 Hello, and welcome to My Career in 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 John Ladly from Summary. 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 talk with people to 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. Today we are joined by John Ladly, the principal at Summary, 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. John, hello, and welcome. Well, thank you very much for having me on your podcast. It's a delighted to be here. Delighted to be here. Well, I'm excited that you're here. I've known you for, gosh, over 10 years now, and I don't know that we've ever had that conversation of how you got started. I don't think we ever have. It's a checkered past. I hope this passes the sensors. We'll find out, I guess, you know. Are there sensors on podcasts? I don't know. Yeah. So I don't know where do you want to start? I'm... Let's start where you are currently. So you're the principal at Summary. Summary. Yeah, Summary. Summary. Yeah, it's an Irish name, right? Summary is the closest thing in Gaelic to the word data. So when I say Summary solutions, it's data solutions in Gaelic. And that's because great-granddad is from Tipperary in Ireland. And I visit Ireland quite a bit. So I like Ireland, so I go there a lot. And then I found out I was actually Irish after I started going there, which was kind of cool to find out. Very cool. And it's spelled S-O-N-R-A-I. Yeah, yeah. And that's Gaelic as a language, really has no alphabet. Right. So we mess around with the words to try to get them close. And it's an astonishingly hard language to learn, too. I've tried and given up. But the Summary is actually... The one thing for the listener is, you know, don't go looking in the Wall Street Journal for a story about, yeah, I'm semi-retired now. I've been at this data business for a very, very long time. And Summary is my LLC that people write checks to, you know. And my accountant is happy that I have that or not. That's really the only reason it exists. I quit building empires about five years ago, six years ago. I stepped away from full-time, aggressive work. And the work I do now in the data field is selective, and it's designed... And in the Spiritist Podcast, what I'm doing now is designed to further the industry, to learn different things, to try different things, to look at really hard problems, and see what can be learned from that. Before one day I wake up and just go, that's it. I'm done, right? You know, and I climb into that thing behind me here and go off into the sunset. So... And so for those of you who cannot see currently, what's behind you? What's behind me, as you said, leaning a little bit, is a 1942 Boeing Stearman biplane. It's two wings, as you can see, but it was a trainer in World War II. A lot of people will say two wings. World War I, no. There were still biplanes in the 1940s, and the primary trainer for the United States military for World War II was that particular airplane right there. They built 9,000 of them, give or take, of them. And if you were a pilot in World War II, that's the first airplane you climbed into. And mine has been restored to its original out-of-the-factory condition. And this year, it's 83 years old this year, so... I don't know. It's an 83-year-old airplane, but it's been restored. Yeah, yeah. Very nice. Yep. So how long have you been flying? We'll get into more of your bio, but how long have you been flying? Oh, yeah, let's talk about the cool stuff first. No, I always wanted to fly from a young lad, and I couldn't do it because of some medical challenges and some life circumstances. But at some point, when I was about 30 years old, I emptied out a... This is no lie, a big jar of money underneath my desk where I'd put my lunch money. And I hadn't bought lunches for myself, and I'd quit the golf team at work and I'd quit the bowling team at work. And I saved up my money and I started to take flying lessons at the age of 30. And that's when I started, so I've been flying for 37 years. It was not military, strictly civilian, but I've always had this craving to get into these old classical machines. I mean, there's nothing digital at all about this machine. It is extremely crude. It is the best technology 1935 had to offer. And to me, it's a tremendous offset and escape from the day job. And I started to fly to get into one of these. That's why I started to fly. I love it. So, bearing back then a little bit to the data in your current role and what your profession and what you're doing. Oh, um, oh, um. Back of the mundane. Oh, no, it's not mundane at all. No, I think it's very cool things. And certainly cool things that we appreciate the community appreciates. But so tell me then what is, you know, you talked about focusing on problems and that kind of thing. So what is your work week or work month look like now? I still work hard Tuesdays, Wednesdays and Thursdays. Monday is research and writing day and Friday is don't bother me day. Okay, I, you