 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 O'Brien from Radiant Advisors. 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. 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 helped 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 John O'Brien, the Principal Advisor of Modern Data Architecture at Radiant Advisors, and normally this is where a podcast host would read a short bio of the guests, but in this podcast, your bio is what we're here to talk about. John, hello and welcome. I don't know, thank you for having me today. It's gonna be fun. I'm so excited, and we were talking a little bit before we started recording about how I get to see you in a couple of weeks at Enterprise Data World in Anaheim. I'm so excited. Now, I'm so much looking forward to the event. It's such a great, big event with so many people there. So you'll even hear that that's such a big part of what I do in my career, so. Very nice. So let's talk about your career. So you're the Principal Advisor of Modern Data Architecture at Radiant Advisors. So tell me, what type of business is for Radiant Advisors? So we're a research and advisory services. That's what we deliver to our clients, which are people at companies, CDOs, CIOs, and enterprise architects primarily, but we're always working on, we solve one thing, which is helping these people get their companies to really fully leveraging data management and all the benefits of analytics. So it's a complicated, lots of questions kind of world. And that's, we take the approach where we try to be very, very prescriptive. We're not talking theoretical about stuff. We take them from, figuring out their data strategies to data enablement. In my career, I've built some great platforms, but you build it, they don't always come. So you've got to focus on the people side as well. So part of the research part of that is always learning from the industry, learning from companies and people and networking and talking to vendors. We're also industry analysts. So we have analyst relations with all the vendors up to date on their latest products, their customer success stories and things like that. But that's a big information side, but a lot of it is applied by research for us. Oh, very nice. So tell me, what do you do for radio advisors as the principal advisor of modern data architecture? So I spend a majority of my time on advisory calls. So advisory calls or sessions with our clients, we are going through either parts of their roadmap or their strategy. Like, it's for a lot of times when you do, you build a roadmap or you put an assessment together for a company. You don't just say, okay, good luck, right? It's the hundreds of questions that come up. Hey, we're dealing with this. Should we do it this way, this way? And then we're always, it's about, we'll hear the pros and cons and in your company, this is probably the best thing. Sometimes it's a lot of being that mentor, that guide for companies and being the sounding board. One of the things that I think companies like is that we're fully independent. We don't have partnerships. We're not a reseller. We're only looking for what's the best fit for that company in order to be successful. So most of my time, I would say, I try to split half of my time there. The other half staying up to date with the industry, fast changing, evolving, lots of things going on. And a big part of our role is all the stuff that those people at companies don't have time for, keeping up with all the latest updates, reading every paper out there, talking to these companies directly about specifics. So we do that. And of course the third area is really just taking all of that and putting it into tools and templates and things that can actually help the client. So a little bit of product development. That's our fun stuff with the design side. But the last section is, every month or two I get to go to places like EDW. And I get to present which I'm sharing this research and what we hear at companies. So there's a lot of the, you're not alone. I think actually in Anaheim in September, I'm presenting on the top challenges of data architects. This is what we see them running up against today and sharing what they do about that. So that's gonna be a great presentation, but attending, presenting and then networking. The other big thing is you get to meet so many other people, you get to hear their stories, learn from what they're going through and their experiences as well. And that's research. So I'm dividing my time between learning and researching and cutting through stuff that actually trying to help companies be successful with their data and analytics programs. So that's kind of my day. I love that. And I love that you spend a lot of time learning because it is changing so fast. Tech is just always, it's always something new. Well, and I would say that reading to advisors, right? I started this 12 years ago. And when I did, you shifting careers going out on your own, all that good stuff. The big thing that people should focus on, I focused on is what do I love doing? I love learning, right? I love learning and learning and hearing about this. I love piecing it all together and saying, hey, this makes sense. That doesn't make sense. This is, you know, there is no one size fits all. And so really figuring out, well, what is the rule for when I should do this and that? And