Matt Cowell didn't start out in tech. In fact, he studied the chemical world heavily in school, getting his Bachelors in Chemistry and Masters in Chemical Engineering. So - by trade, he was clearly a chemist. However, when he joined Accenture, he started in with programming and establishing the SDLC methodology for the company. Matt is married with 2 kids, loves sports - specifically Illinois basketball - loves to play music and golf. He likes to travel with his wife to see family, and make frequent visits to their lakehouse.In his professional past, Matt had held several roles in SaaS companies and startups. He met a company that was an artificial intelligence consultancy, which had a POC around assessments. They wanted to start up a separate company to support launch this POC and take it to market. This is when Matt got involved.This is the creation story of QuantHub.LinksWebsite: https://quanthub.com/LinkedIn: https://www.linkedin.com/in/mattcowell/Our Sponsors:* Check out Vanta and use my code CODESTORY for a great deal: https://www.vanta.comSupport this podcast at — https://redcircle.com/code-story/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
We're trying to teach people data skills. A lot of people don't even realize that they don't realize even what data is or are, if you like the plural form of data. And so we're trying to make sure that people understand that they need data skills if they're in an entry-level HR role or sales or marketing. It's not just for data professionals or technical professionals.
And so one quote that I really loved from a recent customer discussion was they thought data was just numbers. before they got into QuantHub. And then they started realizing that there's some concepts in there. It's not just about numbers. My name is Matt Cowell. I'm the CEO at QuantHub.
This is Code Story, the podcast bringing you interviews with tech visionaries who share in the critical moments of what it takes to change an industry and build and lead a team that has your back. I'm your host, Noah Laphart. And today, how Matt Cowell created a necessary platform to assess data skills and upskill your workforce. All this and more on CodeStory.
Matt Cowell didn't start out in tech. In fact, he studied the chemical world heavily in school, getting his bachelor's in chemistry and master's in chemical engineering. So, by trade, he was clearly a chemist. However, when he joined Accenture, he started in with programming and established the SDLC methodology for the company.
Matt's married with two kids, loves sports, specifically Illinois basketball, and loves to play music and golf. He likes to travel with his wife to see family and make frequent visits to their lake house. In his professional past, Matt held several roles in SaaS companies and startups.
Eventually, he met a company that was an artificial intelligence consultancy, which had a POC around skill assessments. They wanted to start up a separate company to support and launch this POC. This is when Matt got involved. This is the creation story of QuantHub.
What Hub is a data skill platform. And so we really have two lines of business. One is we have an assessment platform of data skills. And so think data scientists, data engineers. So if you're hiring a data scientist at a large company, you're hiring probably hundreds of data scientists. It's a little bit hard to tell people apart and know who has the skills to do the job and who doesn't.
And so we have an assessment platform to do just that. And so we have companies all over the world using us for that. And then the second part of our business is in upskilling of data skills. And so helping organizations build really data literacy across the entire enterprise.
So not just data science and data, true data analytics skills, but just helping salespeople, marketing, HR operations be more effective at using data and being more data driven. And so there's a big skill gap globally in and around data, but data is everywhere. So it's no longer optional to have data skills. And that's really where we come in and fit in helping organizations build those skills.
My background is in product and tech and SaaS companies with CTO-like roles, managing both the product and the tech and engineering side, and met a company. It was an AI consultancy. We're out of Birmingham, Alabama. Mostly remote, but a few of us are out of Birmingham now. And the AI consultancy had sort of a proof of concept, if you will, that was the beginnings of our assessment platform.
And they wanted to spin it out as a separate company, raise money. and sort of have its own future. And that's really how QuantHub got its start. I came on to run it as a separate company. It was sort of day one for the company. We didn't have any customers. We had kind of a proof of concept for the product. Didn't have any marketing. It was kind of early, early days.
But that's how we got our start. They were doing this internally to help in their own hiring. Kind of realized that hiring is difficult. You spend a lot of time. It's relatively error prone in this field. And so they started tuning their hiring process by using assessments. And then that's what we took on as a business.
Tell me about the MVP. So tell me about that first product you built. How long did it take you to build and what sort of tools did you and your team use to bring it to life?
