twists and turns that road would take to get there, but we knew where we wanted to go. And with each initiative building, uh, the, the analogy I like to use is laying down the cobblestones of the road. We get to continually walk on each one of those cobblestones we laid down. And people don't, don't even realize, say, well, how far have we come? And you say, turn around and look behind you.
Charles Link [00:08:47]:
Where were we the previous year? Every year I have my team and my business partners do a review of here's where we were, here's where we are. And they, oh, we didn't even realize we made it that far just because it wasn't, you know, disruptive is the wrong word because everybody associates disruptive with good. It wasn't painful, just things got easier quietly. Kind of like nobody remembers switching from their Blackberries to their iPhones just one day. Everybody was using an iPhone and not a BlackBerry. It just happened. It wasn't a big thing. And that's, that's how we did it.
Charles Link [00:09:21]:
And it's. We've. But the other thing we also did is we made sure that we had a data model. Not a database, but a data model that defined the terms and the domains of data that meant something to re World. And that data model served as the core upon which we could build our enterprise service bus and whatever systems we were using, whether it be through acquisition or anything else, it always got mapped into that model so that we had plug and play capability. So we knew we could handle Just about whatever would come. Because the data model was built for RE World and its business processes and its data domains so that it would be scalable and ready no matter what they threw at us. We didn't necessarily have the, we were able to handle the finance, all the 15 financial systems because we had a central.
Charles Link [00:10:18]:
This is a definition of how finance and accounting works at RE World. Everything else just gets mapped and plugged. So it didn't matter that we, we, we just got to the integrating the financial systems this year, but we had the framework to handle it years ago.
Steve Swan [00:10:33]:
Well, it really is, I mean everything I'm hearing is, everything I hear about, you know, because I handle a lot of different things in it. Everything I'm hearing from you is, is you took care of the blocking and tackling, you took care of the fundamentals so tightly that you knew exactly what you could do and where you could go and where you couldn't go. Which. Yeah. Which meant that this, this level was already built up. It was here. Now we can pick and choose our spots, you know, and a lot of people in IT talk about that. But do they have this foundation really defined? I guess not.
Charles Link [00:11:05]:
I don't see it a lot. I, we learned our, I learned my lesson on how to do this this way the hard way when the first pass at going in. At aig, the data program was very bureaucratic and it was meant to meet government compliance requirements for CCAR reporting and all of that after the collapse. But after those pressures started relaxing, you know, we would have our regular governance meetings and we would talk about our data management maturity scores and things of this nature. And one of the senior executives from the consumer division came to me and said look, it's great that we're making this progress. It's great that we have, you know, this information available to us. But frankly, nothing in my life or my business has gotten any better. So what's the value of this program? And I had to do, because I was new to formal data governance and all of that back in 2013 and I did some thinking, I'm like wait a minute, we have to do this a little bit differently.
Charles Link [00:12:05]:
Our data strategy needs to be around two functions. One is data management strategy and one's data use strategy. The data use strategy is what are the businesses goals, what do they need to know and what levers do they throw to get there? How do they measure that success? And that's going to drive your initiatives priorities. And those things are usually not data projects. You oftentimes are a subcontractor that feeds system implementations, integrations, things like that. But that piece of it that, that we would be a subcontractor to often was managed by our data management strategy. Said these are the tools, the techniques, the methods. These are the capabilities we're going to bring to the table.
Charles Link [00:12:46]:
And the other analogy I like to use is we had a nice box of Legos. And when we understand what the business problem is, what do they need us to build? We have different colors, different shapes. We could snap them together. You want a purple flying dragon? We'd build that out of Legos. If you, you know, if you just wanted a basic house, you build that out of Legos. And we would snap these things together in a way that they were reusable and scalable. But it did require that we laid out that strategy upfront. And the business use strategy would keep evolving every year.
Charles Link [00:13:18]:
Regime changes, objective changes, whatever. But your data management strategy was the consistent. These are the standards you will execute to. These are the tools you were used so that you don't spend your time constantly playing with the technology. You need to the technology not play.
Steve Swan [00:13:35]:
With it now through the regime changes and some of the strategy changes. Like you said, you were scalable and reusable. So, so what you were doing wasn't fundamentally your, your direction and how you would do your thing wouldn't and couldn't really fundamentally change because you're not going to pivot that fast. Right. And so you made sure that it wouldn't.