know, we're recording this on a Friday, but that's because you guys are special. And we, we all go back a long way, right? Back to when it was some other organization. I forget what the other name was with Dataversity. Sure, conferences. Yeah, yeah, Wilshire. Yeah, it was named after a sauce, was it? No, that's Worcestershire. Anyway, yeah, Wilshire. So, so that's what I do now. I do a lot of mentoring, I do a lot of coaching. I work with a lot of executives. Now it's very common for me to speak to two or three CEOs a week now. I work a lot with my peers in the industry right now. I'm right now, because no one's told me it's okay to say it or not. I can't, but in the last six months I've worked with four or five other people that have appeared on this podcast. And they are working with me or for me or we're doing research together or we're doing writing together based on research we've done together. Cause we're trying to come up with a, not a body of knowledge, but a sanity check of the profession in the last year. Or so. So that's what occupies my time. I have clients, but you know, bringing a team of five or six people. I mean, my work has always been a little different. A lot of folks in history, a lot of folks have written books like I have and things that have been one or two person operators and that's what they like to do. I built consulting firms. I brought in four or five, six people on a team and we stayed for a year or two. We did, we were a very tooth and claw competitor for the big five for when I was working in the two of my own company so that I built or helped build. And we were tooth and claw with that type of work. So, you know, lots of people training in salaries and benefits and running organizations and all of that. So my work has been a little bit different. And because of that, I bring different things to the table when there's a conversation to be had about what's working, what's not, what's practical, what's not practical and all of that. So I'm kind of trying to, the culmination of that work for the last 25 years is really what can we learn from that and add to the body of experience that we have with the other people and come up with something that works moving forward. And that's really what we're working on now. Nice. I like it. So when you say the profession, are you talking about all data practitioners or a specific subset of that? No, I'm talking about what we referred to each other as data people, all right? So, you know, there's an enterprise data world conference every year, as you well know. There's the DGIQ conferences. Now, there's other organizations that also have similar themed things. It's just no, you know, I can say TDWI, you won't edit it out, I don't think, or anything like that. There's a lot of out there. And that encompasses, to me, the profession encompasses everything now from strategic planning of saying you wanna be a digital organization or a data-driven organization. And through that leadership structure, which would then of course include chief data officers and the board of directors and CXOs, things like that. And then on down through this, the stack of the architecture, the oversight, the business intelligence part, the data quality part. Now, we've thrown in AI and machine learning, we're sprinkling that in now. And all the way through things like now, the data mesh and data fabric and all the stuff that's related to that, that's all DBAs, data administrators, database administrators, data analysts, that's all our data modelers, that's all our data people. That's the universe that I believe I work in. Like it. All right, well, let's talk about then how you got there. So tell me, John, what was the dream? Like, so I'll say you were six years old. What was the dream? Did you grow up? Did you say, I'm gonna grow up and be an astronaut? I wanna be an astronaut, I wanna be an astronaut. That was the absolute, gosh, honest truth. I was gonna be an astronaut. And my grandfather sent self-addressed stand envelopes. I don't think you're old enough to remember doing that. But that's how we Googled in 1963, is you sent a self-addressed stamped envelope to NASA and they stuffed this envelope full of everything you asked for and mailed it back to you. So you said, I'd like to learn everything there is about the Mercury and the Gemini astronauts and what can I learn? I'm a young man and all this and my grandfather would do that. And I guess that triggered kind of a data research thing for me at a very, very young age. And we would sit there and we would watch the, we would look at the material that NASA had sent my grandfather and I and we would watch the Gemini mission or the Mercury mission or the early Apollo missions. And we would follow the mission cause we would have had, we would they sent us the mission profile and things like, cause it's all public domain, right? And that's why I wanted to be an astronaut. And then I found that I'm blind in one eye and they didn't want