then the other half of what I really love doing is education. So, you know, presenting, helping workshops, those types of, you know, formats of working with people. Those were two of the main things that actually set the basis for what Rating to Advisors does because it was based on what I love doing. And it shows. I have, we have data that shows that you are a very good educator. People always love your sessions. Well, you know, that's great. We always take that feedback and try to improve. I've been, you know, I started presenting at conferences in 2001. So 20 years ago, those first three years are hard. And so, you know, for people who want to get into that, just get out there and do it. The main thing is that, you know, I think a lot of people will have that imposter syndrome, I did. But, you know, when you speak about your experiences, that's true. This is what happened. This is what I did. Here's the results of it. And this is what we learned from it, you know? And sharing that with other people, you know, you just start to get into that. So when you're teaching, sometimes, you know, people say, oh, you're the expert. Sure, I've just been doing it a long time, but I'm sharing my experiences and I'm very open-minded to tell me if you think this is right. One of the things with all of our frameworks when I do present them, I'm like, break this, show me why it's wrong. I want to make it better. And a lot of them just have stood the test of time that year after year, more and more people are saying I was successful with this. So, you know, it's definitely, I would say I'm an introvert. So being a conference speaker was a really hard thing. But for a lot of people know that you can get over that. Oh, I can so relate to that, John. I was just telling somebody the other day that the first webinar I ever produced was so bad. It was so bad. But, you know, I looked at things like leave the, you know, the button on. So people, anytime somebody or the beep on, so anytime somebody logged in or out, it beeped. But you keep getting bad. Well, and that's the other thing for people to really take away is just get out there and do it. Right? I looked at my stuff 20 years ago or the stuff 10 years ago when we started and it was the best I could do at the time. And I'm like, oh my gosh, that's terrible. But put something out there, get better. Put something out, you know. One of the things that there was this whole little mantra in the industry for a while about fail fast, you know, we want to fail fast. No, I don't want to do that. So I kind of changed that phrase to say, yeah, I want to fail fast, but I want to learn faster. So that means, you know, go do something, learn from it. The experience is one of the best ways to get better at anything, not reading and knowing and you have to put yourself out there. Yeah. Well, let's get into that. Let's talk about your experience. So tell me, John, you know, when you were, you know, say six years old, you know, was this the dream? I ain't going to grow up and be a principal advisor of modern day architecture. Oh my gosh. Oh no. What was the dream? Yeah. You know, for me growing up, I was fascinated with planes. I wanted to be a pilot. I grew up in Army Brats and my dad was a lifetime military and I grew up overseas and next to air bases. I watched the students taking off all the time. So growing up all the way up through college, I thought I was going to join the Air Force. So I was drawing, sketching, drafting and that actually led into like architecture buildings, homes and I had an appreciation for architecture. And then later, you know, I ended up going to school and getting my bachelor's in mechanical engineering. That's because I was fascinated with how things work. And for me, that also led into engineering as a discipline is a problem-solving discipline. It's a logic. It's methodical and things like that. So those were the skills I got in school. I tried to be a professional engineer for a number of years, but you know, I kept getting pulled back into the data world. So yeah, I grew up around air shows that led to mechanical engineering. Didn't do much of that, you know, but it was really the problem-solving aspect of things. And the thing I look at it today is that, you know, my materials and the parts that I build today are data. I'm still an engineer. We have data engineers, but now I'm a data architect and a lot of boys. So what was that job out of college? So what's funny is in college, I worked for Bank of America and I was a 10 key operator. So I would sit at a machine doing this, you know, oops, there we go, doing this, 10 key numbers on checks at a thousand per hour type of rates. And then they said, hey, we've got this computer in the back room, we're not sure how we can leverage this. So I started tracking everybody's volumes, every branch's volumes, what time of day. What's funny about how we measure data, we used a yard stick on the ground and said a pack of checks this much, that's 400 items. So we'd register 400 items at 210. So I built massive spreadsheets of every branch, every time and things like that and built up this kind of trove of data for most of California, every branch in California. And we use that to say what time should we bring in our workforce, who's the most productive, who's in training, who had the highest quality rates, things like that. So bringing them in