 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 Evan Levy from Integral Data. Did you know Dativersity offers free monthly webinar series and online conferences throughout the year? Stay in the loop when you follow us on Twitter at Dativersity or on Instagram at Dativersity underscore edu. Get podcast extras and bonus content when you subscribe to our channel at youtube.com slash Dativersity. Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at Dativersity, and this is My Career in Data, a Dativersity 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 Dativersity.net forward slash subscribe. Today we are joined by Evan Levy, a partner at Integral Data, 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. Evan, hello, and welcome. It's terrific to be here. I really appreciate the time, and I'm quite anxious to see where our conversation goes this afternoon. Oh, I'm so excited. And I know you're a speaker coming up at Enterprise Data World in Anaheim, and you've spoken at many of our conferences, so I'm really grateful. I know you are renowned for, you know, everyone's like, where is it? Where is everybody? Like, oh, they're in Evan's session. I hope that's a half truth. I hope that's a half truth. That would be fun. I do enjoy the conference because it's always interesting not only to share some of your thoughts, but also to hear the feedback and the questions. I used to joke with friends. It's like my favorite part of a conference or session is when I call it Stump the Chump, where you get lots of questions and some people are out there trying to stump you, but others just want to know if we have any thoughts and suggestions now, they can thread a needle and solve their problems. And it's always interesting. It keeps us on our toes, and it forces us to maintain our level of knowledge and learn. It does. And yes, I mean, you are, but you are known as a very popular speaker. You know, great topics, great speaker, great content. So always appreciate that. And I'm so excited to use some of that talent today and have you talk about your bio and how you got into data. So you're a partner at Integral Data. So, and that's a little, you're also the owner of these and the sole employee and the, but so tell me about Integral Data. So tell me why you started this company. Well, let me give you a quick, brief background. I am, I've been very, very fortunate. I had a management consulting firm for a number of years and it was acquired back in 2011. I went and joined an insurance company. Was there for a bit of time in spite of getting back into consulting. And Integral Data is me. It's a boutique consulting firm. I hire people as projects require that. But mostly it's about focusing and supporting the clients that contact me and the people that we want to help. And I'm fairly busy. So certainly no complaints in that regard. And got a little bit of a reputation of following because of the types of things that we do. So it was really just a mechanism for me to continue to consult. Oh, I really like that. So tell me about the types of things that you do. Well, it's funny because when, I think a lot of us in the technology world have to describe to our parents or our family members, what is that you do? And I joke, I fix broken data. And that obviously takes a lot of turns. But I think most organizations struggle. Organizations are very, very good at storing data. It's like I've got bookcases behind me and I can jam all kinds of books in the bookcases. But inevitably one of the questions I was asking, classes, okay, could you name the books that you have or even find them? And I spend a lot of my time helping clients do one of two things. One is, okay, you wanna analyze and study what is it you're trying to accomplish from a business perspective and help them not only communicate and identify that, but then understand, okay, if this is the type of stuff I wanna analyze because of the actions I wanna take, I wanna identify trends so I know what to sell or I wanna make sure that my costs are under control. Well, then we have to organize the data so they can actually find it and use it. And then the real game is, how do you not fall prey or fall into the trap of bio-organize the data to solve one problem? Really what we wanna accomplish with most enterprise environments is how do we make that data multipurpose? So it's actually packaged ready to use regardless of the need, whether it's a screen pop-up on an operational system where someone wants to build a report or now the craze with AI and machine learning be able to grab it and use it in the way that you want. So tend to focus most of my energy in the data management space, but inevitably focus and get pulled into the analytics space because of my background in various industries. Makes sense. So what is your typical workplace look like or what's your typical use case with a client? Well, I don't know that I could ever tell you that I have a typical use case. I mean, there's a fair number of folks that show up at EW like me and we are really a product of our experiences. My first job was at Walt Disney World and I was a jungle skipper. The guy that piloted the boat, turned the speed up and down, tried not to soak people and pull out a gun to shoot an invisible elephant. But I mentioned that because it taught me about what is the customer or the