monocular astronauts, you know? I had a touch of asthma and they didn't want anyone with an inhaler orbiting the earth. So, excuse me, Houston, right? I can't do that, couldn't do that. So I ended up just going to college and coming out with a degree in accounting and a degree in economics and a minor in philosophy and a minor in music. And that's a lot of, yeah, I put myself through school. Back again, I hope you don't get tired of this cause when you talk to someone my age, Shannon, you're going to get all this back in the day conversations which could get just stupefying at some point in time, right? But when I went to a liberal arts college, it was a flat fee and you got X number of credit hours every semester and I put myself through school. I scraped and saved and worked and I got 19 credit hours a semester and for all eight semesters, I took 19 credit hours. And then in the summertime towards the end of my college the pit, I was some from Pittsburgh and the Pittsburgh economy, the steel industry collapsed in the late seventies, right? So I couldn't get a summer job. So I went to community college in the summertime and I got another 9, 12, 15 credit hours at community college. So I ended up with two majors and two minors. And they couldn't put that on a diploma. They didn't know how to, but that's what I came out of it with. So then I go into just working and everyone, my first job had nothing to do with that. My first job was a disc jockey because I got involved with the school radio station and that's probably honest, just between you and I, let's not tell anyone else's. That's the most fun I've ever had getting paid for anything and if I could do it again, I'd probably do it again in a heartbeat. But I made a dollar and a quarter an hour and they were giving me a whole eight hours a week. So do the math. I couldn't put gas in the car. Anyway, I ended up, it's a long story, don't have time for it. I ended up doing IT stuff, mostly because a friend called me and said, John, my company needs computer programmers. If you can spell IBM, we'll hire you. And I said, I have no desire to be a programmer. And my friend said, but you get $12,000 a year. And I went, did you just say $1,000 a month? And she said, yes, $1,000 a month. Well, in 1979, that's a princely sum. And I said, where do I sign up? And that is no lie how I got into IT, all right? Wow. And then for the next 10 years, give or take, I was a developer and I'm program manager, low level coder, I've coded, I've coded in Assembler and PO1 and Fortran and Coball and all of that. Now, I learned stuff that, I learned that there was a mystery out there. There was a mysterious force we weren't addressing in applications development that was messing everything up. And eventually I figured out it was data. And at some point, I found myself in a small company in St. Louis where it was a defense contractor and they collected data, analyzed data and sold the results of their data back to the appropriate defense agencies, Army, Navy, Air Force, et cetera. So in the late 80s, I worked for a company that monetized data. Now, you're in this business. You know, you've heard that term in the last five years. 1989, I was the CIO of an organization that monetized data. And at that point, without knowing it, I became a data person, without knowing it. And we had all the problems everyone has discovered in the last 10, 15 years. We had data quality issues. We had no consistent models. We didn't know what a data model was, but we didn't have any, right? And so we invented everything from scratch. We had to do everything. There was nothing we could use and we created a big wave bunch of people that, and I guess the privilege of age or something is in 1989, our little business, our beautiful company where we're having fun, we went out of business cause peace broke out. The Berlin Wall came down. A big historical event affected my career. I, you know, like many human beings, the wave of history pushed me in a different direction. So I had to go into the big five to make a living. And in the big five, I learned how the consulting drill and all of that kind of stuff. And I ended up being a data person in the big five. And then I ended up being a meta group, which became Gartner Group. So I was the industry analyst type in that industry. And then I went off on my own at least 25 years. I've been self-unemployed, I tell people. But in that data, but it was about in the late 80s when I became a full-time data person, which was a lot sooner than a lot of other people in our industry. But I can't claim I invented it because we were clueless. We didn't know what we were doing. We were just meeting our customer's demands as the best we could. And we didn't put a label on it. We just did our jobs. We just showed up and did our jobs. So I mean, I wasn't a guru or anything. I just people, in the big five, the partners began to ask me to speak and do speeches at executive breakfast and stuff because I'd