 Hello and welcome to My Career in Data, a podcast where we would discuss with industry leaders and experts how they have built their careers. I'm your host, Shannon Kemp, and today we're talking to Christopher Berg from Data Kitchen. 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.dativersity.net and use code DVTOX for 20% off your purchase. Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at Dataversity, and this is My Career in Data, a Dataversity 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 dataversity.net forward slash subscribe. Today we are joined by Chris Berg, the CEO and head chef at Data Kitchen, 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. Chris, hello and welcome. Hi, Shannon, how are you today? Thanks for having me. Oh, thanks for being here. So excited you can be here. I had the pleasure of introducing you at a recent Dataversity Demo Day, and as I was reading your bio, I'm like, oh my gosh, you've done some really cool things. I can't wait to dive into it and hear about how you got into it. Yeah. So, well, let's start where you are now and what you're currently doing. So you're the CEO and head chef. I love that title at Data Kitchen. So tell me what type of business is Data Kitchen? So we're a software company. We do some data work on the side, but we, and I guess we've really been devoted to solving a problem in data that's not with data. It's a problem in how the teams work with data. And so we've called that idea DataOps, and it came really from my career and my co-founder's career. And so we've been, you know, I guess the short speech is that I think data now, like people are frustrated. I don't think they're being very successful. I think there's a lot of waste and inefficiency. I don't think it's solved by buying a new ETL tool or a faster database. I think like a lot of cases it's actually solved by how the people work with each other and how the people work with the system as a whole. And that it's taking a series of ideas that were started up in manufacturing with Toyota and went through software development with Agile. And we sort of took those ideas and have nested them in the data analytics world. And we've got some software to help, but we also have written two books and have a training program and manifestos. We have a little content farm that talks about these ideas. Cool. So how's the data kitchen CEO and head chef? What is your typical week look like? What are you doing for the company? Oh, so I guess to me, I think leadership is a service role, right? And so I try to do what other people don't. And I try to set it up so my team is successful, my customers are successful and we're making the right, you know, big picture calls. And so, but I also am an engineer, right? So some of the time I miss coding. So I find ways to code a few hours a week if I can. That's always like a pleasure if I can actually do technical work because I miss that. But I find my job is really just talking. It's communicating. And so that it's been a big change because I spent sort of the first part of my career as a software engineer, sort of writing code for a laboratory. MIT, NASA has some startup companies at Microsoft. And then I got into management in the data field relatively late, like 18 years ago and found that that was just very challenging. And I've been kind of working on solving that challenge ever since in various different ways. So it's a lot of communication and leadership, which is a very different set of skills than the sort of heads down. I can make the machine do what I want and I'm really happy to spend eight hours doing it. And it's just a very different set of skills. And that's something I had to learn in my career. Well, you mentioned some buzzwords already. So let's, let's get into how you got to where you are. And then I've lots of questions to follow up with. So, so tell me Chris, so when is this, was this the dream? So say you're six years old, was this the dream? Like, I was going to be. I was going to go to space. That was the dream, right? It transitioned for a while and middle school and high school. I wanted to be a war photographer and be one of those cool guys running around and then I then sort of, you know, I grew up as a working class kid in Wisconsin, right? So I worked my way through college. And then I, you know, I bounced around, I did a lot of math and science things. I sort of took a lot of liberal arts classes. And then I just wanted to get out of Wisconsin. And I wanted to fly in a plane. It was very important for me. I had influence on a plane or really left Wisconsin very much. So I went to the Peace Corps and I was a teacher for two and a half years in a rural school in Botswana. And so I taught math. And that was just a great experience for me. If anything can help your communication skills, it's teaching. It's teaching middle school and high school kids. So I learned quite a bit there and I came back and then I went to graduate school and I sort of fell in love with, I realized that I was sort of tired of being poor. You know, it's been almost seven, eight years kind of living off of my own, you know, kind of being financially on my own. And then second is I just really got interested in software and computers and actually AI. So this was a very late 80s. So I went to Columbia for graduate school and then I was got a job for five years at this project from a laboratory at MIT and NASA to automate air traffic control to actually learn how to sequence and space aircraft faster. And it turns out that I just read an article in The New York Times that we need that more than ever because a lot of air traffic controllers are retiring and they've had an increase in accidents because they're working overtime. So automation can help and I learned a lot of lessons about working on teams. And then, you know, the internet revolution happened. I thought I'd get rich and retire in two years and 96 that didn't happen. And so I've been, you