at the right times to match the volumes of the day, that was fun. And then from there, we leveraged that to how do we calculate a commission's model, that was fun. And then the operations courier system said, hey, how do we actually re-route all of our drivers to go to the branches at the right time to pick up the check? So we leveraged all of that data I'd been collecting and we ended up cost efficiency-wise like 30, 40% and re-routing optimized the route. So it was fun, and I wrote my first database. I did their direct deposits. So, but the key here was in college, I was a computer phobia. I, those things were hard, it was once again, a little up to intangible type of thing. But whenever you got into the, I need to solve this problem, right? How do I know when to bring in people? Well, how do I know? I could write out all the numbers on paper, but I learned how to do spreadsheets, right? So I built spreadsheets. My data acquisition back in those days was a 1200 bot modem where I'd call up another computer and fetch their files. And then I wrote massive macros that consolidated hundreds of spreadsheets into aggregates. And then we had our workforce model for the next week schedule. So I did that part-time and then full-time while I was in college. And B of A asked me to join their systems engineering team, but I said, hey, I got this engineering degree. I need to go be an engineer. So I actually was hired and brought into nuclear engineering and for engineering, I worked on, get this, instrumentation that collected data about every plant that quit them. I was a controls engineer. And so all of that data, we really worked on statistics, analytics, model like that about plant equipment. Obviously in a nuclear plant, you want to catch it before it breaks. But in building and collecting all of that analysis and data at a time, I get this. I ended up being the, I started out as the analyst on the kind of the data warehouse project. We ended up becoming the team that built it. I led the control systems and we became shadow IT. So I was shadow IT for a couple of years because I'm like, we can build it ourselves. We know the stuff and we did a great job. But then again, I got absorbed into IT. And so I guess career-wise after that, it just went into, I left there to look for new opportunities. The 90s was the beginning of mobile phones. They worked for all of their call records, wrote the decryption routines for activating phones. And but that's where I got into real first-time formal analytics, facts and metrics and things like that in models. And more insights. Did you know that the highest profitable channel in sprint was more about the little cart to the kiosk inside of a mall and not necessarily the big sprint stores? We did more traffic than those, right? So those were insights that went against kind of your normal thinking of things. Through that data, some of the stuff I worked on is a 100 minute plan, a 200 minute plan, three nights and weekends. Those are all things we came up going through the data that was getting bigger and bigger and had a hard time crunching. From there, I went to level three communications. Same thing, but at internet scale, they are the backbone that carries the majority of all the internet traffic globally. So streaming real-time architectures, all for data and analytics to run operations, predictive analytics, things like that. So it just got bigger. Then I started a little bit of consulting on the global architectures around the world and got to travel. What's interesting there is I did get an MBA and said, hey, I wanna be more on the business side. I'm too much in IT. I care about why this matters, right? So I went and got an MBA. And then I ended up becoming after that the CTO and co-founder for a company called Datopia, a startup. So I was a vendor for five years and two people starting out, building a company, getting funding and building large scale data products. We built databases, appliances and did some great implementations. I think one of them, what I remember was British Telecom and other one was BlackBerry, you know, back in those days. And then when I left that, that's when I said, what do I really enjoy doing and started Radiance Advisors? And then just been evolving and working through these implementations, working with amazing great clients, household names on some of the funnest things in the industry from an analytics from how do casinos optimize their floors to how do you predict what's gonna be a popular show or a movie in the entertainment industry? You know, all this type of stuff. Banking, manufacturing and currently I'm working on some pretty large scale decarbonization, right? You know, what we call the sustainability projects using data from plants to take some companies up to the next level of sustainability. So once again, you can still take your passions like I care about the environment, things like that and work on the data side of things and help with the analytics and you know, sustainability models and analytics or things like that. So I do work with a lot of CDOs today and helping them with the business impact strategy along with the technical architecture. And it's kind of been my career that's 35 years right there. Don, that's amazing. So so many things to take away from that. Hey, I- And never had a plan. Never had a plan. So it was, here's what the next step