Charles Link [00:13:54]:
Yeah. So you have to make sure that you've got layers of abstraction in what you build and what you architect. And you have to basically understand what are the core capabilities. I mean, people, they. Here's another analogy people can do. Many people have many different skills. Some are bodybuilders, some are computer software engineers. But at the end of the day, it's different amounts of food, water, housing.
Charles Link [00:14:22]:
There's some basic things that all of them need to do, what they do. And if you have those components, you mix and match the right ones to get them to where they need to be. And the different regime changes, the different executives, the different needs were all pretty much the same. And at the end of the day they may. And these are the twists and turns in that road. I told you, at the end of the day, you know, re world is about sustainability. And what we are trying to do for our customers has always been the same. So having that in mind, that creates that common data model, generally speaking, you may go a little bit one way or a little bit the other.
Charles Link [00:15:04]:
But at the end of the day, if we make our customers more sustainable, make it so that their waste doesn't wind up in landfills, that it's handled cleanly and that we can make reusable materials out of a lot of it, then we. You're pretty much thinking ahead anyway. It's just which twists and turns in the road you go, and that's really not disruptive.
Steve Swan [00:15:27]:
So what do you think? I mean, because you know, this industry, the data industry and the analytics industry is evolving, right? And it's changing a lot and it's really evolved. It's really picked up steam over the last couple years or well, in the media it has anyway. Right. I mean, you've been doing this stuff forever, but what do you think that the, the, the world or the industry in general has gotten right? But then what have we gotten wrong? You know, how can we make this better? How can we make this easier for all of us?
Charles Link [00:15:56]:
Well, I think that, I think that AI is, is right to be respected and I guess worthy of the hype. But I think the hype may be misunderstood and that how it applies and what it means to people is maybe over promised and under delivered. There are many great capabilities, but they are not truly unified to helping people get everything done. So you get things like OpenAI with ChatGPT, awesome tools, right. But at the end of the day, that tool doesn't basically reach out and automatically make recommendations to you to make better financial decisions. It's a capability that one day could contribute to that. And I think we've got a long way to go. And people need to realize we have a long way to go.
Charles Link [00:16:51]:
But we also, as we go down that road, I'll quote the Jurassic park line, we were so focused on whether we could, we didn't think about whether we should. And we have to be very careful about how we apply those capabilities and not turn them loose without any sort of controls or governance. You know, that what we will, we build what we're building here. I've made sure that we always have visibility, but also a kill switch that if something goes wrong, you shut that down fast. And we're also taking an evolutionary approach and I think everybody should, that we look at AI capabilities as an evolution. And it's starting off very basic. I would say right now they're probably, it's probably at the level of a, of a good house pet, but ultimately it's going to become a, an intern and then an apprentice and then a trusted partner and then possibly just Left alone to do its thing while people do other things. And that's the journey that I think people need to be ready to accept.
Charles Link [00:17:57]:
But again it's going to take a lot of training because the, you know, a machine is not going to have a model of right and wrong. It's not going to have an understanding and the perception of right and wrong and what's good and bad and what's ideal is going to depend on the person or people it's working with. One company's objectives are not the same as another company's objectives. They don't necessarily conflict, but they're totally different.
Steve Swan [00:18:22]:
The first thing my mind jumped to as you were just saying that is picking a stock, right? You're going to have it. You may look for a dividend paying stock, I may look for a super growth stock because we're at different points, we got different situation, you know, and it's as simple as that, you know, it really is.
Charles Link [00:18:36]:
Or it may be just a matter of whether I feel this is a good ethical company and it's a feel good stock. It's, I'm not looking to make money. I'm looking to invest in the future for my children and I may never see a payday. These things are, you know, they're, everybody has a different perspective and the, and using AI is going to have to have a good understanding of that perspective.
Steve Swan [00:18:59]:
Right. And all these decisions that we make, you and me, have many more inputs than the 2, 5, 7, 100 that you're, you're, you know, you're talking about AI is going to make. I mean maybe I got a flat tire on the way to work today. Maybe it's raining, you know, maybe, you know, I just slam my finger in the car door, whatever. Those are all going to affect my decision make. A computer can't take that into account.
Charles Link [00:19:26]:
No, that's, it's not going to take that into account.