know, on this career of being technical and then sort of talking and managing and leading. That's sort of been my career cycles between individual contributor and team lead and working my way around that. I mean, so, okay, so you decided, you know, you go to the Peace Corps, which is very cool, great experience teaching math. So how did you come back and transition into getting interested in and it was just you just decided you're going to pick up any degree in for your masters. I thought I was going to get a master's degree and then like go back and teach again. Like I'm getting a master's in math. I'll go teach again. I like teaching and then I got to New York and I like I really had no money. And like it was very tough to be in New York and poor. And so I just like, you know, I worked my way through college and it's like seven or eight years of like really like not may have a lot of money. And I was like, I like teaching is great. But then I sort of got frustrated with teaching and I just really got into the. But after some, you know, have a son now and I had my son turns out the same way I had a lot of confidence that wasn't backed up with any experience. I just thought I'd be really good at it. Like, and I have no experience. Honestly, I was like, I'm going to be really good at computers, even though I kind of hadn't done that much before then. And, you know, turned out always right. And I guess, and I don't know if that's some genetic thing or some male thing but like, I have a 23 year old son who has a lot of confidence with very little actual experience to back it up. Oh, I love it. So, okay. And you got into AI. Was that before you got to the MIT project or. Yeah, I was sort of this before. Yeah, I took a lot of sort of machine learning and neural networks courses in graduate school. And then there wasn't at that time AI was very unfashionable. It was in the winter and what's interesting from my perspective about AI now is that I really was trying to with network computers and work stations and algorithms, trying to get a computer to do what an air traffic controller does, right? It's kind of like self driving, self driving air traffic automation, right? It's the same sort of problem. And what we learned is that you can, you can solve it. It's sort of 90 85 90% of the time, but every time you want to go up to the other next percent, it gets almost asymptotically harder. It's like really hard to go from 90% accurate than 91 to 93. It's super hard because the context of the problems big, it becomes social. It becomes meaningful. It becomes things that are unseen before. And so we went from like we're going to displace controllers to we're just going to try them. Here's the runway you should go to and here's the plane you should follow and just limiting the world and giving them a little advice based on what the computer could get the big picture but and giving them control and thinking about more as an advisor than a dictator. That became a very important touchstone for me and I see it now. Also, when a lot of the talking AI, it's really easy to get a 80 90% result but actually get it right. Like chat GBT self driving cars it's like amazing but then you're like it's it's wrong and like it's wrong at various times and so it's just it's hard to get it. That sort of gap to making things that are actually competent as opposed to skilled are it's really tough and AI and so in some ways I got frustrated with that. And I left. And so now that the AI is back and sort of feels like it's the girlfriend I had in college who I dumped. And now as a major movie star and like, oh, is that one. That's funny. Yeah, because, you know, accuracy rate data quality so important right because there's major consequences, if it's not there and something like that. Yeah, it is and it's also, I think the idea of sort of magic and people like the idea of magic and AI and it can tell you and maybe AI is going to magically do and a lot of vendors do push magic beans which I think is unfortunate. But I think like a lot of things you can help people be more efficient in their job. You know, algorithms and AI in general can help people doing governance faster do it creating. We have technology that you could broadly classes algorithms and AI to help people write bottom any data quality tests right there's a lot of things that you can do but it's it's an it's an advisor, not a dictator. It's always going to go wrong at some point and you have to monitor and control it. And I think that's the theme I learned in sort of 92 and here it is 2023 and it's the same, it's the same stuff and I think it's going to be that way for a while. And there's nothing wrong with that, you know, it's just more on. Yeah, I agree. Okay, so where'd you go to from there. Oh, so, like I said, I joined a couple of internet startups because it was some of which were terrible and some of which were interesting. And then I decided more that I wanted to be a manager. And so my mind worked faster than my fingers, and I was like, and that took me, I think like a lot of people in a technical role. I was kind of thrilled with managing my first small team, and I went from kind of individual contributor to tech lead to it took me a good eight, nine years to get that right out of my head. Because I was sort of the person I wanted to do two things I wanted to be the person who wrote all the cool cool stuff and did all the cool work and tell people what to do. I got a job where I was a director of engineering and managing a team and I stopped doing technical work and I spent about a decade, honestly just learning to be a good people manager. And that's a very, some people annoyingly are like absolutely great at it. And like they're just good at it and it's like really annoying they don't aren't trained they're just like so good at it and like it's just really drives me nuts. I'm married to one and she's really good at it but she's a teacher. She doesn't want I'm like you have