 Hello and welcome to My Career and Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp, and today we're talking to Bill Bruno, the CEO at Celebrus. More and more companies are considering investing in data literacy education, but still have questions about its value, purpose, and how to get the ball rolling. We're doing the newest monthly webinar series from DataVercity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at DataVercity.net. Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at DataVercity, and this is My Career in Data, a DataVercity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education, go to DataVercity.net forward slash subscribe. And today we are joined by Bill Bruno at the CEO at Celebrus. And normally this is where a podcast host would read a short bio of the guest, but in this podcast, your bio is what we're here to talk about. Bill, hello and welcome. Hey there. How are you? Thank you so much for having me. I'm excited to be here. I'm excited for you to be here as well. So tell me, OK, so you're the CEO at Celebrus. So what is Celebrus and what is it that you do? Yeah, no, absolutely. So Celebrus is a data capture and contextualization platform, which is a mouthful. What the heck does that mean? It means that when you read any surveys from like a forester or a gardener, they always talk about how difficult it is to use digital data for business initiatives and to bring it together with other sources. And they talk about things like how digital identity verification is a real problem and difficult for solutions to manage. And those are the gaps that Celebrus solves for. So you deploy Celebrus across all your digital touchpoints, websites, mobile apps, kiosks in an airport, ATMs. And we capture all the data for you. We give you a logical table data model that data scientists love and it makes it easy to use it everywhere. We solve for digital identity and we basically at the end of the day solve for two types of use cases. The root and the heart of everything we do is to build better relationships between brands and consumers. On the marketing side, that's in building better experiences by ideally showing you things that you're interested in. But on the fraud side, it's preventing fraud and protecting all of that hard-earned money that you've got that you're looking to spend that day or unfortunately where someone else might be looking to spend it for you and we're trying to stop that from happening. Awesome, I love that. So and you're the CEO. So, you know, we get to have a general sense of what a CEO is, but what's kind of your day-to-day? What are you doing? You know, I think from a CEO perspective, I'm not going to make it sound all fancy, right? What it really means is you're the guy that flies in and wears many hats ideally and helps your team on a daily basis and tries to build a high performing team, tries to help people better perform at their jobs. And in some days, some days I'm a bulldozer, right? Like there's a roadblock in the way of our team from doing something valuable and I'm the guy that comes in and tries to help clear the path for them, right? So, you know, this is my third stint as CEO in different companies. And, you know, I think it all kind of looks and smells the same, right? You've got to be involved. You've got to be working with the team. You're creating opportunities for people to grow professionally and you're trying to make sure that you're building the right culture that's focused around customers and delivering value. OK, OK, so let's back it up again a little bit way back. So when you were in elementary school, you know, was this the dream? This is what you wanted to be when you grew up. What was the dream? You know, I don't think I ever wrote in my terrible handwriting data analyst or computer engineer and anything that I had ever done in grade school, right? I think you go through all the standard, you know, sport, sports, I'm going to play baseball, I'm going to do all these things and your parents lie to you and tell you you're good at it, even though you're probably not based on my background as you were talking before we started the recording and all the wrestling action figures. You can probably assume that I at one point wanted to join sports entertainment and be a professional wrestler, the likes of a Hulk Hogan or Ultimate Warrior or things, things along those lines. But, you know, data didn't really peak in for me until probably towards the end of high school when I was actually trying to figure out what the heck I wanted to do at college, right? I mean, that that's just kind of it kind of just fell together. Oh, so what was that path? What were those thoughts that you were in high school? Like, what am I going to what am I going to major in? Yeah, so, yeah, I mean, I was always kind of a nerd, right? You know, I'm proud to be one, but the areas I excelled in were math and science. And so for me, it was kind of exploring where where you could go with that type of background. And, you know, when I was entering school, I graduated in 2004, right? So when I was entering school, it was kind of, you know, the the the rise and fall of the dot com, right, throughout my time there. And I just decided that the computer engineering sounded really cool. I didn't have many reasons for it. I kind of just stumbled into it, right? And went down that path, did an internship where I was in a cubicle coding and nothing against against that part. But it wasn't