Steve Swan [00:19:28]:
All this stuff comes in there so it's, yeah, it's the ability to synthesize, right.
Charles Link [00:19:33]:
But that they, it's not that that time's never going to come. I think, you know, when you look forward, the thing that excites me, I'm not going to say scared but just because I don't quite understand how big this is going to be. Quantum computing is going to be a total game changer because it's not binary yes and no anymore. It's states of in between which then starts to truly emulate what humans will do. You may see, I mean you can say come to decision. But it operates in the gray area and at incredible speeds, let's say. Okay, well how does this, how do we make sure this doesn't hurt anybody? Because you know, the, we've seen with all kinds of AI models you have to watch out because bias does emerge and we've seen some odd things come out that says, wait a minute, how did it come to that?
Steve Swan [00:20:31]:
Yeah, so the hallucination and stuff. Right. I used to. Or I do. I know somebody who worked in the automated driving team at Tesla.
Charles Link [00:20:40]:
Oh yeah.
Steve Swan [00:20:40]:
And his big issue with it was that like, for example, he said my car could be driving down the road and if the sun hits a stop sign wrong, it could interpret that as a bowl of guacamole. Right. And blow right through the stop sign. And he's like, that's bad. He's real excited today because what he's now involved with, which apparently is the newest thing getting closer to allow these algorithms to reason. Reason not really is proven out the theorems mathematically. So they're using high school algebra and calculus right now to go mathematically through these things so that the decisions aren't as linear. You know, they're a little more flu now that it's not going to get there.
Steve Swan [00:21:17]:
But you know what I mean, it's, it's getting them a little more flexible.
Charles Link [00:21:20]:
Yeah, I've seen these calculus models and it's, it's interesting because they try to come to the right decision and what they, it's, if you see it visually, it makes more sense. But if you picture a three dimensional, almost like a valley coming down, and your decision is at the lowest point in the valley, there's going to be several ups and downs on the wall of the valley that will get you there. And you have to teach it to say, is there more to this than the first dip I found? Do I stop there? And it has to keep processing. So when I look at the AI entities we're trying to build here, you know, I like to call it a bit of curiosity. We're trying to build a curiosity engine into it so that it will go beyond. Well, I got the right answer. Maybe reason a little bit more. Just make sure you got the right answer.
Charles Link [00:22:10]:
Ask, do I have the right answer?
Steve Swan [00:22:12]:
Are we sure that's what we got to do? You know, and what I've been told is that if we, if we, when we get to the fully reasoning, you know, when something, when the AI model can reason, that's, that's going to be a complete paradigm shift from A computing perspective, right?
Charles Link [00:22:31]:
Yeah.
Steve Swan [00:22:32]:
So that's neural networks and that's nowhere near where we are now. Right. With, with getting these things to think.
Charles Link [00:22:37]:
Once quantum computing comes, you'll see it, it'll, it'll happen overnight. Once quantum computing becomes a, a cost effective thing that people can get access to, you'll see that happen. I mean we're even some of the stuff we have now, it's, it's interesting because you know, we talk, I say hallucination, I say it's a little bit of bias. I had, I had our AI read through all of our documents for executive status reports and I started asking some pointed questions and I can't go into the details of them, but it actually had an opinion that may not have resonated. Well, if one of our executives added, asked that, asked that same question, it actually came back. And I'm thinking there's nothing in these documents to really say it doesn't say that. But it basically interpreted that from the combination of the knowledge and I like okay, well we're not going to turn this loose on the general populace because whether you agree with what it came to or not, it's, you know, we have to be mindful of career survival.
Steve Swan [00:23:42]:
Well, yeah, that too. Right. That comes into play. That's going to feed into every decision, right?
Charles Link [00:23:48]:
Yeah.
Steve Swan [00:23:48]:
So then thinking through this whole thing back to your point, it resonates more and it hits more on that data. If you own that data coming into this, you're king. You're king.
Charles Link [00:24:02]:
Yeah. Well, it's just like for the longest time the oil companies dominated, dominated the US economy and we're seeing that shift to tech companies because data is now the modern day oil and that's what's driving the next, I guess I don't know how to call it, is an industrial revolution. We're seeing that. You saw the industrial revolution, you saw robotics. Now you're seeing information and AI, but without that supply, that AI is meaningless. It's, it's absolutely meaningless. That's, it's just, you can't, you need the fuel to do the job.