done all this stuff. And to me it was like falling out of bed. But to everyone else it was like, oh my gosh, this is nuts. This is the future. So here I am, out of custom as I was to public speaking. How do you imagine now? Well, luckily the DJ thing really helped because I was really a heads down, leave me alone programmer guy. I mean, look, as long as the checks were clearing, I mean, I was in there at eight o'clock and at five o' one when the whistle blew and I actually did work somewhere where we did have a whistle that blew. And at five o' one, I was headed out to the parking lot. There was no hard charging entrepreneurial executive here. I was just a coder guy. But this data stuff, you know, and again, we intimated on this at the beginning. This is society changing. This is an anthropological wave for humanity, this data business, which is why I'm still in it. It's exciting. It's exciting. But we had no idea back then. Other than to me, this was really cool. It was really different and I wanted to run it down. And then the only other thing career-wise really is I was always in the wrong doorway at the right time when someone said, John, we have a terabyte of data to store and we only have 200 gig to do with it. How do we go about doing that? I mean, those are the kind of problems that I was in the right room to deal with. So, you know, we wrote algorithms to do data compression in 1989, which disappeared into history. The company went under and everything disappeared. But we were doing stuff like that way before anyone else was doing stuff like that. Anyway, that's a nutshell. A lot of stories along the way. I will stop here. And- Well, let me, I would say, you know, that you got into data even earlier. I mean, with a degree in accounting. Yes, yeah. Well, yeah, well, yeah, I mean, you know, accounting, you know, accounting, though, from the debit and credit and the financial management side and economics. But we never worried about whether the data was accurate or not when I was an accountant. I mean, one of my first corporate jobs was a plant accountant. I used my programming skills one week a month to hard code COBOL programs to do adjusting entries. I didn't say I wrote good code, but we would change hard code and routines to adjust the books and stuff like that. But the data part, this is an interesting thing for the current generation to understand. If this is the way you want your podcast to go, this is something I'd like to put out there. In 1985, 86, 87, whenever I was in my earlier corporate jobs, or even in the early 80s, we didn't quibble about data accuracy or data quality because the accountant's had controls in place and we had data control clerks in every department. And if we did a batch of data, and that batch of data, the control numbers didn't add out, my beeper went off. Yes, a beeper in the middle of the night, a pager, Google it, young people. My pager would go off in the middle of the night and I would be at work at three in the morning till we found that data problem and we fixed it. And we didn't have, we didn't have to, the way we worry about data now, we didn't have to worry about it. So we had that issue, but we also weren't using data the way we use data now, all right? Which got us into the next, what I call the big ugly lie of the big lie has nothing to do with the last election, by the way. It has to do with the fact that everyone still believes I can take all of our transactional data and drop it into some place. Warehouse, Mart, Lakehouse, outhouse, I don't care. And easily, easily get what I want to out of that data. And the fact is we knew that in 1980, it doesn't work. It does not work, it'll never work. Data quality makes it even worse, but the fact is just the context of operational data makes what we wanna do with data now almost impossible. That's why I find AI scary. Okay, we think we have something that we can do AI with and I don't think we have it. So, and I'm sorry, I went on a soapbox there. I'll get off of it now. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launch pad for career success. Browse the complete catalog at training.dataversity.net and use code DVTALKS for 20% off your purchase. Oh, no, the soapbox is good. It's very, very good. So, but you know, and along that line, so tell me what has been your biggest lesson so far in your career? I've had several epiphanies. I mean, cause at one point, so I can share a story here. I was, when I left the big five, I got the job as the senior director or VP of data at a Blue Cross company. And we had to build what became known as like one of the first big, large-scale data warehouses. This is the early nineties. And I remember going to my boss asking for two terabytes of storage and he almost fired me on the spot. And he said, nobody on God's earth will ever, in the history of everything, till the sun goes nova, need two terabytes of data. And he said, by the way, how much money do they want? And he said, IBM wants $10 