leadership skills coming out of coming out of your sleeve and I have to struggle to learn all these things it's really annoying for that. But like it can be learned and that's I think a problem with a lot of people in data, or people who have these odd set of 2% skills where they can stare at a computer screen all the time and and really get into it. It's very difficult then to take that knowledge and transfer into leading teams or leading functions and organizations it's a really different mindset. And it's like it's like going from a to it's going, it's like going from a to b or a to z is just so different that a lot of people struggle and it took me a while. Was it just through experience and trial and error. trial and error and you know there was a book on 15 years ago called emotional intelligence. That basically talked about this idea that you have intelligence IQ and EQ, and that to manage people it's all an EQ role it's not an IQ role. And that was a big thing. And then I started to learn I took some communication training classes, like how to deal with conflict how to talk media training like I started to do a lot of sales and communication became very important. And so, in my role now I think the biggest thing I do is I'm sort of chief storyteller. I tell stories because trying the purpose of why you want to solve a problem with technology or why should you stay at this company and go to somewhere else. Those are really important stories and a lot of times people aren't particularly we're very built to listen to stories and respond to stories, as opposed to listening to sort of rational discussion or even thinking about data. And so, you know, which is odd thing to say but I think that's that's the way we're built as people. Yeah, so then where did you go from there. So I went, you know, I've kind of stayed in this sort of Massachusetts startup ecosystem right so I was in a startup. And then in 2005, the startup that we had met a guy through another person who was had a startup that did analytics for healthcare, and he was a doctor and he was a company, a small company, a couple of people had and had bigger dreams. And I liked him because he built a company without any outside funding. He was a bootstrap guy. He was smart, went to Harvard Medical School. And so I worked with him for seven years and we built that company up to about 50 people and then we sold it. And it was really my first experience to data and analytics and as a full time job. And I had, I had been a software person and software teams and I had software person arrogance. And honestly, I took the job because my kids were like five and seven and I wanted to get I wanted to eat I wanted an easier job that I could like just do and like at home and play with the kids and and it turned out to be actually incredibly hard. Right. Because things were breaking and we could never go fast enough. And so we had customers, you know, thousands of people using our analytics and I was the chief operating officer so people would call me when things were going wrong. And I really I didn't like that like when things go wrong and I would agree with them and say this is wrong you're a moron and I go yeah we are more than that is wrong. And, and I did like a lot of initial managers did I sought to blame people. So I fired a bunch of people probably unjustly, because they like we're working smart, and then things were still going wrong and the other part is we just, we couldn't go fast enough for what our customers want. They wanted perfect insight and in terms of dashboards and we were integrating much as a data. They wanted to have a bunch of questions. Like I had people who did data, data engineering and data science and data visualization and business analysts all sort of working for me. And we just, it really wasn't a great job because we were caught between the sort of silly and grim this right the solo of like errors and problems and incorrectness and the grimness of like you can't go fast enough. Right we're caught. Right. And like, and that's a very depressing position to be in to be between something that you're going to you're going to fall off either way. And it's frustrated me, frustrated my wife, because I was complaining a lot. And I like, there just had to be something here and it was like, I started to think about what we did as a factory. Right I started to think about, okay, we're manufacturing insight we're manufacturing data. What do people who've done manufacturing lines have done so I went and read the timing and I told you a production system and the machine that changed the world. And they had this very keen insight that floored me and said that 96% of the problems are in the system and not in the particular not in the person there in and who owns the whole system of work. Well, it's the leader. Right. And so what I realized is that almost all of those things were my fault. And I had to fix them. They weren't the fact that someone just wasn't working hard enough or we hired the wrong person. And that also sold sock because I realized that like I probably laid quite far some people I should know because I just didn't know how to manage them. So I started to and this is like 2006 say, how do I adapt Toyota techniques. So we had a quality circle. And every week we'd sit around and say what went wrong. And we'd have a list and then we'd have one thing that we could do. And in my software background, it was do something and it was a, I was a believer in automation like write, write some code to do it. And sometimes it was checklists that helped sometimes people forgot the checklist after a while. But like that, sort of, and then I realized I needed to have these phrases so I had these. I started to use this term opportunity for improvement, like over and over and over again. And I had to get people to love their errors and realize it's shrinking when things go wrong and blame to be able to be more. Open about problems and accepting. And so