Steve Swan [00:24:44]:
And I think back to some of the, you know, the data pioneers, right. You were involved at the beginning. I mean Bloomberg, right. He's oh, that's his whole thing, you know, I mean there's several companies that were just, you know, data kings and they just, you know, now you can look at, I don't know, Dow Jones maybe they dropped the ball a little bit. Right.
Charles Link [00:25:05]:
Done in Bradstreet. I was there in the early days. And I was really surprised because they did have some people in there who were really into the data, what the market value of the data would be. And not just that, but I don't see with the right mindset why D and B wouldn't have beat Amazon to the market for services. They had those capabilities back in the early 90s and they just. That just wasn't where their focus was. Sears was an amazing technological shop and they're gone. I think if you don't think forward and think beyond what is.
Charles Link [00:25:45]:
What is my specific business, think a little bit beyond your walls and your business model. I think you're at risk of finding yourself on the wrong side. Yeah, you won't be relevant anymore.
Steve Swan [00:25:58]:
I saw a video of, in the early days, a real young Jeff Bezos, right. And he. I think he was a investment banking analyst or something at Goldman. I forget where he was, but that. In something along those lines, right. And they interviewed him and he said, I'm starting. You know, they were talking to him, maybe it was the beginning of his business plan or something. But he said, I'm going to start a company because I see this Internet thing, it's growing 2400 a year, so whatever I do is going to be involved with the Internet.
Steve Swan [00:26:27]:
And who doesn't read? I'm going to combine those two, books and Internet and see where it goes. And that's, you know, now we're talking about Amazon, right.
Charles Link [00:26:35]:
And he went beyond books into other markets of things he could sell. But beyond that, now he's even taken that technology and made it a product in and of itself. So that is a perfect example of a traditional bookseller because you could buy books online from Barnes and Noble or.
Steve Swan [00:26:55]:
And everybody else, Right?
Charles Link [00:26:57]:
Yeah. I remember seeing their original website. It's. It was almost comical because it was so crude. But, yeah, it worked.
Steve Swan [00:27:05]:
I remember reading for the first time that they were actually going to sell compute power. And I'm like, I. I think that's genius. I'm not quite sure, you know, and there it is, you know, the cloud, right?
Charles Link [00:27:17]:
Yeah, well, and it's, it's. No, you know, it's really not. You know, the more things change, the more they say the same. It's really just data centers. They're just leasing them to you. And heck, AIG used to lease its data center space out too, but they just didn't market it right. So did they miss the boat there? Because AIG had massive compute and data centers, as did D and B. They just kind of missed that boat of saying, well, how do we make it easy to do business with.
Charles Link [00:27:43]:
How else can we make money with this thing?
Steve Swan [00:27:45]:
Yeah, they didn't see it. Right. Yeah, they're to your point. Like you said three minutes ago, you know, you have your four walls. If you're not thinking slightly outside those four walls, you're going to get run over. And they, they were thinking about, or their insurance or whatever they were thinking about their financial, they weren't thinking about, hey, we own this too, we can monetize that as well.
Charles Link [00:28:03]:
Right, Exactly. And you know, I, I look at, for our business here at RE World, you know, there's, with the data that we have access to and the data we're creating, you know, I sit there and think, well, wait. The ability to promote a company as a green, sustainable company requires information. And you may not have the resources to track the impacts of what you're doing yourself, but we do. And that is something where we've talked about how do we monetize this? Although sometimes it just becomes a de facto it's expected for doing business, but nonetheless that just makes customers stickier. But even like we do, you know, it's funny we say biotech reworld does a lot of work with biotech and pharma because they need assured destruction of pharmaceuticals and they need that transparency and the whole chain of chain of custody, that's all information. And that's something that's very valuable. Now whether they buy the chain of custody, no, but they do buy our services because we can prove it with information.
Charles Link [00:29:11]:
So if we didn't have information capabilities to do these things, we would not be able to stay in business. It's not like anybody with an incinerator can just say, hey, you know, just bring us your stuff, we'll get rid of it. It doesn't work like that. You need to have more.