that made sense. Yeah, data just found you. Like it just, it found you. And you even- Pull it away. Yeah. I mean, I love that you didn't get into computers in college like until you found a problem that you needed to solve with computers it was not your thing. Which is so cool. And then you learned it with the passion. Yeah. And that's one of the things that, with all the sessions and classes that I do I'm always telling people, find a project, right? You know, find something you wanna solve and then teach yourself, right? And that's, I lived in bookstores in those days. I read every book on the bookshelf while enjoying coffee and in caves. But still it was, you have to be driven to solve something. And so, a good example is today you don't just jump up and say I wanna go learn graph databases. You know, I understand that that's the solution that's really great for inference analytics or relationships. And then, you know, come up with a scenario and say, well, what data do I need and how do I do this? And that's how you'll learn. Indeed. And the other thing that I love about your story is how many times do you say the word fun? Like you just went after fun, which is just so great because we forget that, right? We get so kind of in life, I think, you know, and needing to, you know, pay the bills and needing to, you know, that we forget to have fun. You know, it just becomes this thing, something that we have to do. And I don't know if it's just me, but you know, when I look around data's in the world all around us, it pertains to everything. You know, it, you know, whatever you think is fun, data's related to it. Right. And so you don't have to like give up your passions to go, you know, do things. It's just that you're gonna, you know, go to, you know, jobs and projects because, you know, it's something you care about, but the main, for a lot of the people out there is your skill set, you know, is gonna be in working with data and working with analytics. How you choose to apply that or where you wanna put that, that's all up to you. You know, what are your passions? So. And they change. Yeah, indeed. I always, for a long time, I always had not anymore, but every job I took was the, okay, I'm looking to take this job because it's gonna help me get to the job I want after it, which is this job is a learning experience. In order to get the job I want, I'm gonna take this one and I'm gonna learn everything. And then you'll get to the next one. You're like, well, to get to the next level, I need to spend a couple of years learning like crazy in this job to get to the next level. So, you know, it's not a gap or a chasm to, hey, I want this job, I can't get there. It's like, yeah, that's your job. But think of that as the job after the one you're gonna have right now. Indeed, that's great advice. So, let's, so tell me, John, you know, as you're going through all these things, I mean, what was your biggest lesson so far in your career? Oh, this is one where I guess it's one of those things that I can look back in hindsight after so many years, but, you know, for a lot of people who are newer, it's harder to imagine, right? We can imagine a lot of people in our generation before, you know, smartphones, right? Some of us go back and go before the internet. So, one of the things is that the rate of technology change is amazing. And so you're thinking that, hey, you're in this thing and this is the whole thing. And the only thing is like, you know, there's something bigger after it. That's, so one of the things that, the way that's a little bit of a lesson learned was that, you know, keep an open mind to what you're, you know, pouring all yourself into. And for me, it led to some of my lessons learned and I am actually practicing and helping companies this with right now is about what we consider open data architectures, right? The ability to be portable. What if you wanted to change to the next best thing in a couple of years? Did you paint yourself into a corner? And how do we modularize things so that you can have your core architecture but you're always experimenting on the side. And you're, you know, you're not putting your core business or your core analytics on, you know, what we call the difference between bleeding edge and leading edge, you know, so managing an architecture because I did get into positions where we're, you know, running, you know, I think some of the teams I ran were like 70, 80 people, data warehousing teams and we take an architecture direction and you kind of go all in on one approach, architecture, technology, vendor. And you really have to question the pros and cons of what happens when I want to change or what happens when I need to change, right? So I guess I got, as I got older, I got better at managing technical debt, you know, just, and it's not that I want to overanalyze. I do tell another good lesson. I do tell a lot of architects, don't spend so much time to try to solve problems that don't exist yet. So let's focus on delivery. Let's get this out there. And it's like, oh, what if this, what if this? And I'm on a lot of calls where they're talking about things. I'm like, those are either assumptions or they're hypotheticals, right? Look at the real, look at the use case. We need to deliver this. And you know what? We'll evolve it, we'll refactor it. One of the biggest lessons I learned which is very hard