The initial product was PHP Laravel. In terms of how long it took, when I got here, we had sort of a fledgling product. So our product is two things. It's software, of course, but then it's also content. So in particular, it's assessment content. So we have to write assessment questions across a platform.
range of technologies that do a good job of determining whether a person knows that skill or not. And so there are really sort of two aspects for us building the software and then actually generating all the content, making sure the content is good, going through QA review, making sure that it's not plagiarized and it's not just literally pulled off the internet and
And so from a product perspective, we had kind of the beginnings of a product when I first started on sort of day one. It probably took us about three or four months to get something that we were ready for a customer to use. And then in that same amount of time, we were building that content library.
So probably about three or four months before we were doing some beta type work with an early adopter.
So with any MVP, you got to make certain decisions and trade-offs about what you're going to cut in the short term or what you're going to include in the short term and what sort of technical debt you're going to take on or even approach debt. If that's a term, I may have just made that up. But tell me about some of those decisions and trade-offs that you made and how you coped with them.
There are several features, I would say, that people talk about a lot in our market. And, you know, just frankly, we had to do without those early on. So an example of that would be, you know, we were testing or we are assessing coding skills, but also more kind of foundational skills like statistics and mathematics and machine learning algorithms and, you know, these types of things.
And so early on, We had to make a decision whether we wanted to spend a lot of time and actually allow people to build, in essence, a code editor where we could assess coding skills in the browser. And It didn't make sense to spend a lot of time doing that until we were starting to get more feedback from customers. We had people using the product and testing statistics and other things like that.
So that was something that we held on, for example, and ended up building that later. before we had already had a few customers. And so instead, we were doing more kind of multiple choice coding questions and things where we could still assess coding skills, but not necessarily in, let's say, the most sophisticated way. And so that was an example.
There are plenty of other examples where we wanted to do video interviewing and sort of online proctoring where you can actually see the person taking the assessment. And that's another example where that came up some with customers, Not enough to where it made sense for us to put that in the kind of early stages of the product.
And so those decisions, I mean, while I would like to say that there's some magical process that we went through to come up with those decisions. For me, it really comes down to listening to customers and really asking a lot of questions to help understand what it is that they really need. Customers will ask for everything.
But at the end of the day, it's really up to me and others on the team to figure out, okay, but what do they really need? What can they live with? What gets them the value that they need out of the solution with the minimal amount of development time? So what's the least product that we can give them that delivers on the value proposition? And so that's really the mindset.
And so it's, I wouldn't say there's, There's a lot of process around that other than asking a lot of questions, you know, making sure we really understand what it is that gets them the value they need and, you know, what features they need to deliver on that value.
So from that point, you've got your MVP built. How did you progress it? How did you mature it? And to give kind of context to where I'm going with that question, how did you build your roadmap and decide, okay, now this is the next most important thing to build?
Not sure who's listening to this, so hopefully prospective customers aren't listening too much. As any early-stage company, we talk about a lot of things that we're doing. Quote-unquote doing, if you will. You can't see my fingers, but quote-unquote doing. And it's usually... The honest truth is I'm pretty close to the product and I'm very involved in sales.
And so I'm sort of a bridge in our team. As the CEO, I'm a bridge in our team across those two areas. So I know where we're going strategically. I know the types of things that we're planning to build. And I know where our priorities are currently.
But, you know, if we're talking to, you know, a massive, massive company and it's a huge opportunity, then, you know, I know where I can I can sort of talk about futures a little bit. And because I know that if this hits and and, you know, chances are a massive company probably won't. It's going to take it's not going to happen tomorrow.
So we're going to go through procurement and then it's going to take them a little while to get their ducks in a row to actually launch. And so we're talking months out. And so from a road mapping perspective, yeah. you know, we've, we talked to customers, we know what's out there in the market.
We, um, we look at on our specifically on our upskilling product, we look a lot at the learning science to determine what is actually the best way to learn. It's, it's not generally how we've learned in the past. I will say, um, you know, the kind of traditional approach to learning is pretty ineffective.