that took a while to break that out of the habit of like people wanting to hide their errors and and the shame and blame and and also just even on the other side of it trying to control people's terrorism. Right because a lot of people you're you're in a customer success role you want to help them so you work all weekend, you give them what they want that's great but then you create a lot of what software engineers called technical debt. And so I guess we, you know, I made some progress. We had a sustainable company. And then it realized that the founders of the company were kind of getting tired so we sold it and then there's good outcome for the founders I didn't share as much with our team as I wanted but like and then I wanted to start a company because I've sort of been interested in entrepreneurship and two people from that company joined me. And I think in the beginning, I think we realized that we weren't ever going to. We wanted to make a company that was sort of in line with our values. We want to make a company that we're big believers in like selling things and being able to get value so we've never. We've always worked. We never had any investors we've always sort of grown with customer revenue so we still been, you know, a profitable or mildly profitable company. And so we've grown over the years since and I think that lesson of like having a friend to have a company that sort of in line with your values and the other part is like if we're going to that data people all suffer the same thing that we did. Right. They are things are going wrong and breaking. They don't know why they can't go fast enough for their customers. And like, you know, I said in my talk like we did a survey two years ago and it was actually really good survey I think was sort of 700 people responded we had a survey organization do it. So it was statistically valid and 96% of them said they wanted their job to come with a therapist because it was so stressful. And I was like, wow, it's not 100, but there's 4% of doing that's improvement. And so I don't want to get people down on data careers because it does when I bring this up the sort of failure and error rates and lack of frustration that a lot of people have that you even see talking to people at the data diversity conference. I think there's just a way out. And I think the way out is to focus on improving the system that you work in is focusing on work processes instead of the process of work. And so I think that's really the what we've been preaching and we have got some software to back it up to help help make that happen. And so that that's sort of been the mission is like relieve the suffering that I felt in 2005 that I see generally people have and build a company in a line with our values towards that mission. And we've been pretty consistent from that in the last 10 years. And I think, you know, we've had some circumstances we're not a 2000 person company so you know you can make your judgments on whether that's successful or not. But this is the mission I believe in. And, you know, I have good days and bad days. I'm about rather, you know, some days I at the last day diversity event I hear some, you know, I was and maybe this is to you shouldn't say this but I was in a data products discussion. And you had this wrap sessions were smart special interest group discussions. And they're talking about data products and they were very everyone was saying data products is a what well it's a what a data product is a database full of stuff a data product is a report a data product is a virtualization thing. And I'm like, No, a data product is a how, not a what it's a how you do things. It's a how you focus on the iterative delivery your customer, you want to work on a product that's that's always improving not a project. That's done and there's book and software development by Mick Kirkston called project product that I think encapsulates that idea. And so I get a little depressed sometimes because like people, data people are very focused on the what not the how. And the how is the real problem in that there's a lot of opportunity for us to do more good what stuff if we just focus for a while on improving or how I'm glad you attended that special interest group. I mean that's the point of those discussions right to have those debates and get somebody's perspective to kind of start challenging the norm right like and people I brought it up and people people agreed and like, you know, the reason I say that is I've just been talking about this same idea for 10 years and like I hear, I'm like why aren't you know I'm an engineer I'm like, I figured out why haven't you, and that's like one of the biggest problems and for technical people it's like what seems obvious to us isn't always obvious to other people, and it's crystal clear in my mind. But like you've got to communicate it and persuade and get people on the path and that that takes time and change and that's in any career, if you're going to leave and not be an individual contributor, persuasion and discussion and empathy for people I think is really important because everyone's got a lot going on, and a lot of times people who were who come from a technical background tend to bonk they tend to go like I can't stand how stupid these people are you know this is obvious to me. Why are they doing all this dumb stuff, and then they, they quit or they change careers or something. And so I think that's a really important lesson for anyone who's in data is that we're kind of in the influence business. Right we're influencing people with data and so you know we use technology to do that influence and if you're going to try to influence people to take to not make intuitive decisions and that's an influencing job. You know I, I think you know it just is not just in data I think it's in any career and it's just a human thing right I mean I had, I had to learn that lesson I you know I'm like why doesn't anybody understand this very