Data, Analytics, & Intelligence in Sports with Eddie Kendralla and Ben Wang - Ohio U Life After Court Street Series

(upbeat music) - Welcome to today's episode on Life in the Front Office. I'm your host, Jake Hirschman. This is part of our e-com Ohio University Sports Administration series Life After Court Street. I'm excited to be with my special co-host and Laura Waters Brown and our two guests today and Ben Wong and Eddie Kendrala from both the New Orleans Saints and Pelicans and the Pittsburgh Penguins. Excited to talk to them a little bit about business intelligence, analytics, the roles it plays within. The sports industry not only on the business side but the player side as well. Some of the things that you maybe don't know about the business and analytics side of things and how it truly impacts all different aspects of the business, not only on the court but off the court. So Ben, Eddie, welcome, excited to dive into your journeys from Athens a little bit and go from there. (upbeat music) - Thanks so much. - Thanks, Jake. - Ben, let's start with you from a journey from Athens perspective. Any lessons learned that you were able to kind of take upon from Athens into the rest of your career? - Yeah, that's a great question. I've always think of Athens as a second hometown just because I'm from China and Athens is the first city that I lived for more than a year with all the classmates. And for sure, the biggest lesson I learned is really just as an international student you gotta learn the culture. You gotta learn the language first. You gotta hang out with your friends and classmates. You gotta learn how people think, how people discuss of business matters. And the most thing I really appreciate is really all the classmates are so friendly, not just my class, but also the class before me and after our class. So I think the biggest thing I would say, special appreciation and thank you for our staff and at the time it was led by Jim Kailer and all the professors, Dr. Lee, everybody was extremely helpful and helped me a lot. - So, Eddie, you crossed over with Ben for a year, I think you said and what was that experience like going from Athens to working with Navigate to kind of start out, but then going to Phoenix for a while? - Yeah, I mean, Athens and Phoenix are very different places, but enjoy my time at both of the places. I think for me from a program perspective, one of the things that was helpful, I knew when I was going there that I wanted to focus in some sort of data and analytics profession, once I left OU, but I actually really valued being in a class and in a program where there was very diverse in terms of what everyone was interested in, right? You had people interested in collegiate athletics, you had people interested in community relations, sales, development, you know, some of those things that I wasn't really interested in or even as familiar in, but being able to interact and then maintain those relationships beyond has been was really helpful and has been, you know, helpful throughout my career. And, you know, the program in general, it helps expose you to a lot of different areas of the sports industry. And, you know, I think that that was honestly something that gave me a leg up when I left OU, was being familiar with, you know, the different areas of a sports organization and understanding what their challenges are and helped me as an analytics person to help them, which is, you know, I really how I see the role of business intelligence analytics is as a support role in helping the rest of business operations do their job better, which of course, it helps when you understand what they actually do. So, you know, I think that that's something that, you know, I probably didn't appreciate as much at the time, but as soon as I left, you know, I was very grateful that I got to be exposed to all those different areas. You'll never, ever, ever catch me picking up a phone and trying to sell something, but it's good to know people who can do that. - You know, that's still interesting both of you going through the program because I think when you think of a higher university in a sports ad program, you don't really think about the data side of things and the data science and the analytics portion. After one, definitely, definitely should have paid more attention in my data analytics classes that I had and then ended up in data analytics for a little bit. So, I find that very interesting. But I guess for me, both of you going into this world that is so heavy, focused in data and analytics and numbers and oftentimes I think people go to schools to have that specific education curriculum. How did the sports side or understanding the sports business classes help you in your career and to where you are now? - Yeah, I mean, again, I think like understanding the difference, not only jobs within a sports organization, but also what exists outside of just sports organizations. I did my internship with Navigate and that's an agency that specializes in sponsorship valuation and research and that was something I didn't realize that existed when I got to Ohio, right? So, I think it's just understanding some of the different things that are out there, some of the different challenges that organizations or agencies and things like that are facing. And I think even since I was at OU, the number of jobs in the analytics field has increased significantly and the, I guess the industry, analytics industry is kind of expanded and broadened. So, it's not just the backend data programming type things. There's now analytics, there's people focused with jobs in the sponsorship department or in CRM, like those are now largely like analytics fields that even when I was at OU were not, we're probably not as analytic centric, but that's just kind of the evolution, but at the same time, what OU exposed me to, I think helped me understand how you could apply data into those areas. - Eddie, it's safe to say that when you were probably going through the program, everyone kind of thought of