really for me. I didn't mind the coding part, but I kind of liked interacting with people. So then I started looking into consulting. And so when I came out of college, I joined and found co-founded a consulting firm with with a couple of other folks. And that was the start of my career. And it was focused on at the time, what was called web analytics. So helping people actually prove whether or not those websites were going to make money for them, because, you know, we had just gone through the crash. Wow, that's that's amazing. So you really got into some some data right away. Yeah. Yeah. So tell me a little bit about that. So in that and that journey and yeah, it all started for me in college. I had a couple of classes I took, actually, ironically, given what the art what's talked about so much every day these days. I took some artificial intelligence courses in college. I did a lot of math, did a lot of coding, did some gaming coding, which was quite interesting, although very, very difficult to make so you appreciate the people that make video games today. So I dabbled in a little bit of everything, but I just always loved understanding how things worked. And that and that just kind of tailed itself into a couple of projects for me towards the end of my time at the University of Illinois down in Champaign, which was focused around actually using at the time log file data from servers to identify insights. And that's largely what became web analytics. And now it's obviously grown into something much crazier. It's been a wild ride for the 20 plus years I've been doing it, but it all started with some simple web log files from some servers and trying to make sense of them and write code to do so. Yeah. Yeah. Love it. So you still have that consulting. So tell me about that, that consulting and to grow into that. Yeah. So I did we the company that we found it was called Stratigent. You know, everybody's got to have a fancy name with strategy in it. And, you know, we built that business up. I ended up selling it to a to a different business in the UK in 2013. And then started sort of running analytics globally for that business for quite some time before I decided to move on from there, figure out what I wanted to do next. And now I'm on the vendor side for the first time in my life, which has been a been an interesting shift because historically I've always been the consultant that's, you know, vendor agnostic. I'm going to tell you what the right fit is. And now I'm telling you why my solution is the right fit. So that's been kind of a fun transition in my journey, I suppose. I love it. Well, so now with all this experience with data. So what is what is your definition of data? Something that's usable. And I think that that's how I always like to think of it, because there is a lot of information out there. I mean, even even in the data that Celebrus collects for clients, like you're not always going to need all of that data on a daily basis. Right. It's to me, it's what's usable. How are you going to use it? And that doesn't mean that you should limit what you're what you're collecting because I think it's very easy. And I've seen a lot of people talk about that, like only collect what you need. And I think the problem with that is none of us are Miss Cleo. You're not going to know what you're going to need three months from now, six months from now, a year from now. So I think the most important thing now is collect something compliant that you're actually allowed to collect that consumers are allowing you to do so. But the definition for data to me is it's it's not something that's just sitting there, right? It's some it's information that is beneficial that you can use to add value in your job, in at home, you know, with your customers, you know, it's kind of all over the place. Right. I mean, I just read I just reconfigured all of our thermostats for the summer, right, to change all the schedules. And it's all automated and everything's based on data and how we historically use it and when you're home, when you're not. Right. So there's there's no shortage of information, but it's it's to me, it has to be usable. Otherwise, you're probably just wasting your time. Absolutely makes sense. And I have full appreciation of an automated house. Most of mine is too. So tell me, so as a CEO, how do you work with data? What's important to you and how do you use it? And, you know, I mean, you're building it into a product and really at the core center of your product. But but how are you using it in your daily work? Yeah, so it's a it's a great question, right? And I think, you know, from in all three instances where I've run businesses in my career, it's it's always wherever possible, every decision needs to be data driven. But you've got to bring in the right people across the business that buy into that approach, that buy into that culture and find ways to easily automate these things to get the right information to the right people, because it's not just me. That's going to need data on a on a daily basis or on a weekly basis. It's going to be my head of professional services and delivery. It's going to be my CFO and his team. It's going to be my head of HR and her team. It, you know, every single team needs to to to have that culture embedded or that value embedded, that data is how we're going to make decisions, right, even every marketing campaign or every sales idea that gets thrown my way. I'm happy to test them, but