important thing that is so clear to me and so easy for me to understand but then, you know, I have had the opposite experience right somebody come to me and they're who like Shannon why aren't you understanding this this is so clear to me this is I'm like, that's not the way my brain works. That's just, you know, because you know we all have those strengths and can share those different things with, like you say with empathy and patients, then we can share and build and you know and capitalize on those, those strengths from each of us right. Yeah, yeah, and like, like I appreciated you introduce yourself at the conference I spaced your face. I was like Shannon I thought you're like, I don't know who you were I was like I had no idea who she was I thought you're someone I worked with 10 years ago, and I was like, like blank that you're very kind to me, while I while my brain sort of snapped in said okay, this is who you are. And so I appreciate that and but that's a really that's a good skill, you know, and like, I think it is and that's if you're going to know if it's certainly a good skill, we need people who are focused nine to five on technical things. Right. However, even if you're going to do that, if you're going to do SQL work or data science work full time, you work with others who are a lot of things that you build together are collaborative efforts. And we all kind of work on this shared technically complicated thing. Right. And it's very rare that someone does something that makes an impact just by themselves, you're part of a team building something. And what is that you build well it's a data warehouse with a set of reports. It's a well governed data governance system. It's a software product, it's a car. And I think from my standpoint, this activity of having sort of socially awkward technically skilled, high abstraction people work on a shared technically shared thing, share complicated thing. There is a management set of ideas on how to how to do that. That's probably very different than, you know, managing a play or managing, you know, managing diversity, right. And I think the principles, their principles of like, you know, run it with metrics and try to search for bottlenecks in the system, having a searching for errors, looking for waste honesty. I think our invariant of whether it's like you're making a car or making software or delivering insight to your customer, these principles kind of apply. And that's my sort of belief that those things work and you don't need to reinvent the wheel and data is not different. Data is just another share technically complicated thing like software manufacturing and and get over it and stop being a hero or stop being a hero and working nights and weekends only do that once in a while and stop being fearful and hiding your process and not delivering value or putting your head in the sand. So this is my role. I don't know what else is there. You know, your job is to influence your customer and your part of the team. And, and your product that you deliver in any form is what what matters in the world and just because your piece works. If it's not used. It's not great. You haven't done you haven't done a job. And so that's a that's a perspective of like focus on customer value and and and make them successful is really that's really what it matters and that's what I want more people to talk about and less talk about data products, data mesh, data fabric, data lake house, technology, do you were the sort of techno fetishes and that happens. I think it's just not not useful or but you know that so customers that that's where I am and and and I think I'm right but I don't think it's standard practice yet and data analytics. Visit dataversity.net and expand your knowledge with thousands of articles and blogs written by industry experts, plus free live and on demand webinars covering the complete data management spectrum. While you're there, subscribe to the weekly newsletter, so you'll never miss a beat. You've mentioned, I mean, these are a lot of great lessons and real this passion comes seems to come from some lessons that you've learned along the way and your own challenges, you know, so what has been your biggest lesson so far in your career what was, you know, was it this or was it something that influenced this. This lesson. Oh, I mean personal lesson is just. I can work on it, and I got to continue to work on it it's the it's the sort of growth mindset, the cycles, the cycles of your own failure and growth, or like, are kind of continual in your life. If you know if this if you try you have a risk of failure and like. And trying is all sorts of forms talking to someone you wouldn't want to talk to. Trying a new thing changing a job starting a company talking on a podcast they're all like these things that we try and like we learn and they don't quite do it right. But like, but at the end we learn and grow. And so I think the sort of iterative growth mindset I think is a really powerful thing and also the forgiveness of your own failures is a really good thing to do. And be somewhat optimistic that things even if because I'm a sort of a pessimist sort of trained optimism and trained growth mindset, and if I can keep doing that things get better, not always not perfect but like they get better and so you can. You can keep that in applies to relationships and a lot of different things so just just swinging keep swinging the bad things and you're eventually going to hit something. I love that. So that that's like if if any career lesson it's like, you are going to have bosses that sock you're are going to have like I've had jobs where I within three weeks I realize I made a huge mistake. This is not good, and it's going to be bad and like, I don't want to look for a job, I'm going to be here for a year. This isn't good, or I realized that I'm on a project and it's just it's not working and it's going to it's it's bad, and then we need to back out of this