stakeholders' expectations and perspective? And in our line of work, we often interact with technology people and we also interact with business people. And my typical day is someone's not gotten what they expected. So I'm gonna get a phone call that try and work through it. The last week I've actually been helping a client refine and improve their development methodology because it was so application-centric they forgot about the data details. They didn't have what the definition of success was. So walking through, wait a second, what defines success? How do we communicate that? Getting people to communicate but also then laying out the pieces and parts but last week I was building a database and helping someone evaluate why a query wasn't performing well and explaining how parallel databases work with road redistributions and things like that. So the center point is really someone's got data on a database somewhere. Someone's got a problem they need to solve and all the pieces in between. So I usually help them with those pieces. I like it. Well, let's get back to some of your bio too in a little bit here. So tell me Evan, when you were very young, say six years old, was this the dream? What was the dream? My goodness, when I was six years old I grew up in Central Florida and when I lived there in the mid-70s the today newspaper actually had a nice little blurb in the top right corner when the next launch was and we go out in our front yard and you'd watch missiles and rockets launch and it was usually once every few months. So of course, like any other six year old, I want to be a fireman, I want to be an astronaut and everybody where I lived actually drove 30 to 45 minutes to work on the cape and the guy across the street, my best friend's dad was a Apollo mission inspector. People up and down the street worked in various aspects of the space program or in the defense. So everybody was an engineer. Did I know what a computer was? Well, of course I was six and did I know what data was? Of course not. Never really touched a computer actually till my first year in college and it was mesmerized by it. Where I went to school I had one of the first PCs and just became absorbed and took everything I could, got an electrical engineering degree and realized that I would rather build software than design circuits and had the good fortune of having a couple of jobs over the summers learning that, yeah, this was really a lot of work that I wanted to do. So I figured I'd be a programmer. The whole idea of selling or interacting with business people I thought was just a ridiculous concept. I was someone that wanted to, I used to joke, Evan, what do you do for a living? Well, if you plug it in, it blows up, it breaks or it fails, well, I fix it. And that's really, I did two things. I built new software and I fixed other people's broken code and did that for probably five or six years. I joined a startup company and in those days it wasn't like it is today where everyone becomes a billionaire. It was back in the 80s, but loved writing code and ended up quickly getting pulled into client activities because I was young, single, had no limitations on travel. So I'd get on a plane when I needed to and I'd go live with the client and build whatever software was necessary because the company I worked for was a database, database computer company as it was once called and they had IBM mainframe connectivity and here I was involved with the internet or the network and TCPIP version. So I built stuff in Windows and Unix or Linux or whatever the operating system built them applications. So they could do that stuff and enjoyed working with customers as many of the people are probably listening that are consultants or have worked in IT groups. It's almost an adrenaline junkies high where you get to focus on building stuff, you deliver it, you get feedback, you go change it and fix it and that's what I ended up doing. And so from age six to my early 20s, I built software. That's amazing. I like that. So tell me, so you got into data kind of accidentally just kind of playing with databases. Well, you know, it's interesting as I shared with you when I worked for the database company, it was terror data in the early years. There were lots of people that knew how databases worked but no one was really focusing a whole lot of energy of so what questions do you wanna answer? And how do we organize the data so you could actually look at it because in those early days, the concept of a billion record table or how do you organize tens of billions of records was not a common skill. So we helped them do that. But what occurred over time is I went from project to project, worked at retailers, worked at telco companies, utilities and others. One of the things that happens when you're on the road and you work Monday through Friday on the road is you don't work 40 hours, you work a lot more than that. So you tend to gain a lot of hands-on experience and much more compressed time than you would in a traditional company. There was no holidays, no time off, no meetings. Literally, you just, you worked on stuff. So what ended up happening is the value that I brought to the table wasn't just how to build code but actually how to use the data itself. So I kind of became a data person because while most companies view applications as their asset, early