we have to have a plan in place that clearly defines what success looks like so that we can work back from that and decide what data we need to be able to measure that success and then make sure that the systems we put in place do that. And I'll be very honest with you. I've only been CEO of Celebrus for almost two years now. And when I joined and the new CFO joined with me at the basically the same time, the first assessment we made was that there weren't enough systems and in some cases any systems in place to really help bring that data driven philosophy across the business and to help empower our leadership and their management teams and sort of as you go throughout the whole organization, you know, to not empower there was nothing there really to empower them to use data in the right ways. And so that has always been a passion of mine. I mean, I think I will always be a data analyst at heart. And there are things that I see daily. There are things I see weekly, you know, so on and so forth that are used to help guide the direction of the business. Amazing. So so is there been any key wins that has enabled you to transform that that culture? Absolutely. I mean, I think, you know, it's always easy, I think, to point to the stuff that goes well, right? Because it's like, oh, it's going great. Like, look, the data is showing us that something's performing. In my opinion, culturally, when something doesn't work, that's when that's when it really comes out. Because how you respond to that, I think, can make or break a culture, right? If you want people to be honest in your business about what's working and what isn't, then you also have to create a space for them to be able to admit that something didn't work, but that we learned from it and how we're going to apply that going forward, right? And, you know, that could be something simple, like a, you know, trying a different style event for marketing purposes and sending the team that maybe may or may not work, or it could be something internal. Like we brought in some new technology to try to help automate some of our efficiencies and it just didn't quite meet expectations, right? It's it happens. You need to be able to test and learn. But the problem, I think, is that a lot of businesses, you know, sometimes skip the data part that goes into that and don't don't think through what success looks like and don't don't make sure that the right metrics or, you know, measurement protocols are in place to help guide that. It's complete in total sense. So, you know, especially being in an executive role, you know, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? So another great question and one that is in almost every article these days with with artificial intelligence, isn't it? And what that's going to do, right? But I think whenever there's been technology innovation, holistically, yes, you're going to see certain jobs perhaps become less important or perhaps become automated by that technology. You've seen that in every industry. But then there's always something next. There's a there's a next step from that that creates different jobs or brings different skill sets or offers opportunities for people to to really, you know, dive into a new career or a new set of skills. I mean, data science was like that, I would say, over the last, you know, let's say 10, 10 to 15 years, right, is sort of something that when I was in, you know, in university or college, depending on what side of the pond you're on, wasn't really discussed much outside of like some hypothetical artificial intelligence models, right? But, you know, so for me, I think that jobs and data are going to continue to grow. I think that they're going to become increasingly more necessary. And I think a lot of that's going to be driven by privacy and compliance around the globe as well. You know, it it just may look different, right? You might not have as many people wrangling the data, but you're probably going to have a lot more people spot checking, right, so that the robots don't take over and so that Arnold Schwarzenegger doesn't have to save us again. You know what I mean? I do, I do indeed. Yes. So, so then not to put words in your mouth, but just kind of so it sounds like data governance roles are going to be really, really important and data governance isn't necessarily a dirty word, but a necessary word to to to manage the the policies and processes behind what's. Yeah, yeah, I think data governance should have been for a while, right? And I think we're going to see a pretty heavy investment in that going forward because nobody wants to be the next brand in the news. But I also think data cleansing and validation, too, is is going to be a big one. And I I'm coming at that from an angle of ethical AI, right? You know, I think we're we've seen for years, you know, where things aren't perhaps tested against, you know, what society actually looks like in the real world when products are brought out and it's because the data is bad, right? Or it's not complete or it doesn't represent, you know, the the target market or what the real world actually looks like. And I think, you know, as as organizations run and in many cases are right now sprinting towards AI or generative AI or whatever you want to call it, you know, we can move fast, I suppose. But the part we're going to need something underpinning to make sure that the inputs are good enough to to satisfy the output, right? And so I think you're going