Steve Swan [00:29:26]:
Yeah, yeah, absolutely. No, that's great. Now I want to pivot just a little bit. You know, I like when as I'm talking to folks, I mean, I know why I'd want to work for you, but why would you say, you know, if you, if, if, if, if you were talking to somebody that didn't know you, Right. What's unique about working for, for Charles and, or Charles's group? You know, why, why would Steve Swan say, hey, I'm a data scientist or I'm a data engineer or, or what have you? Why would I want to be there? Why, why do I like, I mean, I know again, I'm excited about what you do. This is, let's not talk about mint.
Charles Link [00:29:59]:
We do the coolest projects, even though we're not the biggest and, you know, best funded operation out there. But we do do the coolest projects and we don't ask anybody to pigeonhole. We actually expect you not to be only doing one thing. We look for you to grow and be more than what you came to us with. We want to utilize your complete set of skills and don't nothing gets nothing's routine around here. The projects are fun, they're fast paced, but I would say we've also managed a very good work life balance. I think that's what really makes people want to be here and stay here because I have some very talented people working for me. And I actually asked one of them because he could be anywhere he wanted to be sure.
Charles Link [00:30:48]:
And I said, what is it that you're here and not at MIT or something?
Steve Swan [00:30:54]:
Yeah, yeah, yeah.
Charles Link [00:30:55]:
And he said, he says, I have complete control of my work life balance and it's close to my home. It's a lot of camaraderie. There's no, you know, there's none of this formal you have to go through so and so to talk to so and so. It's, everybody is in this with a vision to do the right thing for the environment. And even when we don't all agree on how we're going to do it, that's really what brings people here.
Steve Swan [00:31:26]:
Yeah. And I mean, differing opinions are good. Right. Because you see a pitch from a different angle, someone sees a pitch from another angle, you're all going to get together and figure it out again, you may not have to agree, but it's going to create better conversations and a better angle on each problem.
Charles Link [00:31:43]:
Yeah. To quote that one song, we find beauty in the dissonance.
Steve Swan [00:31:47]:
Yeah, absolutely, 100%. How big is your team? How many folks are on your team?
Charles Link [00:31:52]:
Well, if for full time employees, I have seven, including myself.
Steve Swan [00:31:56]:
There you go.
Charles Link [00:31:57]:
And then we have a lot of projects that are going on all the time, so. And with the production support being outsourced, we're at about 30 right now.
Steve Swan [00:32:05]:
Okay.
Charles Link [00:32:06]:
But that we have some massive projects going on in quieter times, you know, we're, we're well under half that. But this has been a, every year has been a bigger year than the last. I always tell my team, brace yourself because this year, next year is going to make this year look like a cakewalk. And they've come to actually believe that because every year, because we've been successful, the projects are bigger More involved and just at a faster pace.
Steve Swan [00:32:37]:
What you're saying and what you're doing is a conversation that I tend to try and have. All right, I do have with hiring managers all the time, but you're living it. And by that I mean, you know, when I talk to a hiring manager, right. I. I was just on a. On a. On a zoom with a bunch of folks that had been displaced, and we were talking about some things, and one of them asked me about purple unicorn or skills that people look for. I said, nowadays in the market, a lot of managers look for folks with, you know, 11 out of 10 skills.
Steve Swan [00:33:07]:
And my comment back to them is, you know, I used to coach kids teams. The reason why they come back to you is they're happy, they had fun, and they're learning something. If I find somebody who knows 11 out of 10 skills, right, they're not learning anything. They've been there and done that. That's not for a lot of longevity, creativity, a whole bunch of things. Everything you decided says to me, these people are doing new, fun, cool, and exciting stuff, you know, all day, every day. You know?
Charles Link [00:33:33]:
Yes. And we, because we don't.
Steve Swan [00:33:34]:
We're.
Charles Link [00:33:35]:
We're inventing some of the stuff we build. I mean, we use common tools, but the things we've put together to create this data fabric, a lot of it, our vendors are like, oh, I didn't know you could do that with our product. And we're very proud of that. But the other thing I think that we also look for is I look for aptitude. Could you. Because I read once, and I believe they probably do this, that the big investment banks, they look for smart people and let them figure out where they can add value. And I thought that's a really smart thing because I've done a lot of programming work, but I've never been a programmer. And at the end of the day, if you have the right mindset, you want to create something, it's really just all about your aptitude.