on, I was one of the few people that actually knew the data. So when I went and worked with a retailer, I learned about what sell-through was, what advertising and promotions were, what pricing elasticity and those details were. So a client along the way said, you know, even if you had folks that you worked with, we would probably want them to join. So me being the very clever person, I was like, well, you know, I can respond to that. I'd like to add some sort of magical plan, but I hired people that didn't want to work with me and I wanted to work with because the client gives the opportunity. But what ended up occurring was, as I would hire these people, what became valuable to the client wasn't the technical skills. It's not that they were disinterested. It was teaching and showing them how to actually use and organize the data. So it wasn't just, hey, help me build a dashboard. It's help me deal with a pricing analysis model. So we learned about pricing analysis and we learned about reclamation, which for the trivia buffs out there, when you work at a grocer, you have to deal with damaged products and understanding the difference between slippage, which is people stealing things or shrinkage versus things that are destroyed, different animals you have to track. And then those days we dealt with coupons. So you learned a lot of business process stuff. You know, learned about the data and the data shared with you, well, is the business process working or not? I know it seems a little convoluted or tedious, but I learned about data because when a customer is unhappy, they tell you, here's what I can't do. It wasn't about the tool not shown a bar chart. It's here's the information I don't have. And you start seeing those things. So learned about things like marketing, advertising, promotion, operations, revenue and financials and went to go work at another company that was a telco and focused where the strength was, which was in product and customer details. I wasn't an expert on phone operations or a USOX or all those other wonderful codes, but I knew how to learn data. And then the people that were with me, we quickly realized the value of our skills wasn't that we brought large database expertise or analytics or code building expertise. We could learn data and we could help them organize it, not to hide it from them, but to get them in the middle of it. It's almost like training a librarian on what the Library of Congress or the Dewey Decimal System is. Well, we coached and helped clients organize data so they could analyze product-based details or customer-based details and event and existence details. And we went from being a technology company into a management consulting firm. So I had revenue analysis people and product analysis folks and customer oriented folks and people that knew banking and telco and that type of stuff. I used to joke when someone said, Evan, did you plan on doing this when you got a college degree? It's like, okay, I wired stuff. I learned how concrete exploded under duress. I learned how to write software. Of course I never expected to learn about business processes. But I think in our world of software, even today, we hear about all this nifty stuff you can do with AI and machine learning. It still goes back to you really need to understand, well, what decision does someone need to make? What action do they want to take? I used to joke that people would ask you about data and you'd have to figure out, well, do you want it because it's neat to know and you've never had it before because a lot of companies, whether you want to call it big data or specialized data or whatever, they're so hungry for information. They'll ask you for things that they've never had and never think about, well, how are they gonna use it? And it's not that they're arrogant. It's that they're anxious. They want that stuff. They have this enormous appetite and they're confident that if they had it, well, they'd find a use for it. And I'm certainly not here to challenge that, but if you ask one question, which is, okay, so when I give it to you, what action will you take? There's always a pause and it's not challenging their role. I'm not a product information manager or product manager. I'm just here to make one better. And that migrated from the neat to know which is, okay, now let's talk about how you use this stuff. And my guess is my training as an engineer in college is what probably was one of the things that helped the most, which is I like to know how things work. So you ask somebody, what's their decision or what's important to them? You inevitably jump into how does this work? And it's not about us challenging how someone does things. It's trying to understand so I can give them the data they want and package it in a way that they can use it to make the decisions so they don't question it. I joke and pardon me for droning on here, but one of the biggest challenge I think we run into with analytics is we're so enthusiastic that we've delivered this data because people waste all this time trying to find it. We don't stop to think, well, wait a second, now that I've delivered it, will they believe it? Because they've never had it before. I mean, think about Christopher Columbus or actually pardon me, Copernicus and Galileo that said the world was round and the church said absolutely not