million for two terabyte server. And he was like, ah, I just had, you know, running around in circles and smoke coming out of his ears. I guess I'm not gonna get my two terabytes of storage. Anyway, so I was in this healthcare company and I had the playbook for data with me and I was do a data model, agree on the definitions, draw all this stuff. And I went up to a blank white board and I brought everyone in on a Saturday morning cause we were busy and said, let's do a data model like Mickey and Judy, let's do a show, right? And I got thrown out of, you know, but just about got thrown out of town, hard and feathered. I learned really quick that an awful lot of stuff we had been taught was good in theory but had not had any contact with reality yet. What I, my first epiphany was a lot of what we do in, and by the way, I'm not gonna, this is not just in data for a short time because I was a developer. I got involved with software engineering and development standards and the, I was involved with a very early capability maturity model out of, you know, Carnegie Mellon. I was part of that process many, many years ago with software engineering tools, things like that. And I learned from that one too and I learned from the client server a bit and the object oriented a bit and all this kind of stuff. We get these great ideas in this world. And my first epiphany was most of the great theories don't survive first contact with reality. So I became my first epiphany was in the, mid 90s was be very practical. And I guess if I have a trademark, if I have a brand characterization, that's me is practical, all right? That was my first big epiphany in the industry. The second one was coming out of my metagroup slash Gartner group days and going into my own work and still being hung up on installing tools and stuff. The second big epiphany was a lot of vendors really don't care about the success of their technology. They just wanna make money and get acquired. And that was sad, that was sad. Now, we're in a different era now and I think to an extent that's changed but you know, during the dot com boom things, were pretty wild west. And that was my second epiphany, which was do your due diligence, do your due diligence. The third epiphany has been pretty recent. I would call that and the final one here is in the last couple of years, we've been banging away, doing things a certain way. Just look at all the presentations at EDW for the last 10 years, try to sum them up into some certain processes. You'll see some distinct patterns, right? A lot of those patterns, a lot of those protocols that people have tried to use aren't working. They sound like they should work. They've worked once or twice. Our good friend, Tom Redman calls them points of light, right? But on the whole, we're finding out that a lot of what we've been doing is not delivering on the promises we've made. It works, all right? I'm not dismaying our industry. It works. We're on the right track. Data's super important. You need data people to understand that. But a lot of what we have done hasn't really delivered the mail. So my third epiphany is how I've been doing the work I'm doing now, which is what does work? And is there something new we need to do? And do we have to re-skill our profession? And that's my third epiphany, really. Those are the three bellwether things in the last 35 years for me. That's fantastic. And it definitely sets up the your soapbox. Yeah. Well, you know, I was raised in what I would call an industrial middle-class or middle-class house where everyone worked hard hourly. Everyone was in the union in the family, except me. I was the first person in... No, I was the second person in the four generations since the immigration in the mid-19th century. I failed me to get there. I failed immigration in the mid-19th century. I failed me to go to college. So we're talking 50, 60 offspring and all the other offsprings and stuff. And there's only two of us complete at college. And I'm one of them. But what I came out of here was a fierce, probably early on in my, when I was less mature, almost an unrealistic and frankly annoying sense of justice. I'm here tempered now by, you know, the fact that the world's not perfect and the world's just by definition going to aggravate you and you might as well just laugh at it. So, but I do find along the way that I do lean in on this stuff because I just don't like to see people waste their time. Does that make sense? You see someone appointed to do a data governance program or a data architect or something and they bust their tail for two years and do this and do that. And they follow all the protocols we've given them over the last 20, 30 years. And at the end of it, nobody looks at it. Everyone ignores it. And then someone else comes along in two years and does the exact same thing. And you see it again and again and again. And to me, I just, it gets me viscerally. You know, it's someone's wasted their time. Life is