and, and I've had things that are great and that I love right and like we're going to have swings and then sometimes the bad things happen you step back and realize yeah that was my fault. And what can I learn from it, and how do I. And I think any leadership, any position in anything means that you're the person who sort of goes in the cave and finds the bad thing and says here's the bad thing and this bad thing can happen and like maybe we shouldn't have done that or maybe we have to do this to avoid it. Very much so. Oh, thank you. I love that and thanks for sharing those challenges too. And so Chris tell me, I mean you've worked with data a lot throughout your career really from the beginning of your career. So what is your definition of data. The definition of data. So that's sort of quantitative metric measurements of stuff that happens in the world, right, I think that's the way I think of it. It's sort of like the reflection and play dough and me knows cave right it's the reflection of what happens not reality is a reflection of reality right and so and since I think data is a way to perceive the world that is something can be accurate and can be inaccurate. But it is a way to help you, it is a tool to help you make better decisions about your life and your business or your activity. And so but it's not the only tool. What I found is that most people and I've worked mostly in sort of business and government contacts I've worked in others but people look at data in many different ways as part of that but sometimes they look at data as a self as a thing to justify their intuitions. I already know what I'm going to do. I'm going to find in the data where it is other you know I think other people look at the data more directly and say okay it disconfirms and then they change their mind. And but it's part of the process of decision and change and it's only part of it because there's intuition, there's experience, there's peer pressure, there's theories of the world and like human decision making is sort of super, super flexible in that way. So data is part of it but it can play a really strong part in how people make decisions and you know I think likewise I think it also has the same as I've seen the data career in the sort of 18 years that I've been in it go from. It's really gone from a lot of ways as furniture in organizations it's like oh show me the data okay it's like I'm seeing a share it's like okay we got to have data to actually having power in the sort of moneyball idea that's that's a really interesting change data as power and companies that have gotten very big and influential because of their, their data that they have you know specifically the big consumer internet companies. And so that's also another definition of data is data has power that I think is can cut both ways. And so we have to, we have to sort of put our moral lens on it as well and treat it as something that that can be misused, as well as use for good things. I couldn't agree more. Couldn't agree more. So very true. So and and as we, you know, the data becomes more mainstream or more companies are focusing on it. You know do you see the importance of data management and the number of jobs working with data increasing or decreasing in the next 10 years and why. I see that everyone who has a part of the company should be using data in some way. Right. Whether you're working in a consumer business and you've got your retail store, whether you're a teacher, part of your work should be maybe not every day but part of your work should involve reflecting on what the data is telling you and how that can do it. And so there are always going to be groups of people who are who are in charge of the calls of data and integrating it, explaining it, improving upon it, sharing it like and so I think those jobs are really important. Right. And so I think they are not going to decrease. And I do think, even though teams are woefully inefficient, I think, and there's a huge journey for teams to get 10 times more efficient. I think those jobs are going to increase. And I think there's a superpower that I've found is that people who can kind of people who can talk, can think, and who can, I guess, I think of it as code, but really do data. Do data code, think and talk. Like that's, if you can do those three things you are, because a lot of people can talk, but they can't think. They really think and they can't talk and can't code. You know, there's some people that can code and they can't, you know, so like, you got all three, you're like, you're like the superpower. And like, you're, you're, you're sad. That's a really nice segue into my next question, Chris, because you know, you know, if, as people are looking to get into careers and data and there's so many different aspects of it so wherever you want to focus. You know, is that is, you know, how, what advice would you give to people looking into it? Would you like say home all those three skills or what advice would you give? That's interesting. So there's a story. So, yeah, my daughter has a boyfriend, her boyfriend just started teaching at a college. And he's kind of, he's teaching sort of data science, computer science, and he has a sort of a data analysis class for business majors, and he gives his first test, and he goes, yeah, I got scores from 20 to 100. And like, what do I do with that? And I was like, I was not at all surprised because people who are in non-technical fields who score 100 and it's like, it's just super easy for them. They're like, they don't even know why. They like to just, they look at it and it makes sense for them. And like, if that's part of your makeup, that's a good thing. It just sort of makes sense to you and you can use that. And a lot of people in data come into that where they're the ones who are just interested in it. They get the data in the spreadsheet and they look at it. They understand the data. They start doing it on weekends. And they find that that