And so we look a lot at that and, and, you know, we, we formulate our approach to it, but then from a prioritization and roadmap standpoint, You know, it's usually a combination of looking at what current customers are asking for, what they need, and then, you know, balancing that with prospective customers and and quite honestly, who I think is going to who I think is going to bite first.
Well, let's switch to team. So how did you go about building your team and what did you look for in those people to indicate that they were the winning horses to join you?
Yeah, for me, I look for smart, passionate, low ego people. And it's maybe as simple as that. At a startup, we're not a huge team. We need people that are adaptable to doing lots of different things. And that usually comes with people being low ego people. You know, so not super concerned about, well, that's below what I should be doing at this point in my career.
I mean, for me, I do, you know, sort of technical support sometimes. I answer support questions and we need people that are willing to roll up their sleeves like that. And so that's a big deal, particularly for a company at our stage. That's a huge deal. But then also people that are passionate, right? and this is not just a quantum thing.
I've always felt this way that, you know, it's, it's, you got to find people that you asked about hobbies earlier. Quite honestly, one of my hobbies is this. I love doing this and, you know, I love building companies. I love building solutions. I love solving customer problems. And it's like, it's one of my favorite things to do period. And, um,
And so I tend to want to surround myself with people that are like that, that are constantly looking at this as a hobby. And it doesn't mean they don't have work-life balance, but it's just this is what they love to do. They love to talk to customers. They love to see new ways that we can solve age-old problems. And usually that comes from people that you can sense a passion for.
about them and then you know the smart side is is people that can pick up on things quickly they can think on their feet they can understand different circumstances um you know we're in a relatively technical area with at least part of our product and so being able to pick up on that quickly even though that's not their background and so those three things you know to me are are really important traits
Let's flip to scalability. Did you build this to scale efficiently from day one, or were you fighting this as you grow?
I assume that's a joke, right? I can't tell sarcasm in your voice, but the answer to that is obviously no. I assume everyone that's on this podcast would answer that similarly, but no, heck no, it wasn't built to scale efficiently on day one. What was interesting for us, and a lot of companies, when they start early on, they can sort of dabble in it before they have to scale.
Well, for us, that was not the case, which was good and bad. The good is that we were in with a large company early on that was doing a ton of hiring. It's one of the largest management consulting companies in the world. The bad is that we found ourselves, remember I said three to four months to sort of build the MVP. Well, about three months after that,
They were doing campus recruiting, and they were actually sending out, I think it was 3,600 assessments on our platform. And this was for campus across the entire country, and those people were to complete those within one week. Keep in mind, we had tested like 30 people ever. Yeah.
at that point and and so to do 3600 in a week when we had tested 30 ever we weren't ready for that because they were all going to be doing it at the same time students they all procrastinate so literally one sunday night at 10 p.m everyone started doing it at the same time and all hell broke loose and you know we were just we were literally there just holding on for dear life hoping that we would get through it and we ended up getting through it before that we knew this might be a problem
We actually did some performance testing. We used some freelancers that had been on my team previously at a SaaS software company that knew performance testing. We had them come on and do some testing, thank goodness, because we found some areas with the potential for improvement, let's say. We had implemented some improvements before that first run, thank goodness, but We certainly learned.
We have some scars to show for it, I guess, is maybe a way to say it. And then we continued to improve it over time. Prior to the next year campus recruiting, we had done some more performance testing. We had solved a lot of the issues. So it went much, much better.
So you're kind of. growing with your growth, right? You're campus recruiting, you're hitting some scale issues, you're fixing them, your campus recruiting, scale issues, fixing them, and you're growing as you need to sort of just in time.
Yeah, yeah, yeah. I think a lot of the stuff that we've done has been just in time. Yeah, absolutely. Well, as you step out on the balcony and look across all that you've built, what are you most proud of? I will say on the learning aspect of our solution, what really gets me fired up is, I'll give you an example from recently. We're trying to teach people data skills.
A lot of people don't even realize that they don't realize even what data is or are, if you like the plural form of data. And so we're trying to make sure that people understand that they need data skills if they're in an entry-level HR role or sales or marketing. It's not just for data professionals or technical professionals.