analytics as Billy Ball, Money Ball, I'm sure that was probably close to the time in which maybe the movie or the book came out or something of that sort. And you know, everyone thinks that that's where analytics is. And at the end of the day, and I'll go to you Ben, right, if the analytics is ultimately a tool to help decision making, to help further business decisions, strategy, you know, futuristic planning, et cetera. You have a unique spot where you're not only on the NFL side, but also with the MBA team, to where you see two totally different landscapes in a sense. Yes, there's probably a lot of crossover, but can you talk a little bit about how the business intelligence world has grown and maybe the differences between sports? - Yeah, definitely. That's such a great question. And Jake, I cannot agree more with what you just said what an analytics mission is about. I think it's really just a tool or a team of people who folks on using their skills to connect people and data and people's decision and answer people's questions. So I think that's really spot on. And my experience really helped me a lot because, you know, before I came to OU, I was a software engineer. So dealing with databases and coding is like, you know, is like a skill that I have practiced for so long. And with the school works and everything and my internship with DD sports, I started to learn about, you know, all the ways that I can use the skill. That's really, I guess when I really started analytics was not, you know, super hot yet. It just right, right the beginning of it. So I got the opportunity to really try all the different stuff, different projects using different tools like Excel, start from Excel, then I'll, you know, SQL, Python, Tableau for visualization. So. That helps a lot and it's a very common right now for most of the teams. Everybody need to learn these tools to be able to do their work. And in terms of the difference of sports, I think for two teams in the same town, I can say that the business focus is a little different and that kind of really drive what we do. For example, the scenes are in a very lucky spot because we have very loyal fans. We have the whole golf-coast fans who are supporting us every year. And our focus is really driving the best fun experience and make sure we have provided the best service and make sure when the fans come to the games they enjoy the experience and also focus on data, focus on how to capture as much data as we can to understand our fans better, understand what their needs better. And the third part I think on the business side for the Saints is really drive the revenue. So maintain the revenue we can. With the Saints, we are still in the process of doing the Silver Dome renovation. You probably saw on the news. Right now we're in phase two and hopefully by end of August, the world finish phase two, when we'll create a bunch of new seating locations, new premium inventory. So focus on that is also part of our job right now. On the public side, the season is still going on, but the major difference is that the public is still we still need to try to tell all the sports fans and especially basketball fans how the experience is, how good it is to come to the to the public in the game. We still need to retain all the season-tick member who buy the public in season-tickers or partial plans. So obviously the job for us is focusing on reporting, analyzing the challenges and opportunities pricing of different products during the season, doing all the dynamic pricing projects for each game every day and try to automate all the process from data operations to analytical projects to help all the data systems to connect to with each other and help all the different departments to sell and to serve the fans. So on the business side, definitely different challenges. Yeah, you mentioned quite a few things there from a complexity standpoint, right, in that you're trying to accomplish or help support accomplish multiple goals, right, moving at the same time, yet you're in a city with two different fan bases, right, in a sense. And so Eddie, my question to you would be, you know, in Phoenix, right, you kind of have the Phoenixes is up and coming, there's people moving from everywhere, you don't necessarily have the same type of fan base that you would as a Pittsburgh Penguin fan, right, where you're dealing with different fan insights and so on. So as you think about how the organization structured or how the organization has created their culture, has that affected the business analytics side where you're using data for insights and decision making and so on. Yeah, it's a good question. And, you know, I think, you know, having been at the suns for seven years and now the penguins, you know, approaching a year, you know, I've got a pretty good handle on the differences. And, you know, I think it, it's a macro thing to be right, like every organization faces different challenges and has has different opportunities, whether it's regard to look how team performance, the league, things like that. So that's that's going to be the case no matter, matter what your team you're at. But yeah, like, you know, Phoenix is very much a transplant city. And, you know, you have have people from all over a lot of people from, you know, California, but also a lot Midwest, influence people, people going west. So, you know, it tends to be more transplant, not everyone there is a Phoenix sports fan. So I think that lends itself to, you know, you want to make your product more experiential. And how can you, how can you do that? Because you, you do have people who maybe aren't going to be in town for, for the whole year too, right? It's a, it's a tourism destination. So how do you create opportunities to capitalize on certain events? The, the waste management open was a big one, right? So like, you know, that was always an opportunity for us, having, having games at the Suns to capitalize on that, that type of thing. But yeah, so I think like, you know, the experiential thing is certainly