for IT people that are trained to do good analysis, good design, a good product. The whole idea of going fast and doing it loose, that's just not in our personality nature. And, but, you know, I did live on a project where first mover advantage gave our company market share before anybody else. And they dominated that product and market, which was a big market for a number of years. And then we improved it, but then we exited that market as everybody else came into it behind us. And we were onto the next big thing. So there's a lot of value and speed versus perfection. And so that was probably one of the other biggest kind of lessons I learned. Oh, by the way, do you remember those CDs we used to get in the mail that was AOL 1-800 numbers? Yes. That was the product we built in a matter of months. Oh, wow. All of those were managed. Every one of the sessions in real time provided to AOL, to Earthlink, to others. We did that faster than anybody and it's like, wow, it's, you know, it's flawed, it's not well designed. It's like, we're learning as we go, but first mover advantage is a huge value to businesses. So don't try to solve problems. If we had taken six months to design and analyze this thing, we wouldn't have found that market share in that product. So it's very hard and it's against our nature for a lot of IT who are trained in CMML levels and all that kind of stuff to do good analysis and do a good job versus do something good enough and fast. I think I learned about that later in business school. That's called creative destruction. I'm for it. I've been trying to apply that into a life lesson myself, you know, progress, not perfection. 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 DBTOX for 20% off your purchase. Yep, yep. That's a hard one. It's okay to work on it. Yeah, yeah. Indeed, right? How we learn is making mistakes, right? So as you said, yeah. Yeah, putting something out there sooner before anybody else and capturing mind share and then product share for your companies, the companies of the listeners, that's the value. That's what the business is. Sure. So, John, so gosh, it's starting in data. So early in your career, what is your definition of data? Oh, it's everywhere. It's like seeing the matrix. So, you know, we're in this podcast. Well, you know, the data around us, it's you, it's me, it's this recording, it's this event, it's by all the people who are gonna listen. There's data. So I look at data, you know, that it can be captured, it can be everywhere, but one of the biggest things I think about data is the importance of data modeling. And the importance of data modeling says, I understand something well enough to build a representation of it that holds, that can hold the data, right? Data doesn't just, you don't just dump it into a file or into a table and you play with it. Having an understanding of relationships and rules and, you know, you're modeling people and entities and things and activities. When you put together this mental model, then you can put data into it. And that's one of the big things. It's like, yeah, there's data everywhere, but the important thing is what is your mental model that you wanna load data into? And so I would, in some designs at companies, say, hey, you're currently running your business in this model and kind of represent low data. I'm like, well, what if we ran the business like this? This is blow this data into it. So it does get into different perspectives, right? You know, everybody who's working with data has a different way that they see it, they use it, they apply it, right? You know, the whole, you know, I'm a customer, well, what is finances definition of a customer? You pay a bill. You know, what is marketing's definition of it? I wanna send you information, you know, tech support, customer support, yeah, they call in and have the products. So everybody's kind of perspective of that's a different mental model. And so one data's everywhere, it's all related. You just need to figure out a model that you wanna put it into. And one of the beauties that's going on right now is that they're the models we come up with and document and think of, then they're the ones that AI is gonna say, hey, this is what I see in the data, is it a model? And that's kind of the exciting part. The other thing I really enjoy sharing with people is that I'm a bit of a data hoarder. So I was building data lakes for a long, long time, right? I thought, or get everything in store, everything cause I never knew when I was gonna need it. But the other aspect of it is I, because I'm a hoarder, I also love lots of data cause I'm gonna learn from it. And when you do that, it really changes the whole definition of bad data, data quality. So quite often I'm like, we have this data coming in, it's bad data quality. I'm like, well, what's bad about it? It doesn't match a rule. Well, maybe the rule is wrong and the data's right. So as is data, what I consider immutable data that you capture is really, there's a lot of value in that. And you can question whether or not the rules or the perceptions about the data's right and what is it trying to tell you? Or if data's missing incomplete, that's different, but that also tells you we need to go improve business processes for capturing it. So I like to think of data like my kids. I love them all equally. Sometimes one of them might be a little