Charles Link [00:34:26]:
Do you want to do this? Do you think along the right lines? Rest is syntax.
Steve Swan [00:34:32]:
It is, it is. And really, have you been had again? You talked about a business thinking outside their four walls. An individual slightly thinking outside their four walls pushes them right to one of those things. And do they? You're pushing your own aptitude, right? You're pushing to the next level, you know?
Charles Link [00:34:49]:
Yeah. And always, just, always just saying, what. What's next? What. What might I be interested in? I mean, the tools and the capabilities that we're working with. Today certainly didn't exist when I started down this road in 1990. I mean, it just, you know, none of these things exist. No Internet, no nada.
Steve Swan [00:35:08]:
Right, right, right. Yeah. Yeah. We are in the Wild west right now again, right?
Charles Link [00:35:12]:
Yeah. And it's just. It's. It's exciting, it's fun, and, you know, it's. I really like what I do for a living.
Steve Swan [00:35:21]:
I can tell. This is great. And again, I get excited talking to you and talking about this stuff. I just do. So anyway, I usually have one final question that I ask folks, but before I get to that. Anything else you want to add? Before I get to that last question that I asked pretty much everybody, I.
Charles Link [00:35:40]:
Think the one thing I would add is the one piece I missed. Another reason why people like to work here is because they really believe in what we're doing. We have different lines of business here at reworld that ultimately seek to make sure that waste gets back into a circular economy, that it gets reused, turned into energy, turned into road bedding. We make sure that as little as possible goes to landfills. One of the things I like to tell people, one of the facts is we recycle enough aluminum out of the waste stream. This is the stuff that doesn't go to recycling. This is stuff that's in your garbage to make 2 billion soda cans a year. 2 billion.
Charles Link [00:36:24]:
It's enough to go to the moon. Yeah, right. You know, or the. You know, we can. We can build. I believe it is. It's either three or five Golden Gate Bridges out of the steel. We recover out of the garbage.
Charles Link [00:36:37]:
This is after all the recycle work that's been done. But the most fun one, I will say is we have a facility that processes the ash after the incineration. And that facility sorts through the ash and recovers depositable coins. We recover over $100,000 a month in depositable coins. Never mind the ones that get destroyed. Depositable coins. And I went to visit the facility once, and the gentleman was doing the tour. He says, you wouldn't believe what comes out of here.
Charles Link [00:37:11]:
I'm like, this is weird. It's because it's people's garbage. He reached into the ashtray. He pulled out what looked like a diamond ring.
Steve Swan [00:37:18]:
Wow.
Charles Link [00:37:18]:
It was a gold ring with a star. I don't know. Was diamond. I didn't certify. But he's like, he literally couldn't have been staged. He just reached in, says, we find these things all the time.
Steve Swan [00:37:27]:
Wow. Wow. Crazy. So then how does. How does all this Because I read an article about waste management not long ago getting nailed for not recycling. They're only doing 1% of what they said they were doing. Where does that all fit into what we're talking about here?
Charles Link [00:37:43]:
Well, it depends on what you're doing. We. We aren't a recycling company per se. We don't. We don't run recycling centers, but we do. We do try to market the outcomes of the waste management process. So we have a. We have a business that will take old tires shredded and use it for powering cement kilns because they get energy credits for using something renewable instead of fossil fuels.
Charles Link [00:38:09]:
Because those tires had to go somewhere and they'll use.
Steve Swan [00:38:12]:
Tires are all petroleum anyway. So if you get paid for that, do it.
Charles Link [00:38:15]:
It's very high BTUs, but. Or, you know, like I mentioned, we recover a lot of the metals, but even the ash and the aggregate that comes out, it gets you sold for road bedding and things like that. So basically it cuts down what goes to the landfill by about 90%, which is massive. Especially when you think. Yeah. I mean, if you think back when we were younger, think about how, you know, the garbage from New York City used to wash up on the Jersey shore. I remember that in the 80s. That doesn't happen anymore.
Charles Link [00:38:44]:
We're handling their garbage. It doesn't go there.
Steve Swan [00:38:46]:
I was telling my father, when we were kids, we used to go. We lived in Bridgewater. I'm taking a sidetrack here. But, yeah, we used to go. We used to go to. We used to go to the Delaware river and he had a little ski boat and we would ski on it as kids.