to see a lot created around this and to help sort of drive us into the next, I guess, data frontier, I suppose. Absolutely. Now, you mentioned ethical AI. So what does that mean to you? There's so much debate around this and there's so much discussion on this now. I think it varies by category or what type of model or what type of product you're talking about, right? But, you know, I've I've I've spoken on many podcasts over the years and, you know, it's it's a bit tongue in cheek, but true. I used to call them racist robots, right? But it was like, you know, you you've got products or things, you know, like self driving cars and things like that that are not designed for the society that we live in because the the the test samples, you know, are then what they've used as the data in is not complete. And so it's not a full test. So for me, there there's ethical AI on sort of the product side. Then I think when you when you get into things like the banking industry or even retail or travel, where you're perhaps changing the prices on things or you're perhaps changing the offers that I might be seeing versus what you you might be seeing, there's obviously an ethical element to that as well to make sure that the reason we're seeing different offers is actually valid and based on interests and not based on some sort of discrimination. And then I think there's just holistically this this thought that needs to be to spot check, right? Because robots, whether you you know, what do you think of them as like, you know, what's in the movies or you think of them as just a data model running on a server doesn't matter, right? They're they're not they're not going to be any smarter than the data that you've provided access to. And so that's that's when I think of ethical AI and it's something I'm extremely passionate about. But it's you know, there's a lot of angles to it. There's not a right or wrong answer. But I think the most important answer is remembering that there's a lot of bad data in digital and there's a lot of bad data online. And when you've got things pulling from those sources, they're not necessarily using the facts because a robot's not going to just be able to discern that. I mean, if it's if it's published online, it may not be true. You know, for, you know, for someone who has trusted everything on Wikipedia for his entire career, I have a really difficult difficult time making that statement. Right. But it but yeah, you know, it's someone told me the other day that the internet sometimes lies and I I I was just as shocked as you are. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DVTALKS for 20 percent off your purchase. I love it. Well, so what advice then would you give to people looking to get into career in data management in any form in any level, whether it be, you know, a data scientist or into data governance? Maybe specifically, since we're kind of talking about that, our data cleansing, what skills do you need? You know, and how do they get into that? You know, you know, I think what the best part about when a bunch of of new opportunities are being created quickly is that, you know, people can can jump in and learn quickly as well because you don't you're not surrounded by a bunch of experts, right? There's not necessarily a massive pool of experts that organizations can pull from. And so there's really a good opportunity to land and grow and build a career around it. So for people, you know, in in college now, maybe trying like me to figure out what the heck you want to do in life or maybe even those that have skipped college because it's so damn expensive these days, part of my language. And they've chosen to just teach themselves some things. You know, it's there's an ample amount of material out there to learn. There's an ample amount of online courses to teach you about data structures and principles and some of the basics. It's a lot of books out there. You know, so it's not it's not hard. I think my only advice would be if you if you're passionate and you think this is something for you, then then get ready to put in the legwork and work hard. And, you know, that that'll pay off. It might not pay off right away. And you might have to, you know, have some stop starts at some organizations or take an intern internship or you're bringing people coffee in exchange for getting access to stuff. But it's, you know, that the hard work will pay off. And their data is currency in our in our world these days. You know, it's it's the gold of digital. It's the gold of brands and organizations. And so I think it's it's a great career to explore, you know, digital analytics, data science, data governance, however you want to look at it. If you have a passion there, there are an ample amount of roles available for it. And every time my organization publishes a role for someone in our org, you know, things flood in and we're not even that well known of a brand, right? So it shows just the amount of demand and the amount of interest out there. So it's I think it's a great great career for someone that that is looking to start one or someone who's looking to reboot. Oh, yeah, indeed. Any advice on, you know, additional resources and, you know, and how they go and keep on learning? You know, of course, we have our program. So I'm just going to give my plug for a database.net. But, you know, also, you know, where else can you go? Where else can you go learn and and what are some great resources and how do