and it was a little more involved than just a slight argument. We deliver data and people won't believe it. So you immediately have to jump into the idea that how do I make it to defend and explain without making anybody look bad? I mean, our job as data professionals is not only to organize and help them get it, deliver it and use it, but our goal is to not make anybody look bad. I mean, it shouldn't be in us and them. It shouldn't be contentiousness and it shouldn't be an argument when we're building delivering what we need enough and tell folks, take a deep breath. People get upset and angry because they don't have what they need and if you give it to them, they're gonna be cautious because guess what? They're not sure. What we're doing is not rocket science but we have to make sure that people aren't surprised. Well, I love that you talk about being an engineer. You're curious about how things work, right? And it sounds like you've used that curiosity just to kind of rephrase that a little bit throughout your career. You're really curious about what the customer wants. You're curious about, and you weren't afraid to learn new things. You take on new things, which is, I think it's also so key to, you weren't afraid to say, I don't know that or I'm gonna go learn that and be right back. I think part of it, there's so many roles in this data sphere or data space. There's a lot of people that just wanna be DBAs. That's wonderful. There's no harm in that. I mean, there's always going to be need for those types of roles despite what all the vendors portray that everything's gonna be automatically managed. And it's come a long way but we're always going to need people to help build and maintain systems. There's always a need for developers to write code even if chat GBT or all the other AI bots can generate as good a code or better. But there's always going to also be this need of figuring out the problem that someone has. And I'll be the first to admit, I prefer to dabble in a bunch of different areas if given the opportunity of only being a DBA or only being a performance and tuning person or only being a data analyst or only being a business analyst. Well, I would rather do many of them. I've had roles where that's all I did. And I think one of the perks that exists in our industry is over time as you accumulate experience, you can now apply that. I think the biggest challenge for any of us is learning and understanding the client's data because you also have to learn and understand all the goofs, mistakes and anomalies that are built in over the generations of systems where data's been generated and then fork lifted and put into a new system and then fork lifted and put into a new system and you gotta figure all that stuff out. So I mean, it's like, yeah, like puzzles. If you're the type of person that's frustrated because all your blanks aren't filled out or you're an anal type person that wants completion, what I do is probably not gonna make them happy. But if you can live with, yep, here's the things we didn't finish today and that's just gonna be our quest for tomorrow, then that's why I like what I do. That's great. And I love that you're teaching your customers how to manage it themselves and what you're doing and explaining and really communicating the what and why. I think there's a lot of unfair criticism actually to the industry where people are portrayed as trying to protect their turf or protecting their job. I guess it happens. I don't see that very often. I think a lot of times we tend to work in management chains and management styles where people say stay in your lane. This is what I've asked you to do. And then a lot of companies, it's not cultivated to go outside your lane and do other things. I mean, as a consultant, you have that luxury. And it's one of those things where I'm a big fan of let people spread wings, expand and learn if they're comfortable with that and can you manage frustration because it's not about the 50 things you goofed on. It's like, did you finally accomplish and learn something? It's like taking a class, going to school. It's you studied, you studied, you studied to finally get the hang of how something works and now you know how to do it. It's the same thing in this environment. I worked with a colleague. He said, you know, this is really cool because we're paid to deliver but we're also paid to learn and the learning isn't a tool as much as it is learning about someone's business, how they define success and picking up speed. So I mean, that's probably one of the reasons I enjoy teaching it at the DJI Q and the EDW conferences because it's really about just sharing years. Well, let me share with you how I got beat up so maybe you don't have to and maybe some of the tricks and tips that I've learned. I love it. You know, it's part of my favorite. It's one of my favorite aspects of my job is getting to work with this community because I found the community to be educating me all the time because everyone is so supportive everyone really helps each other out no matter what job they're in and what company they work for they're all helping each other to solve similar problems and it's just, I love it. It's great. You know, if there's anything that never continues to amaze me or I'm pleased with is people's willingness to share and coach, mentor and teach you. I mean, I run into that with all the clients that I work with. They'll explain here's the way this thing is. This is what obligation means. And the current client is our budgeting process works. I guess I'm always intrigued when someone shows up and they decide to start talking instead of listening. I mean, we all have bad habits, not so much that. It's, there's a lot of information out there and a lot of times it's not written down. So just asking people, like you say, tell me how this works and just listen. It's really phenomenal amount of tribal knowledge or social knowledge that exists that sometimes isn't written down, that's invaluable. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DBTOX for 20% off your purchase. Indeed. So, Evan, so tell me, what was your biggest lesson so far in your career? Gosh, my biggest lesson is don't, well, truly as a traveling person, don't be wedded to a single airline because you'll only be disappointed, but for those of you that see me on Facebook, you'll inevitably see that when I'll have a flight and if the airline is treating me poorly, I'll inevitably have a rant about stuff. I guess my big learning, ask questions and listen to what people have to say. You can learn from everyone and anyone. Don't underestimate the knowledge of an individual, regardless of their title, their role, or what your perception of their experience and knowledge is. Just listen and ask because there's a lot to be learned if for nothing else, the way they respond because you're gonna hear about years of frustration or experience that worked with a banking client in Canada and they were talking about, you know, that project was just a, they went on, I mean, for like five minutes about how horrible it was. And I asked, so when did that project happen? Oh, it was about 15 years ago. And I'm thinking, my God, 15 years ago, you talk about like it's just yesterday, but there are cultures where there aren't risk-takers and if there's one mistake, it literally makes something nuclear or radioactive forever, which is tragic, but some cultures or in companies are not only not risk-takers, but very fear-based. And then there's others that, yeah, we goofed on that. Let's try it again. There's no harm because we're penalized here for doing nothing. We're not penalized for making mistakes. And I mean, obviously you wanna be somewhere in between, but you can't learn about that stuff if you don't listen to what people tell you. It is amazing to me how diverse and different cultures can be from company A to company B or even organizationally. And it never ceases to surprise me. I mean, very often I'm astonished when people bet their career on something. And then I've just made the remarks about how the culture can be so diverse and sometimes not good and sometimes very accommodating to then admit your job on it. I mean, at the end of the day, this should never be about a single milestone. This should really be okay, how do I help get them to where they need to be? My old business part you say, how do we skate to where the puck is? Not necessarily where we want it because it may not go there. And then how do we help the puck get to where we want it to go? We're showing people not only what date is, but how to use it, how to make it simpler. So I mean, what have I learned? Listen, I mean, I hate to be so flip or corny, but it's really, how do you come up with a solution that works within the environment that you're working? Do you give things that people can accept and it not over exaggerate risk or scare them? I mean, coming up with a technical solution is usually not the challenge. How do you introduce it? And how do you get people to adopt it in a way that no one feels a level of risk that they're uncomfortable with? Yeah, no, I don't think it's corny at all, Evan. I think that's a really good lesson. I wish I'd learned it a lot earlier. For me, I was taught to go in and you're supposed to show authority, you're supposed to know everything and be right. I, you know, one of the things that I always start off when I teach a class is, okay, I'm a consultant, which means if I put pauses when I talk and I lower the tone of my voice, people think I know what I'm talking about. It's complete crap, okay? The speed and the tone and the gaps shouldn't matter, but it is rather striking when you're really good presenters. And it's almost subconscious that they will lower their voice. We'll put a bunch of pauses in. A very good friend of mine in college, we had this professor, Dr. George, God bless him. Best professor I ever had, I probably took four or five class from electrical engineering and used to drive us batshit crazy. I loved the man, but he would talk so slowly. It would drive us nuts. So it's, I mean, what's interesting about it, if I play back maybe some of the remarks I've made you, it sounds like I'm a communications person and a psychologist, which I'm not, obviously, but I think part of success when you're delivering something new to somebody is you have to be comfortable in communicating with others and be transparent and not be shy and just be upfront and honest. It's very hard in the consulting world or even the vendor world, if you deliver something new and then you're not around there to help them learn and work through it. I mean, it's like when you get this brand new whiz bang