is a, that's a skill and you should, not everyone has it. And it's not that you're, in some ways it makes you, like, we all have different skills, right? But it's sort of a unique skill that you can, that you can hang your hat on and go with. And so if you can couple that with sort of influence skills and certainly technical skills, because there are a lot. Like if you go from sort of Excel hacking to Tableau to trying to data management and the database to data governance, those are all really good things. And I think those are important. There are some anti-patterns and careers and data about people who get really focused and know every, their company data and then just get all bitter because no one's listening to them. But like, I think it's a great career. I find there's a lot more women than men and like there's just way too many men in software. So there's, and I've been personally trying to recruit more women for years. And it's like impossible because only 20% of the software, the computer science graduates are women at any time. And so it's a lot more female friendly. It's a lot more mix of this talking to people and doing technical things. And if you, if you can sort of have a business sense and couple that with technical skills and understanding data, that's also really a superpower. Like if that's, that's really good because it's a, in some ways, looking at a business through its data is sort of an x-ray. It's kind of an x-ray vision, right? And like I once, one of our friends is a teacher and she wanted me to come talk about business to third graders. And I said, put your, go into a store and put your x-ray glasses on, try to figure out where they're making all their money. And like, yeah, those bits of candy right by the cash register that cost a dollar, that's where they're making their money because it really costs them three cents. Why are some things higher and lower? Why are some things on the end and not? You know, restaurants make all their money off of drinks, not the food. Like you got to look for where the margin is generated. And it's like, you know, I think data is like that. It's kind of like, if you can, it gives you a sort of an x-ray into a truth about an organization. And that's, that's a very important thing for organizations to have. So they'll always be in there, always need people to do that because most people kind of don't want to do that. They don't want to do that results. And if you can help, you can really help an organization by being part of a team that is able to give that company x-ray vision and influence the organization to the right way. Oh, so good. Well, Chris, I would be remiss if I didn't ask if people wanted to learn more about data kitchen, how, where would they go? Simple data kitchen.io and data is just the one word data, just Google data kitchen. And we have, if you're interested in some of these ideas of analytic agility, data ops, lean, we have actually two books that are free for download on data ops, several hundred pages. We've had over 30,000 people download them. We have a training program, a free certification program and data ops that we've over 3,000 people take. If you enjoy listening to me more now, I need to take format. We have a manifesto of 18 points that you can look at and sign. So we have a lot of content that helps because I think fairly early to this idea. So we had to create a lot of ideas. And now there's a lot more companies talking about testing and observability and agility and data analytics, which is great. But, you know, one of the challenges of being early is that we had to, you know, like when I went to my first, when I talked about these ideas at the first day diversity conference, no one knew what I was talking about. It'd be like some guy, 70 years ago, like what? What are you talking about? People know a little bit more in a house, which is great. They do. Yeah, yeah, indeed. And I have to ask, Chris, you know, you are in addition to being CEO, you're head chef. So does everybody at the company have these fun names, fun titles? No, not really. I like it because, you know, we like good data people. We made a spreadsheet of company names. And like good business people, we had a couple of ones that didn't work that were too technical or like, you know, and then as so we finally sell on kitchen because it's fun and also because it's, you know, if you've eaten at a good restaurant, the work that a team of people does in a kitchen is really important, right? And it's a lot like a data team, right? Because you want to consistently put out the dishes on your menu with high quality, right? And you want to maintain lots of variations of it because somebody's going to be a vegetarian or not like cheese on their, you know, on their dinner, right? But then you also have to create new stuff because you have to create new menu items. So consistency, low errors, but you have to be able to change things rapidly or create new things. And that metaphor I think really applies to data and analytic teams. You have to put out high quality results, but you have to respond to what customers request and create very rapidly so you can improve your learning. And so I think the kitchen is a good metaphor, the team. And so I think that's, I think it's a good name in that way. And people, it's fun. It's less boring than like, it's a very technical sounding names and they were like, yeah, who wants another technical nerd sounding company, you know, so. I love it as a great metaphor. And so data kitchen.io. Check it out. Well, Chris, it's been such a pleasure. Thank you so much for taking the time to join with us today. Thank you. Thank you so much. Really appreciate it. And to all of our listeners out there, if you'd like to keep up to date in the latest podcasts and in the latest in data management education, we go to dataversity.net forward slash subscribe. Until next time, stay curious everyone. 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.