important when you have that, that transplant type of scenario at the penguins. You know, it's a little bit different. You know, a lot of people are born in Pittsburgh and never leave, right? And it's definitely a sports town and every team shares the same colors and has, you know, a extremely wide following. And, you know, I think one thing too at the, the penguins, which is, is probably a little bit different than what experience at the Suns is just the gender differences too. You know, the, the league, and there are those that the team here has, has an extremely, uh, rabid female following as well, which I, you know, I think creates some, some opportunities. But in general, like, just the engagement is really, really high. So, you know, from a social media perspective, um, it kind of puts a, uh, a microscope on, on that. So, you know, there's a lot of opportunity, but also a lot of pressure to make sure you're, um, engaging with the fans in the proper way, because there is, there is so many people that maybe won't even ever come to a game, whether it's because, um, you know, it's, they're not able to get tickets or, or just because they, they like to watch on TV, but, um, so I think, you know, more of a focus on the social side is, has certainly been one thing that, um, is, is evident, um, from, from coming over here because, uh, there is such that loyal following, but, you know, with that comes, um, challenges to make sure that you're, you're delivering value to them and, and also monetizing it the proper way. Yeah, I just, I think I really want to echo what, what, uh, Eddie mentioned earlier is that, because one thing I have so much memory about Phoenix is that, you know, when I was working for the coyotes, it's essentially in the same market. The biggest challenge for, for me, to kind of think about in a work on almost for every game is that, you know, there are just so many away team fans, opponent team fans, every game and, and the, the best game you're thinking at the time was the coyotes versus Chicago Blackhawks, right? But, when, that's, when during the game, when you're figured like 80% fancy and tanned are wearing the Blackhawks jersey. So it's for our fans who are actually buying season tickets. It's actually a nightmare for them to attend, because they feel like being, you know, it's, it's not a good experience, because they, they can't, their voice is not heard, and they're dominated by the, you know, by the opponent's fans. So, you know, that's definitely one of the interesting I remember about Phoenix and the challenges of teams in there. What in both of your, you know, unique sense to where you are now in your career? What are some skills that you use regularly now that you maybe didn't think you would need, or something that you kind of picked up a long way like a skill you didn't know you would need? Yeah, I think for, for me, one thing that, you know, when I was preparing for, you know, current and analytics, you know, you, you think of the data side of it, you think of the math side of it, right? But the, the data visualization side of it was, you know, I, I think it is and will continue to be kind of the underserved and under taught piece of it. So, you know, Ben mentioned Tableau and, you know, there's other tools in that same space, but, you know, being proficient in Tableau and not only, you know, knowing how to use the technology, but how to use it effectively to take the data analysis that you've done and present it to others in the organization that may not be as data friendly because, you know, nine times out of 10, they don't necessarily care about all the hard work you went in data prep, data cleansing, data manipulation. in programming and applying mathematical processes, they really just want to know the findings and know that you did it the right way. So being able to display that and show them in a concise manner and there is a right and a wrong way to do data visualization and it can ruin a lot of your hard work if you don't do it correctly. And it's something that is kind of a passion of mine is data visualization and understanding how to apply it. And also it's kind of funny but like PowerPoint too, you know, like probably use PowerPoint way more than I ever, ever expected to and a lot of that is taking the visualizations you're creating and then putting words to that to convey recommendations, analysis and things like that. So those are two things, which again, like maybe you don't think of it right away as being analytics, you know, primary focus, but they're, they're crucial to really being effective in an organization. Ben, a quick follow up on at these point to you and that, you know, the data visualization is huge, but for someone who's coming out of grad school or college or even an entry-level role where they're just learning about the data side, what are the hard skills that they truly need to be competent in to be able to go into an interview because it's one thing to just say, oh yeah, I'm good with numbers or I know, you know, I got a, I took math classes in college, right? Like there are true, I mean, do you put, do you put people through like coding projects in the interview process? How do you figure out if they can truly do what you need them to do? Great question. Great question. So I think what Eddie mentioned is, is very helpful to understand this is that if you really want to have a career analytics, which means, you know, you're not going to switch to sales or marketing or event operations or sponsorships, those are all great careers too, but if you really want to dedicate and commit to a career analytics, the skills of, you know, SQL, Tableau, and maybe some Python is extremely important. The reason is that when I started working for, you know, my first job with Columbus Crew, there's really no data visualization tools yet, but we still need to create a lot of visualizations to help people understand the situation, understand what, you know, what the data is telling, what story the data is telling or what insights are. So