appliance at home, whether it be an air fryer or a new microwave, you're all excited because it's fixed and replaced something that's broken. And then, wow, you know where the popcorn button is, you know how this game works or whatever and you're really excited. And then, okay, let me go to the manual and I'm one of those people, I go to the manual and I'll read it. But then you realize after the third day and the fifth day, all you can do is popcorn, you got all this other stuff you wanna cook. And then you realize what's gonna take me time to get acclimated to how this damn thing works, to actually use it and gain everything I can out of it. And I think a lot of that's the same case when it comes to data. When you're a consultant, obviously there's been it's a being a client longer financially, but in reality, the real benefit is you can ensure that they can use what it is you've built and delivered and give them the time to use those things. I think the other a-ha that I had in my early days of delivering dashboards or reports or applications is in this era of agile and fast delivery or continuous delivery or DevOps or all the various terms, we absolutely don't wanna lose the sense of urgency. We don't wanna lose the value of continuous improvement. But unfortunately, what we lost track of is it takes time for people to learn and acclimate and absorb what has been built for them to actually use it and learn it and then give you feedback. We kind of neglect that part. It's like when you have kids, you don't get the luxury of going from age six to age 12 in 20 minutes. It takes six years and all the trials and tribulations that go between that. And while I'm certainly not encouraging six year iterations for development, I think you have to remember, let's give the customer or the user the stakeholder time to learn and give us feedback and not rush them. I mean, if I talk faster, that doesn't mean you're going to absorb everything. So we tend to underestimate the value of time and it becomes one level or the other, which is it's either too fast or it's too slow. And that would say it's probably one of the other things I've learned over time, which is you gotta get people time to learn the data. That's really good. That's really good advice. So Evan, tell me what then is your definition of data? I'll give you a definition of data. I'll give you a definition of big data because everybody, it's probably not terribly of interest anymore. Data, it's things we want to analyze, the electronic version of what happened, events, counts, amounts, details, descriptions. I tend to look at things in a way that how can I describe it to my mom? Obviously when we're talking about data, realistically it's not the electronic version of it, but given what we do for a living, there's the electronic version of it. The reason I characterize it, you gotta keep it simple. I mean that there's lots of great descriptions from data to knowledge or information, to knowledge, to wisdom. Fine, I'm not here to argue with that, but the reason I also threw in the premise of big data is for many years you'd hear everybody talking about and it's certainly not top of mind anymore, but when people were talking about it, it wasn't necessarily volume, variety, and so forth, it was more content that I currently have access to. So it's like when you talk to people when they're raising families and they want a new house, you almost always know it's for more space. And when people are talking about data and the data that they want, well, they always want more data because they want to broaden the content that they have access to, so they know more about what has happened. They want more history to know, tell me over time what has occurred, and then let me look at all the related aspects and details that I can tie to it. So thus, to respond to your question, I've told you what I think data or what my definition is, but I think it's people's appetite is such that I think we're gonna actually challenge what those definitions mean because we're in an era where the source of data is now changed. We used to talk about in the analytics and the BI world having data from the core systems that I joke in my class, how many of you could add three new sources or five new sources in the next month? Okay, we've got about 500 new sources. And then when someone speaks to them, there's no chance I could have 500 sources in my company. And it's like, okay, let's bet. I'll bet you that I'm right and you're wrong and I could come up with 500 new sources in your company. And that's so many people do you have. And they'll say, oh, we have 10,000 people. I said, I'll tell you what, I'll bet you my house. Now, if I'm right, you have to tell your spouse, hon, I made a really bad decision today and we got 48 hours because I lost the house. I said, never, I'm wrong. I'll give you my house and I will gladly call my wife and tell her, hon, I did something that someone finally held me accountable to and we better get out. Obviously I don't want anybody's home and I don't want to give mine away, but we have to challenge our paradigm on not only what the definition of data is as you so eloquently put it, but also where does it come from? Because the volumes and sources, why is a source not a spreadsheet? Because someone has worked diligently to come up with a new way of