And so one quote that I really loved from a recent customer discussion was they thought data was just numbers. Before they got into quantum and then they started realizing that there's some concepts in there. It's not just about numbers. And so the thing that I really love about what we do is is how we're we're making we're trying to make data and we're succeeding in making data relatable.
to the masses. Um, so, so a lot of people think when they think of data skills, they think, oh, I don't need those. I'm just going to send that to my analytics person. Well, that's just not the world we live in anymore. The data's all around us. My voice right now is data. And, and so, cause Alexa is listening in the next room. And, and so, you know, that's, it's yeah. Yeah. Oh, for sure. I
Um, and so, yeah, it's, it's really making it relatable to people that just gets me all fired up. And, and so that solving that problem, getting people to more confident, hearing them say, Oh, I'm actually, I'm now recognizing the data around me and I'm using it more effectively and I'm understanding the different charts. I just, I just get so fired up by hearing people, um,
You know that it's actually doing that for them. That's that's what gets me fired up. I mean, I'm proud of a lot of things, our team and and so on. But that that gets me really excited.
Well, let's flip the script a little bit. So tell me about a mistake you made and how you and your team responded to it.
Yeah, I mean, I think mistake, I guess I don't think about it like this. And I know this is almost cliche in the startup world now, but when you make mistakes, you learn from it and you, well, hopefully you learn and grow and you're better off for it down the road. I mean, we've made some hires that didn't pan out. You know, we we probably started too heavy in conferences early on.
We spent about one hundred thousand dollars in our first 12 months on conferences and then not enough on, I would say, SEO and making sure that we could get the inbound funnel coming in faster. Um, you know, I just, there are all kinds of, all kinds of things.
And in terms of how we respond to those, to those things, I mean, I think we have to be humble enough to, and this is, this is one of our, I think key cultural traits is that we don't take ourselves too seriously. It's literally, um, in sort of our core values is that the intent is not to take ourselves too seriously. And that way we have an openness to learn from those things.
And so if we repeat them, we repeat them differently and have a better chance for success or maybe we shouldn't repeat them. Maybe it should influence how we do things in the future. And so certainly that's happened over and over and over again. I mean, I
It's in literally every area of the business where, you know, we make mistakes and and then, you know, hopefully those mistakes are low impact and they help us learn to make better decisions next time. And so I think that's the key. But if you do things in small pieces, you know, which is kind of the whole agile movement, right? You do things in small pieces.
then you break things down into bite-sized pieces, then the impact of bad mistakes is much, much smaller, and you're able to learn from them in a positive way and be better next time, which is, generally speaking, kind of how we tend to operate.
This will be fun to ask. I always love to hear the answer from my guest. What does the future look like for QuantHub, the product, and for your team?
Yeah, like most startups, we think big. And so I honestly think that our vision, as a matter of fact, is that five years from now, companies or people all over the world will... There's going to be a transformation in data skills, sort of a human capital transformation. And honestly, I think in five years from now, companies...
And people all over the world will look and they will see QuantHub as the greatest facilitator of that transformation. And so it's so needed right now, and there just aren't great ways to accomplish it. And so from a product perspective, the way we get there is...
right now if you search on data literacy or you search on data citizens if you will quote unquote you see all kinds of training but that training is all the same it's all long form training it it almost takes us back to academia where where you you learn learn learn then you take a test and then you forget everything And there's plenty of learning science to back that up.
And we just don't approach it that way. So we're very much a micro learning platform. You learn a little bit every day. And we're much more like the language learning platforms, like the Duolingos of the world, which are those are based on learning science. And this is the best way to learn. This is the best way to retain information.
And so from a product perspective, we're going to continue to lean into that. We're going to lean into the best way for people actually to learn. Because I like to say that the problem we're trying to solve is in every company in the world, every person in the world type of problem. We all can actually use data more effectively. And it's just now pervasive. Data is everywhere.
And so you have to be able to use it effectively. And so from a product perspective, we'll continue to lean into making this as seamless a solution as possible where people can just spend a little bit every day And learn as opposed to having to go off site, spend eight hours, you know, 16 hours, 24 hours scheduling time to go to training that they're going to forget the next week.