having that skills is really, you know, step one. And then step two and three is really what I learned through the career is that, you know, the hard skills in SQL is extremely important because the teams are using all the different data systems from ticketing system, no matter tick-and-master or stop-hub or sea geek, and they're using merchant die systems, every one of them, and CRM and the email marketing, every one of them is a data system. So having a hard skill of using SQL is one of the interview question, I always, I always kind of ask, we have a schedule of interview questions, basically is a three step question focused on SQL, basically just simply as giving you a table in SQL, write a query for me to answer a few questions, as simple as that. If somebody is really no SQL and work with SQL before, the question will, you know, the answer will help us to understand if the candy base is qualified. And besides that, I think one more thing I learned along the way through the past 10 years is really, as an analytic person, absolutely, you have to work hard, but working smart in a funny and automated way to help you save time is so important. That you don't want to wear yourself out, you don't want to keep doing the same thing again and again and again, not, you know, not making progress. It's so important that you use the tool, learn about the tool, learn about the new skills to automate the things, you know, automate your reports, right? The insights, even the story you want to tell, automate the charts, automate the dashboards, automate, you know, the pricing for single game tickets, if you're dynamically pricing them, automate, you know, the data integration between systems, right? All these stuff are going to be interesting and fun for the students and young professionals to learn, as well as going to help you save time a lot of time so that you have, you have more time to talk with people, you know, dealing with issues and learn more, you know, better skills. So I think that's, that is an important thing to always keep in mind when, you know, students are coming, coming to the industry. Yeah, that's a great point. I know Laura's got to follow up to it, but, but Eddie, I think, you know, it's, it's kind of like different languages, right? If you're going into a player development and, in baseball and you, you think you can speak Spanish, but you really can't, right? It's, it's kind of that, that level of competency, right, Laura? Jake, you literally are reading my mind because that was quite literally my question, was I feel like in my little stint in the data and the sequel in the tablet world, and I do mean stint very short amount of time, it was almost a different language that you had to learn, you know, on a scale of one to ten, ten being extremely important and one being whatever, you know, how important is it that somebody coming into this field is able to take what the client is asking for, understand what they're asking, actually asking, provide that in data and then, quote unquote, translate it into Langton's terms, if you will. Yeah, I mean, I think what you just described is really kind of the essence of analytics and sort of what we look for when, you know, or at least what I look for when I'm hiring whether that's for an internship position or, you know, a full-time hire is really someone being able to go soup the nuts from a very unstructured raw data set and take it and create actionable insights off of it and going, that's going through the steps of data cleansing, data manipulation using programming, you know, applying mathematical techniques, creating visualizations and then eventually articulating those findings, you know, if someone can demonstrate they can do all of those, then, you know, I think that, you know, that's, that's someone where we're interested in and I think that, you know, as students are looking to get into the analytics field, I think that that's the approach they should be taking is, can I check each of those boxes in some capacity? Now, from a programming language, you know, I don't, you know, at least personally don't expect, you know, every candidate to have, you know, knowledge of all the different languages, SQL, Ben mentioned is very important and it's very translatable across, you know, most organizations, so I always recommend that's a good place to start, but, you know, there's our Python, C#, like there's Java, there's a lot of different languages out there. Generally, and I think this is, is probably true across the field is, if you can show demonstrated ability to learn one of those languages, usually you can learn and pick up the others and start to apply it. So really it's just showing that you're, you're able and willing to, to do and work in those languages, you know, when I, when I started the Suns, I had SQL knowledge, but they were in the process of implementing SAS technology, which in and of itself is it's language a little bit, has some, some SQL attributes to it, but, you know, they didn't expect me or, or try and find someone that actually had SAS, because you're starting to really limit your pool, ultimately, you bet on the person, right? And, you know, they, they thought that I could have learned and, and go through training and, and figure it out. So I was able to use kind of that foundational SQL and, you know, I had some Java knowledge and then apply it to learning new things, right? You know, because every organization's different, they're set up differently from a data standpoint, use different languages, you know, at the penguins, we're largely language agnostic, you know, so we'll hire interns and, you know, different projects called for different things, but if they want to use R to do something more, you SQL, like, we're open to, to a lot of different things. But, but yes, I think like, long-winded answer, but being able to, to just show that you can, can do those different areas, and then, you know, hopefully pick up on the rest and learn the rest. Eddie, I think there's such a critical component