characterizing or identifying customer or a customer value score or a success criteria or a metric. So there's all this content out on spreadsheets. Why is that not considered a source and data? And by the way, that's how we have 500 sources because of just all the content generated internally. Then all the third-party content. I worked with a hedge fund and I'm not lying. They had a hundred million spreadsheets stored and no way of finding them. And it wasn't a lie. There's a hundred and there's actually more than that. But think about it's because someone wanted to model something that would occur in a macroeconomic manner in a particular country in a particular set of circumstances. And there are people in this world that are pretty big time analysts that wanna play out what happens if or what happens when. And for those people that understand gaming theory that that's really a discipline about that. Anyway, if you consider there's open data which comes from governments and financial services, financial service companies about here's what's going on in the world. There's all kinds of third-party organizations that will sell you psychographic, pharmacographic and other types of content about human beings. There's all kinds of environmental weather and other details that are available. You can go on and on and on. And I think our challenge isn't how do we store the core content of our company? It's how do we manage the onboarding of an explosive amount of content? I mean, it's interesting. I tend to compare everything to retail because people understand grocery stores or department stores. But think about what folks like a target or a Home Depot and what they went through with the whole online world. Suddenly, there was no limitation to what they could sell. I mean, there was always limitations to the number of shelves and square footage. And even with special order, the number of catalogs they could actually load into a desk and then the skills they'd have to give the individual that you went to the special order to figure it out. But with the internet, that all is gone. I mean, there's practically no limit to what any of these companies can sell. And then of course, Amazon came to the table with there's no limit to what we can sell. In fact, you can put something for sale at Amazon and never talk to somebody. I mean, one of the things that I tell clients as you know something, I would argue our biggest challenge when it comes to data is, yeah, what is it and how do you define it as your point mentioned? But maybe our role isn't data management. Maybe our role is more about analytics because we've built and organized our environments really to deliver reports and dashboards. We haven't really designed and built them yet at least across the board to make data self-service and shareable. And I would argue really the next step maybe is how do we instill data sharing? How do we have our data strategy reflect? How does someone find the data without picking up the phone? Is it possible in your company to publish and share information without you contacting a technologist without a user being able to find it contacting a technologist? I mean, one of the clients I work with now we've positioned this concept of a data marketplace and not a marketplace from a selling perspective but more from a bizarre. I mean, and I don't mean bizarre peculiar. I mean, why do you think about a mid-eastern bizarre? Where people come in to trade, communicate, learn and share anything imaginable if you've ever been in any of the Suks in countries in the mid-eastery you hear about the spice market or the gold market but literally anything. And I would say one of our big challenges is how do we reposition or remodel or remodel or reshape our data environments to support that? Because ultimately that's what I think people want. I mean, data should be an office supply. It shouldn't require me to get on the phone and pardon me for again, ranting on but I think we really have an opportunity to not just redefine it and challenge what a source is but also what are we really trying to accomplish? Very much so, and you keep coming back to that and I love that theme, what is it that we do? It's got that question of curiosity again, right? What is it we're trying to accomplish? What's the end goal? I mean, think about the space program. I mean, they started with, how do I get someone to get a thing to go around the earth? Which prior to that, I mean, people forget that it was what? It was like, was it 19, pardon me if I get the dates wrong, 1903, 1905? And when were you able to put 400 people in the sky, fly them back and forth to Hawaii as a commodity? Like 60 years. And then how many years did it take to actually not put an object around earth but in fact a whole bunch of people and send them to the moon? Really an astonishing, but it wasn't about going around the earth, it was about space travel and now we're talking about space colonization. So obviously what we're talking about with data is not nearly sophisticated but I think we have to challenge our views because we sometimes forget what people really want to accomplish. They just want to answer questions and they don't want to call anybody. They just want to be able to do it themselves. It's like, if we ran companies the way we sometimes handle data, if you needed a pen, you'd have