precious. You know, if we're going to waste that much time, I think we all just go fishing, okay? And just tell corporate America to stuff it, all right? But there's a job that needs to be done and we've got to figure out how to do that job. And that's what, I'm working on my third book right now. My third book is starting to form and it's going to be around that theme that we've got to learn how to do better. And by the way, it's not just the data, folks. It's also our peers in the business and our constituents in and out of the companies and organizations we work with. All of humanity has to up its game around data. We are in an era here where data is much more profoundly affecting our life than we ever imagined it would. And it's no longer just a cool thing and a way to make money, et cetera, et cetera, et cetera. We are infantile in this world as to what the accumulation of our activity, which is data. Data represents human activity now. It documents human activity real time 24 by seven, 365. And we don't know how to manage that, deal with it, handle it or anything. I think our generation, our data people, we're the first generation to actually confront this and go, ooh, ooh, this is hard. This is not easy. But we go to what talk and say, well, the vendor says all you gotta do is buy this tool and do this thing and get some buy-in. And then the heavens open and there's skittles and unicorns flying all over the place. And that's just all hoo-ha, okay? It doesn't mean it's wrong. It just means we're, I don't know how many other things in human endeavors have every people started optimistic. Hey, you and I were supposed to be having anti-gravity pants right now, right? And we were supposed to be the Jetsons, right? We were supposed to just fly to school and every push a button and we were gonna supposed to have when I was a kid, my grandfather said it's amazing that someday I'll take a pill and it'll give me all the nutrition I need for the day. You know, I'm going to eat hyper-processed food and I will not need to eat anything during the day. And we were gonna be on Mars by now, right? And all of that, people oversell everything. We've oversold data maybe, I don't know. Anyway, that's part of what's forming this conversation here in where I am, I am where I am. I like it. So I'm still waiting for a teleportation, by the way. Oh, hey, the beaming thing, sign me up day one. Right. That would be so nice. I don't know. You were at our event in Washington, DC, right? In December, you were there. I saw you there. Yeah, I mean, I had the going home nightmare was at the airport plenty of time and didn't get home till nine hours later. On a one-hour flight. Oh gosh. Sign me up, Scotty. I'm ready. Yeah, absolutely. All right, gentlemen, with such a distinguished career in data, what is your definition of data and how do you, so what's your definition of data? What's changing, all right? I mean, in the 70s or 80s, data was row and column representations of operational events, right? And then data became rows and columns and also collections of bits and bytes without structure, like a document that represent events and contracts and agreements and expanded. And then we got into this, there's data and then there's information and then there's knowledge and we rode that bus for a while. And I think that's malarkey, by the way. That's total malarkey now. Okay, I mean, I've had CFOs lean across the table and bang on a table and say, John, I don't want to hear about a data strategy. I don't need data, I need information. And I said to one, I said, well, here's the problem in this 20th century. And by the way, before I go on in the story, the CEO had given me full carte blanche to be candid with his team. So you all don't think I'm some kind of annoying person. All right, I might be an annoying person. I just don't want you to think it. So, and I took to the CFO and I said, here's the problem with that. If I gave you all the information you think you need, you don't know what to do with it. You can't handle the truth, okay? All right. And I said, I want to hire Harry Potter. Harry Potter is going to come in and go twink, boom. There it is. Lean your head against its terminal. Everything sucks into your mind. What's different tomorrow? What do you improve tomorrow? Oh, well, I got to think about it. No, no, then you haven't thought it through, right? They're like, no, I haven't. I go, yeah. I said, forget data and information. Now I'm on this third thing. What's data? Data is, as I intimated earlier, it is the record of our human existence in all of its forms. But that means it comes with an enormous amount of baggage. You know, what are the ethics? Who can see my data? I'm sorry, Facebook. So I can have an enhanced user experience. Forget that now. I want to sell it. I don't want, I mean, the minute I can sell my data to Facebook for 80 bucks a year is when I sign up for that. And I drop all of my apps on this kind of thing. I mean, we are, you