different and doesn't fit the norms and not as well understood, but I love her just as much as I love my other daughter. So that's kind of how you embrace data and look at quality application. You're gonna learn things from whatever you're able to capture because it was generated for some reason. Indeed. So John, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? So that's actually a great question. Years ago, I think I'm trying to think back on dates, right? There was a big data craze and then we were really peeking out on the AI craze. I don't know what was it? Maybe five, six years ago, like every conference was an AI conference. Today it's a whole new level, but back then it was machine learning AI. And at that time, given the excitement and the applications and how much fun that is, plus my background data science statistics, things like that, I was like, great, reading advisors as a firm could go specialize in this area. But one of the things I also just knew, maybe it was experience and intuition was, it's only as good as the data management underneath it. And rather than jumping on the AI kind of bandwagon, we said, we're gonna double down on data management. Cause what I see at every company I talk to, they'd love to do that, but their data management's not at the level to do this. I mean, so often the data that you collect and how you manage it is what you're gonna use to train these models. So it's not just about garbage data, things like that. It's about bias quite often. It's like, if I take my last 10 years of HR history and use that to train models to do promotions, raises and changes, that model will simply become extremely efficient at reproducing all the mistakes I've made in the last 10 years and apply those. So you really have to give a lot of careful thought about bias, about what the data should be, how well we'd manage it, because it's only that data management aspect that would feed AI. So you could train so that those things. So I mean, to the point that I said, hey, AI is exciting, I'd love to do it, but the hard work is in data management. Now, the inflection point we're at, which is pretty exciting for me, and I think the next several years, my focus is, what if we use AI? We're always doing it for the business. What if we applied that into data management to solve our problems, not sales problems, not finance problems, things like that, but quality problems, governance problems? Well, in order to do that, we would need to build a really good training dataset. But how would we teach a model to do what we do when it comes to data quality assessments, data governance, looking at usage, looking at how people are working with data applying data? So the big thing for me today is, how do we capture all of this active metadata, change the way, it's like, how do we see this in my career? How do I instrument what we do in data management to collect all the data so I can, in a couple of years, train data management AI models? So we had fuzzy logic matching for years. We've had that in the MDM space. We've, not everything is directly related. So we're leveraging graph and semantic models. But for me, the real exciting part is that data, what I see at companies is getting harder. We used to have a monolithic application with a data model we could reverse engineer. Now we have SAS applications for every little niche function of the company. And we don't know what the data is behind that. And so managing that, integrating that, making sense of that is getting harder and harder. And we have to do it now at scale and people want real time. So the future is not what we could do in data management. It's more of the, we need help. Who's gonna help us? AI, managing your data quality check bot or your things that will pop up in the applications and help people do data management better or automated. So I think we're at that inflection point. So a lot of that is related to data fabric work. And that's where I've kind of spent most of my time these days researching, helping companies, finding companies that are leading the way, learning from them as well. So it is pretty exciting. It is very exciting. So then do you think the, if we automate a lot of the data management aspects, do you think then the jobs will decrease or do you think there's gonna be even a bigger need for that to create and manage the AI models? Yeah, so if our workload increases by 20X and that bot can do half of our job, we're still underwater. I mean, the only way to keep up is to, you know, get augmented help, augmented AI type help. So I still think we're gonna be underwater even with all the AI help. Now you, you know, people joke about the, you know, even things with chat GPT or a lot of the ML, it's like, it's already flipped. We work for the machines. We're constantly trying to figure out how to train them, how to maintain them, reinforcement, how to pick the best models, all of this stuff. We're already, our job is working for the machine, those machine models anyway. So our job's not going away. It's just that once we figure out how to train a great bot, it's gonna be able to do scalable workforce, but the hard work is gonna be doing all of that. And, you know, one of the jokes I use in class quite often is just ask people how many weather apps they have on their smartphone. I had at least six, right? I had six models that need to be maintained