I've had. I've gotten a similar stint to Laura in terms of the Tableau world and coming from and someone who didn't have any experience and then getting kind of thrown into the fire and trying to figure it out, you learn a lot about there's good data and bad data, right? And not all data is good. If you have bad data and you're trying to tell the wrong story, right? It can have a lot of bad repercussions in the long run and from the perspective of making sure, you know, you mentioned data cleansing and, you know, understanding how to structure the data, right? There's a lot of backend stuff that the typical employee has no idea about, but at the same time, how important is good data and understanding how you're getting your data to telling the right story because ultimately you guys were talking about bringing this to the, you know, ultimate decision-maker or setting a strategy ban to, you know, set the, you know, the future with a project or where you're spending or what your revenue is, but if you're relying on data, you drive those decisions, you've got to make sure you've got the right data, right? Totally, totally. I think that's what I was thinking about to talk about is, really, I think, back, you know, just to talk about this question, Jake, and also Laura's question earlier, I really feel like strongly that every team, pretty much every sports organization, even though most people don't use C-core or Tableau, but there are a lot of people understand data, understand the stories and are being very analytical, at least in my experience for, you know, in all the teams I work with, they're always a champion that I partner with, that, you know, who are so analytical, detailed oriented people who, you know, can help me translate the business challenges into what I need to do. And that's always, you know, a big win, I think, winning their trust is always huge. And Jake, back to your question, the good data, back data has become more and more a big issue in my experience. I think in the past two years, what we're trying to do is, you know, having people focus on data operations. So data operations touch when we have an analytical project, they need to understand what kind of data we need to get. Is it sales data, marketing data, you know, attendance data, where we should get that from, and how to transform them into the correct format. How do we make sure there's no duplicates, so we're not double counted, right? A lot of quality control or quality checking steps to make sure data is valid before you even start counting it or analyzing it. So I think that's kind of one most important step and that's what we have done in the past two years is we created this data operation team in the department and there are people really focusing on database management, but folks on SQL, folks on cleaning off the data and make sure it's accurate and, you know, monitor the quality of the data as well. Yeah, when I first, when I first kind of dabble in the tableau and they started to mention, hey, we got to make sure you've got clean data. I'm like, clean data. What does that mean? You know, what's wrong with my data, right? And so I think there's just such a big learning curve, right, to this side of the business, but it's so vitally important as we continue to advance with technology and you know, other infrastructure as we move forward. Eddie, you know, you had a little bit of time on the player side of analytics with the sons when you first started out and again, that's at the beginning episode we kind of mentioned that's where everyone that's where their minds go is player analytics, you know, and there's a lot of similarities yet differences from the player to the business side, but one of the biggest things that I noticed back in my my quick time on the player side was the there's the data, then there's the actionable insights, but to actually coach it and teach it is a whole nother level, right, to get it to the player for the player to understand what to actually do with it on, you know, on the quarter on the field. That's a whole nother ballgame and that's almost, I don't know if there's a comparable to it, Eddie, in a sense, from the business side. Yeah, but there is a little bit, right? Because ultimately, like in both cases, you're, you're usually taking data and insights and trying to communicate actionable information to someone who's maybe not as comfortable, and you get that on the business side too, there's there's managers and you know, executives who are more comfortable or less comfortable with data, you know, you're finding the same thing on the coaching side and player side for sure, you know, I think in general, like a lot of the player analytics stuff is either going through front office or coaches, so that's really your target audience and then it's on them to take the information and and teach it to the players, but yeah, I mean, I think a lot of it is, you know, I think you got to understand who your audience is, their level of comfortability with with the data, how do they best receive it? I think that's a key thing too, you can't just force feed it down, you know, whoever receiving it, you got to understand like what's the best way? One of the things that are interested in and this goes back to even been mentioning about like automating things like, you know, this is this is the case across both, but like that's the easiest way to become someone's best friend is make their life easier, so start there, gain their trust, and then move on to saying like, hey, you know, this is this is interesting, we should look at this, but like start by helping them with the things they're interested in making their job easier first, build that trust, and then you can start to kind of form that relationship in terms of, you know, recommend making recommendations using data and additional things, maybe that they didn't ask for, but I think, you know, sometimes you can make a mistake of coming in to aggressive and saying like, here's all this information, it can be overwhelming and