Let's switch to you, Matt. Who influences the way that you work? Name a CEO, CTO, architect, really any person that you look up to and why.
Yeah, I don't actually have specific people. Like a lot of people, you know, have specific mentors in their past. And I can name plenty of people that I've worked with that have influenced me over the years. And they're not, I would say, off limits. almost more often than not, they're actually people that were on my team rather than people that I reported to.
I've certainly learned from people I reported to as well. I was at a SaaS software company for about 10 years and we grew 10X in those 10 years. And the culture was amazing there. We went through lots of ups and downs and I learned a ton from the people that were my peers and the CEO there. But at that same place, I learned a tremendous amount from the people that were on my team.
And I think, you know, you do that by hiring people that are better than you and in lots of aspects and have a different perspective than you. And so my whole mindset over those 10 years almost completely shifted, whether that's, you know, more agile thinking or. I had never been in a startup until I got to QuantHub. That was an early stage company.
But I learned a lot around kind of the startup mentality there. And and so it just really transformed how I how I approach customers, how I approach solving problems, how I approach leading teams. And that was a combination of my peers, you know. CEO that I worked for, and certainly plenty of people on my team.
So I don't have like a specific person that I always think of that I would go to, it's more just a collection of experiences, you know, over my whole career.
We talked about a mistake, but a little bit different spin. If you could go back to the beginning, what would you do differently? Or where would you consider taking a different approach?
and I think about this even in, in my, in my life, you know, I didn't, I didn't necessarily come into this field through, I've never taken a computer science course in my life, but I've done tons of programming and, and served in like CTO roles. But would I go back and, and major in that instead of majoring in chemistry and, and,
working as a research chemist at Procter and Gamble for a couple of years? No, no way I would. And, and it's just because I think you learn in these things, help form who you are by going the approach that, that you went. And so, um, I don't tend to think like this where I would actually go back and, and redo something. Now that doesn't mean we didn't learn.
We spent a lot of money on conferences early on and, and, you know, I, I would maybe do that a little bit differently because we I don't know that we got as much out of that as we needed to. You know, we could have gotten into our upskilling solution earlier on. But then we you know, then we wouldn't have some of the great customers we have in on our hiring solution. So I don't know.
I don't I don't know that I would I would do things differently if given the opportunity just because then, you know, I wouldn't have learned what I learned.
Well, last question, Matt. So you're getting on a plane and you're sitting next to a young entrepreneur who's built the next big thing. They're jazzed about it. They can't wait to show it off to the world. Can't wait to show it off to you right there on the plane. What advice do you give that person having gone down this road a bit?
Yeah, I would say don't be too proud. You know, I think it goes back to the pride and ego thing. One of the things that's difficult is you always feel like you have the right solution and you're not open to – there's not an openness there. to listen or to listening to what others are saying, customers or the market or whatever.
And so one of the things that I see in people, particularly early in their career, and I certainly did the same thing, is that people oftentimes don't listen to understand. They listen just so they can ask the next question or say the next thing they want to say. And
when you, the advice I would give, in fact, I literally was just sitting, um, at a restaurant and a gentleman came up and he was a, he was a coach for, um, he was an assistant coach at a soccer, a soccer program at a university. And he was talking about, uh, recruiting. And so I was giving him this advice, which is similar to this question is that, um,
Just make sure you're listening to understand because if you really want to make a connection with someone, which could be a customer, or you want your solution to connect with them, you want it to actually solve their problems, you have to understand them. And so that comes with being empathetic.
But the root of all of that is listening to understand and not just listening to respond or listening to comment or thinking about what you're going to say next. take it in, listen to understand what they're saying. And then, you know, that actually will let you then absorb that and figure out if you're on the right path.
If you're not listening to understand, if you're, then you're just sort of thinking about how you're going to force this into situations, then you're probably not going to be very adaptable as a startup. And I think that's the most deadly trait is when customers, when startups and early stage companies aren't adaptable to feedback and
then I'd say that's usually the death knell for startups, in my opinion.
I think that's great advice. Well, Matt, thank you for being on the show today. Thank you for telling the creation story of QuantHub.
Yeah, my pleasure, Noah. I appreciate you having me.
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