know, so data is really, to me, it's the recording of our human existence in all the shapes, sizes, forms, and permutations. So that's data now. Yeah, indeed. So tell me, do you see the importance, as the definition is changing, 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? Well, I certainly see data being more important. But I also see data becoming more and more worked into the everyday language of business. So to be maybe scarily prescient, I see the data jobs, the data profession. I see shrinking, all right? A, because the promises haven't been met. And corporate America has no mercy with that. All right, let's just be kind, let's be candid. You only get so many failures, and then they go something else, all right? Second is data is so ubiquitous that eventually, and this is starting to happen, you're seeing this a lot of places, that the whole thing of data mesh and data fabric and having business-empowered business people learning to be that product manager out on the mesh or the fabric or whatever syntax you want to use or something like that. Those, you know, we're not saying, hey, you want a data architect to be a data mesh product manager. No, it's like, oh, you're in marketing. You understand marketing. I want you to represent the marketing data, right? And the AI and the metadata and the technology are taking care of all that nuts and bolts stuff. So I see our profession shrinking. I see the need to be smarter about data and the need to be data aware and the need to educate more people about it. And at the end of the day, I see the need for data skill sets in, you know, expanding exponentially. But I don't think that means our profession grows exponentially because this is just like anything else. Everything starts out specialized and then it commoditizes. And there is no universal rule that says the data business is going to be any different than taking care of your car, making food, electrical. I mean, in 1920, if you wanted to add an outlet to your house, you had to call an electrician because nobody understood alternating current. You had to get somebody else or you burned your house down. Right, right. Now you go to Home Depot and there's a little box and it's got a package and it says, and then there's a little QR code and you do look at the YouTube video and you do it, right? It's commoditized, right? Data's not immune from that. Oh, we must have a normalized database. Ah, the AI will do it. Forget it, right? Yeah, it's gonna do it. I mean, I asked chat GPT two years ago when I was first starting to rumble, I asked chat GPT to do a data model for a specific client of mine. And I had their data model in front of me and it matched about 90%. And it did it in 30 seconds. So, no, it's gonna change a lot. It's gonna change a lot, but it's not a bad change. It's an evolution. It's we're gonna get a handle on it. I think the skills we have, the fact that our data profession gets it and got it a lot before other people puts us ahead. We have an obligation to discern what is gobbledygook to our customers and don't make them endure that. I've had on a client meeting, I had someone all of a sudden thought they had the ear of the CEO and they started talking about a very complicated snowflake schema that they were working on. And the CEO was like, excuse me, how did we get here? I think if we learn to talk right to leadership and be more a part of the business and we lose some of the hubris we have accumulated and we have on there, I think we are in the driver seat. But I don't think our profession's gonna grow exponentially. It's gonna get absorbed. Okay, it's gonna get absorbed into the fabric of the organizations. That might be 20 years from now, third in renews, but that's, I mean, take finance. Okay, yeah, there's the financial people they have the controls. They make sure everything adds up. They make sure that the financial picture represents the organization legally. But there isn't anybody in a business leadership thing that doesn't know what a debt but credit is, doesn't know what a budget is, doesn't know what a cruel is or not a cruel. If you don't know those things, you don't get your job, right? So I think that's where we're headed. I don't know, what do you think? Can I interview the interviewer? No. You know, I- I haven't interviewed, but I- No, I really do think, I don't know if anyone else has said that to you when you've asked me that question, I think the growth of data skills is exponential. The growth of our profession is not so much, not so much, the bottom line it. Would you, what advice then would you give to people looking to get into a career in data? Well, I was thinking that there was a, that that question might come up. And I actually in a flash of uncharacteristic organization wrote a few things down. And I actually do a slide on this for a lot of my talks now. Tom Redmond and I just did an article and it's in the Dataversity content right now. It's about two months ago now. And