off-putting, and you know, I think you got to be double careful in the player's side just because, you know, in a lot of cases it's not as comfortable, right, and people gravitate to what they're comfortable and familiar with, so it's definitely a, you know, progression in a lot of cases, you know, there are guys now, especially that are more comfortable with the data side and even coming from data backgrounds, so that's of course an easier conversation, but that's not the case everywhere. Yeah, it's certainly, you know, even getting the buy-in to, right, to use the data, I think, is step one, and then, you know, continuing to progress from there, Eddie, as you mentioned, with what do they actually need, what do they want, and what are they actually going to use, making their lives easier. Laura, rapid fire time to wrap up the episode, I think this is always a fun one. This is the best part of the whole podcast. This is the best segment. I might be, you know, biased, but I think it's the best one, so these are going to be the toughest questions you've ever answered in your entire life, so I'm just going to, you know, say that out front, but basically, I'm going to ask your question, we're going to ask you questions, Jacob, ask you question, and the first thing that comes to your mind, that's what we're going with, okay? I have to put on my data, my data hat, so I can ask these questions. So here's my first question for you. Are you a one monitor person, two monitor, or three? Five. How's that set, wait a minute, what? How? So you have your laptop? How? Five? I have a desktop and I have a kind of a rack that can hold four monitors and have another one on the side. I thought I was living a good life with two monitors, me and five, that's amazing. Little do we know Eddie's got like seven, you know? Eddie, that gives me something to aspire to. I have two, but I guess the caveat is my one is like probably the equivalent of two monitors, so it's like two and a half or three is one of those giant ones, but definitely not five, but I could probably find use for five. Wow, now Ben, here's the question, will you ever go back to two monitors? If I have no other choice. Once you've experienced greatness, you're like, I'm that, that's amazing. I want five. I got to follow up standing desk or darkness, which one's more important? Uh, for me, like, and I don't have one at my, my apartment, but I have it work as standing desk, and I miss it a lot because it's so easy to just sit here and forget to like stand up, even when when your watch is like yelling at you to stay. And I don't, you know, like the standing desk is just a lot easier to keep that as part of your day. And working for home is definitely definitely hinder that so I'm standing desk for me. Yeah, me too. I always say I have a standing desk and I always say I want to use my standing desk, but it's just I get comfy my chair on my ball and the next thing you know I'm sitting. You got to have all the mat. You got to have the mat to stand on. That's that is that is key. That is not one of the cheap ones. Like you can't, you can't cut corners on the mat. You got to get a good, it gives me a new found appreciation for grocery store clerks. Saying all day. Okay, last paragraph question. We're going to take it back to Athens to this to the good, the cold, cold brick streets of Athens. Okay. Your late night food choice. Oh, oh, Betty's done. Then, is there a kind of a, I don't remember the exact name, but it could be big mamas. Is that correct? Yes, yes, the burrito. Oh my god. Yes, I remembered it. Yes, the burrito. That's my favorite. Those are those are both class. Those are those are I really want to burrito. It's like you see a big mama's burrito. And you don't think that you can consume the entire thing. And then next thing you know, it's gone. It's like. I got a, I got a, I got to follow up. Yeah, burderina. Convocation center or peed in. I have bad memories, man, from peed and so. All set up and all that. Yeah, no, 25 degree nights on a team. Yeah. Yeah. No, I'll do the condo. That's the classic 1970s bowl arena. Ben. I'm kind of indifferent to honest. I've been with you. They're all, they're all the, they're all the same Eddie. I remember all the same. So Eddie and I were at OU at the same time, who's a year ahead of me. And I remember the set up at the football games and it being freezing cold. But Eddie used to be so quick. Right. He would be come in and like, okay, we need to build this structure, this structure, this structure and this structure. And here comes Eddie. He's like, boom, boom, boom, boom. And I'm still trying to tie my shoes. Not that I was doing that on purpose or anything, but I just, I just started out there. Thank you. Eddie, Ben, last, I know, I know Laura said last question, but last question of the episode. If you could describe what other people think of data and analytics in one word, how could you describe it? Oh, that's good. That's a good one. Other people like from, you know, sales and marketing. So people, people you work with, how do they describe data and analytics? I would say they are, they would describe it as important and helpful. I was going to say trying to work it into one phrase, but intimidatingly helpful, I guess, is probably the, you know, the word. I think sometimes it can be a lot, obviously, but, you know, it's definitely come a long way in terms of just being intimidating to now people understanding it's applicable and important. I'll just pick you back on, on Ben's. I'm so excited. Someone else get like their eyes light up when they, when they see a new tablet dashboard or somebody keeps asking you for daily reports and you're like, you know what? I'm going to set you up a dashboard and you can check it yourself. I was, I was dealing with mind blowing. I mean, I just sometimes they're like, just, just don't even know how to. You might be biased on this call the gate. I know, I know, I know. Ben, really appreciate your time, your thoughts, person. Thanks so much. Thanks guys. Thank you.