to call somebody and they'd have to order it and they have to go pick it up. It's like, no, I go to the, I just go get the pen. While it is an asset, we really do want to commoditize it. We want to make it simple so anybody can find it, use it and understand its value. Well, Evan, then do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years? It's going to explode. The whole game here is we now not, our goal isn't to organize data so a programmer or a data knowledgeable person can access this stuff. But in fact, how do we educate people so they can share with each other and they don't duplicate the data and they can find and they can identify what it is. I mean, how many people in the 1970s knew what a UPC code is. Now it's built into our phone. I can go up and scan and it takes me some place. With the last 40 or 50 years of knowledge think about what's feasible and what we need to accomplish with data management. I think that there's a phenomenal opportunity here. And by the way, it's not about doing away with our jobs. It's doing away with our jobs as they currently are. I mean, we've got online books, we've got online data, all kinds of content. The value of librarians hasn't gone away. The skill in the field of library science is probably more valuable now than it is. In fact, that's one of the skill areas I think is probably more appropriate to data management than anything, which is how do you organize information? And then educate others how to do it so they can be self-sufficient. Because I get into many discussions about the power and the wonder of AI and machine learning and chat GPT and all these things. Like if you don't have the data, none of this works. And if you can't find the data, none of this works. And if you can't characterize the data, none of this works. People talk about, well, chat GPT gave me an accurate answer. Well, that's because it didn't know the details about that data. So the whole discipline of metadata management or cataloging, it's in its infancy. So do I think it's gonna grow? I think I probably answered that question. Do I think there'll be more jobs? Absolutely. And do I think data management professionals will always be in a role of explaining ourselves and doing missionary work and educating people about problems that they have. They don't even know they have. Yeah. And do I think the challenge is, let's make sure we characterize things correctly. If they don't have a metadata dictionary as a disappointment and an obsolete, but it's not a catastrophe, okay? I mean, we need to sometimes take a deep breath. And again, don't focus on the problem that exists. Focus how you solve it and then get the folks to the next step or next phase. So yeah, I only wish people appreciated it more with sexies, the applications. It'll always be sexy, but if you don't have the data, none of it works. Yeah, it's very true. So you've given a lot of great advice already, Evan. I mean, it just, you know, what any additional advice you give to somebody who's just looking to get into a career in data management or anything they should study, anything they should, you know, aside from being curious. So I have a group of friends I see every day when I take my dog to the park. We're all a bunch of crackbots and we decided I'll throw in a few dollars and buy the lottery ticket. And one of them said, Evan, why should we bother? So you can't win if you don't play. So we've had a good time in just a few dollars. And then, well, Evan, this one's not worth, it's less than a hundred million, why should we bother? I said, wow. So now winning is even important to you for the cost of two dollars. I use that as analogy. You know, you have to appreciate and enjoy that it's a game. And how do we make data easier to use? And whether it's big data or small data, that's not the value. The value is someone's ability to answer a question and take a business action. And you don't know that when you see the data. So don't poo poo a small piece of data over here or really complex and sophisticated data set over there because at the end of the day, it's about the value of the business question and the answer. And that could be staggering. Oh, such great advice. Evan, this has been such a pleasure. I really enjoyed talking to you. Oh my gosh. So many great moments in the conversation here. And I would remiss to ask, if I didn't ask what, if somebody wanted to solicit your services, how would they find you? Oh, well, that would be wonderful, but certainly not expected. I have a website. I'm on LinkedIn. I have an email address, evan, at evanjleevy.com. And I'm sure the podcast will have all those appropriate connections. And I'm pretty good about responding to mail. I travel fair amount, but certainly happy to interact. And like I said, you can also find me on LinkedIn. Perfect. And yes, we will absolutely publish those with the podcast. So, and I'll see you soon in a few weeks. Yes. In Anaheim. Looking forward to it. It'll be a lot of fun. Likewise. So again, thank you so much for taking the time to talk with us today. And to all of our listeners out there, if you'd like to keep up to date on the latest in podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe until next time and stay curious. 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. Thanks for watching. I'll see you in the next video. Bye.