it's, things aren't working as well as we want them to, what can we do about it? And we stepped back, Tom and I threw everything out. We did a thought experiment, right? Can you do this philosophically or logically? You can take a premise and you can say, well, what if my premise is wrong? Let me posit the incorrect premises or the premise that's different. And we posited that and we used that the way to run down all kinds of scenarios that happened in our industry. And what we came up with, was some common things that we see whether you accept the premise that data management works or you accept a premise, which is not true, but you're thinking this stuff doesn't work. We got to try something entirely different. The one thing that came up a lot was that there are innate skills required in any human endeavor to make that endeavor work. And those innate skills are missing in data folks. Now, Tom's new book about people and data is wonderful. And he touches on this, but just to recap, because this is something where Kim and I have been doing for a couple of years, okay? First of all, our profession needs to learn how to communicate better, okay? I would give, on average, every team I've worked with that are just pure data people who are running the data governance program or setting up data management or working on it, I would give them, at best, a B-minus in communication skills, right? Where we've been successful is there's been that business sponsor come in and they've done the communication, okay? I would say there's essential business skills missing. When you do a meeting, you take minutes and you send the meetings out. You send the minutes out. I do a lot of firefighting. I do a lot of coming in when things have gone off the rails right now. That's kind of most of my work is we've tried this four times, John. What are we doing, Ro? And one thing I go and I go, let me see the minutes. Let me see the notes, the meetings. Let me see the admin flow of this now. There aren't any minutes. Oh, are you kidding me? All right, learn how to be a business person, okay? Oh, we don't have a tool to record the workflow. Oh, well, too bad. Suck it up, buttercup, put it in a spreadsheet. Okay. Learn logic. Janet, did you ever take a symbolic logic or philosophy or logic or the math of logic in school? Yeah, that's a fun course. I've never talked to anyone who didn't like that course. Oh yeah, absolutely, yeah. Yeah, well, I hear a lot of people express things as fact in an architecture or something that if you would mathematically reduce their argument, it's not a valid argument, right? It's a lot like listening to the heavily tilted news media, whether it's right or left in our world right now, the divisive news media, it is the fact that you will hear them say something and you go, wait a second, I studied logic. And when they just said they would get a C or a D on our logic test, okay? So that's the next thing. And the last thing is just learn to talk about your business or your organization. Don't sit back there on your ivory tower and saying, well, you obviously don't understand, but I have the magic keys to the kingdom. And if you just do what I'm going to tell you to do, it will all work out well because you don't have the right context there. Okay, you're separating yourself from your peers, you're separating yourself from the practical. And the result you're gonna get is a textbook generic philosophically correct result that may not work. And that's, so those are things I actually tell everybody all the time. So that's a great advice. I try. Very good advice. I try. John, it has been such a pleasure. I'd be remiss if I didn't ask how people would get in touch with you if they wanted to reach out. I have a website after a lot of research and investment in marketing of smart people, people smarter than me, we decided to call it johnlandley.com. There you go. I have a website called johnlandley.com. So it's not hard to find. It's got some good content on it. It's not up to date, but that's a way to get ahold of me. You can also email me at johnatladley.biz. I'm no longer on Twitter. I don't do Instagram. Also LinkedIn, I'm on LinkedIn. So that's the ways. Or just go down to an airport in the St. Louis area and hang out around. I'll show up sooner or later in that blue and yellow beastie that I fly. So. Oh, I love it. Well, John, thank you so much. And we'll get those links posted to the podcast site as well. Sure, sure. We'll have those to that. Thank you so much for chatting with us today. Thank you, Shannon. And thank you, DataVersity. You guys are awesome. And we've had a good 20, 25 year run since we'll share and I'm hoping for many, many years of it. Likewise. All right. Well, and to all of our listeners out there, if you'd like to keep up to date on the latest podcast and in the latest in data management education, go to dataversity.net forward slash subscribe. Until next time. 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