Charles Link [00:39:00]:
Oh, fun.
Steve Swan [00:39:00]:
I'll tell you that. That Delaware river, you couldn't put your foot this far in and lose it. You just couldn't see it. Well, I was there for lunch, I don't know, a month ago. I swear I was watching these ducks swim. I probably could have walked into the river up to here and I still would have been able to see my feet. So they cleaned it up a lot, you know, and probably help with. From companies like yours.
Charles Link [00:39:19]:
Yeah, it's exactly that. And then we help the companies manage, you know, because everybody's got stuff left over from their manufacturing processes. They need their wastewater treated. They may have oil, they need disposed of. We basically take care of that in an ecological, environmentally friendly way and give them that certificate that says, look, this is how it was handled. And you can basically take that to the bank when the regulators come around and say, are you. Are you Being environmentally conscious? Are you being green? And you can actually say, yes, I am, because a lot of companies have made false claims about being green. And I see it a lot and it's a shame.
Charles Link [00:39:58]:
But you need to be able to back that up with real information, real science and data. So we come full circle that you have to have that data.
Steve Swan [00:40:07]:
Yeah, it comes all the way back to the data. Yeah, that's cool. Nice. Well, thank you. So my. My one last question I asked of all my guests. Okay. Well, I don't know if you got the.
Steve Swan [00:40:19]:
Had the patience to go all the way to end of one of my other podcasts. Lots of folks don't. That's why I hit him with this question at the end. So I like live music. I like going to see bands, I like seeing live acts and stuff like that. So what I always like asking my folks is, you know, if you've ever seen a concert, which most people have, what would you say is your favorite live band you've ever seen?
Charles Link [00:40:41]:
I would say my favorite. It is a tough one, but I think my favorite live band I ever saw was I saw Rush at the Meadowlands when I was 17 years old and just got my license.
Steve Swan [00:40:56]:
Second person. That's giving me Rush. Yes.
Charles Link [00:40:59]:
Yeah. I had never. I. You couldn't see Neil Peart. He was so surrounded by drums.
Steve Swan [00:41:05]:
Yeah, I know.
Charles Link [00:41:06]:
And it was just, it was just. It was amazing. I mean, part of it was because it was, you know, first experience as a 17 year old. My parents. Yeah, go ahead, drive your friends to the concert. And it was. And we were on the floor in those metal folding chairs. I don't think they let you do that anymore.
Charles Link [00:41:24]:
But it was just, it was. They were. I've always enjoyed their music. They're not my favorite band, but they just put on an incredible show and.
Steve Swan [00:41:34]:
I'm really bummed I didn't see them when Neil Perret was. Was alive.
Charles Link [00:41:37]:
Yeah, yeah, yeah. But that's. That's probably. Was probably my favorite show ever. I've seen some other good ones, but that's the one that, when I think of what was my favorite concert, that's definitely it.
Steve Swan [00:41:47]:
Okay. And that was msg, you said, right?
Charles Link [00:41:49]:
Yeah, no, not msg. It was Meadowlands.
Steve Swan [00:41:52]:
Oh, Meadowlands. Okay. Brendan Byrne or something.
Charles Link [00:41:55]:
I think it was Brendan Byrne at the time. It might be MetLife now. I don't remember.
Steve Swan [00:41:59]:
Yeah, it's changed names. That's cool. Yeah. No, my brothers, my three brothers have all seen Rush. I didn't ever see them, which I'm kind of bummed out. I've seen a lot of live shows.
Charles Link [00:42:08]:
But yeah, yeah, I only saw them once, but it's. That's the one that sticks with me.
Steve Swan [00:42:12]:
Very cool. One other person did that. I got some Bruce Springsteen and things like that.
Charles Link [00:42:16]:
But yeah, Rush, I've seen him. He's. He's great. But it was just the, the show was just. And I think part of it is because I was 17. It was of. It was your first time.
Steve Swan [00:42:24]:
Of course. Yeah, of course. That's awesome. Cool. Well, thank you very much. I really appreciate it. I love this conversation. I don't know, whenever I start talking about data, I get all jazzed up, so I apologize if I got a little excited on you.
Charles Link [00:42:38]:
It's great. I love when people share my passion for it.
Steve Swan [00:42:43]:
Yeah. Well, thank you.