you find mentors and. Yeah, it's a it's a it's a good question. I mean, I think I was going to plug your stuff anyways, but you you be to it, right? But I because I think everybody learns differently, right? And so, you know, depending on how you learn, probably is going to dictate where you go, right? You know, for me, I love podcasts. I love listening to things like this to to learn things of interest and then go off on my Google journey and learn more, right? But the for those there's an ample amount of of phenomenal books that I'm failing to remember the names of that have been written about data structures and principles. There are a ton of analytics universities and and actual, you know, not just online versions, but there are actual universities throughout the states that have and in Canada that I'm aware of that have some phenomenal data and analytics courses. I have a friend of mine who's taking some online courses right now to to reteach themselves some new principles around data. So it it's it's crazy. I mean, you can even go to like, you know, you can go to Google's education. Most of it's free to get you up and running. You could go to a Amazon and they have an entire suite of educational materials around AWS and sort of cloud principles. And the list could go on and on. But it's as you might suspect just like data is increasing on a daily basis. So are the amount of opportunities to learn about it. And and just to kind of expand it a little bit. I mean, you mentioned early on, too, that you, you know, you were as a coder. You really wanted to go out and be more engaged with people. You know, how important are those communication skills and those soft skills needed in these data careers? You know, I think it's a good question. I think it's interesting because it depends on the kind of networking is great for everyone, right? But but some people and and, you know, I've had tech people that I've worked with in my career that just like to go work and they're in there with their, you know, six screens and and they just want to go get their work done. And that's their preference. Then you've got people like me who are going to be more on the other side where I kind of want to I want to talk tech, but I also want to want to be out in the front and interacting with clients and partners and and learning from them. But, you know, there's a lot of phenomenal networking opportunities in almost every city these days. I mean, on LinkedIn, there's several that I participate in the Chicagoland area where I'm based, but there's a ton of LinkedIn groups around data that share different conferences, networking opportunities, etc. And, you know, I think those are great ways for you to meet people, you know, and find people who perhaps are in roles that you're interested in or working for companies that you're interested in working for. I love it. Well, I would be remiss if I didn't ask you. If people wanted to learn more about Celebrus, where would they go? Sure. So you can go to Celebrus.com. That's C-E-L-E-B-R-U-S.com. You can find Celebrus Tech on Twitter as well as on LinkedIn. You can just find me on LinkedIn, Bill Bruno, and I'm happy to connect you as well. And we're always we're always open to conversations. If you need recommendations on jobs, if you want to look at my LinkedIn network and you want me to try to lead an introduction for you because you're looking for a role in data, I'm happy to do it. I've been fortunate in my career to have people help me along the way. I wouldn't be here without the help of many, many people, and I'm happy to pay that forward. Thanks so much. That's a really gracious offer. Thank you. And that's part of my one of my favorite things about the data communities. Everyone seems to be really, you know, willing to help each other out. I haven't seen anybody make it on their own. It's always been a collaborative effort. Yeah, anybody that thinks that you can just make it running on your own, that's a bit of a fool's errand and they've been lied to, right? I mean, you know, there's there's hard work. There's always a little bit of luck and there's always somebody that's put their hand out to help you get there, whether that's coaching, whether that's advice, whether it's actually just giving you an opportunity to to start a career, right? And you know, like me, if I, you know, I came across the Josh and Julie, they were a husband and wife that wanted wanted wanted to build this consulting firm. And I started in their basement, right? I mean, that that's that's where I started and many people along the way, you know, offered me opportunity, you know, you can work as hard as you want, but you still got you still need that network to help you out. I love it. Well, Bill, thank you so much for taking the time to chat with us today. I really appreciate it. Oh, you're absolutely welcome. I really, really enjoyed this. I listened to a lot of your talks, so I was really excited to be part of this. So so thanks. Thanks for having me. And it's yeah, it's been a been a pleasure. Thank you so much. And and and to all of our listeners out there, if you'd like to keep up to date in the latest in podcasts and the latest in data management education, go to dataversity.net forward slash subscribe until next time. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity subscribe to our newsletter for podcast updates and information about our free educational webinars at dataversity.net forward slash subscribe.