that are all competing for who's got the right answer. You know, so we may not be actually doing the hands-on kind of work with the data, but we're gonna get help where we can automate and offload more and more of that, but there's no shortage of the work we need to do. The complexity is increasing faster than we can keep up with. And that's why I think it's a necessity. I agree. So then what advice would you give to people who are looking to get into a career in data management? That's a good one. My experience has been to listen, you know, learn how to be open-minded. You know, one of the things is there are so many different opinions about data management, you know. I always love every week I'm reading, pundits against each other all the time. The sky's falling, it's never been better, you know. So just be open-minded to listen to those. And what I like is with a lot of, even in my teams, the diversity of this person's career experience background, their belief system, all of that gives them a different opinion to contribute. And I wanna pull in all of those to have the best kind of final thought, which is my own. But a lot of this, well, also I challenge people that wanna go into data management is to focus on data management principles. The principles don't go out of style, right? And when I talk to different architects, I'm like, where are you on this scale of, you know, openness, manageability, data duplication, I'm comfortable with it. That's terrible, right? Distributed architecture versus centralized. So understand where somebody's coming from and what their reasons are, and then understand how that would influence. So, but the principles don't change. It's just the technologies come and go. The other thing I like to push people when they're like, if you know your principles and you know that this is how you're interpreting that to apply it, you know, but a lot of times you're saying, oh, but the best practices are saying this. Best practices by definition are hindsight. It is what has been proven a high number of times in the past. Maybe what you're doing is applying and coming up with something is a future best practice. So you can only deviate from what everybody's doing to do something if you believe it's right because of your principles. And these are data management principles, right? It simply does, you know, for me, it simply did not make sense to jump on AI because I knew the data management needed to be focused on, right, that was going against the grain. And I would just tell people to be aware where they stand on things, be very open, listen, focus on their kind of principles and be the next best practice. Oh, I am in such agreement with you on that, John. And it's hard to go against the grain. It's, you know, there's a lot of peer pressure. There's a lot of people telling you that you need to fit into these best practices. But I agree. I mean, you cannot be a leader in anything if you're constantly doing what everybody else is and trying to back step and do what everyone else has already done. Yeah. You definitely want to leverage, you know, other people's successes. Yeah. And, you know, something is their best practice, look at their scenario, look at the time, look at what they were going through as a company and their culture and does that apply to your situation, right? So those things are great to say, hey, that solution seems to fit, but when it doesn't and your gut's telling you it doesn't, right, you know, trust what you know is the right thing to do with the data and the analytics. And you were not wrong. We've heard, and I've mentioned it before another podcast that, you know, we've heard so many companies who try to stand up the AI and machine learning and tried to skip the whole data management step and went, oh no, why isn't this data correct? Why is it not working? And then went back and had to go back and go, oh, we need that data management. We need the data craft. We need the data model. We need to have some quality in our data, right? Yeah. I think, so with what we do, you know, Radiant Advisors is that a lot of these companies have been doing data management and BI for decades, right, 10, 15 years. They have an existing warehouse and way of doing things. So when they say, because the world's saying, every, you know, modern data architectures, right? Well, what does that mean? And, you know, have to understand that you're gonna really be shifting from one paradigm to another. And that's where a lot of companies run into the challenges. I, you know, modernizing into the cloud, right? That's a different paradigm. So a lot of companies go there, have struggles and then fall back on prem, or live in some kind of hybrid approach, but, you know, did they fully, you know, understand or have the right expectations what they're doing? There are some percentage of clients we work with that are turnaround. They were at it a year, they got in over their head. They said, we bit off more than we could chew or we're trying to boil the ocean. And so a lot of, you know, what we do in our work is break things down into patterns, architectures, principles, and business delivery and kind of work those together so that you don't get into those situations when you are modernizing, you know, your architecture, your data management policies, things like that into a new world. That's the hard part and that's what keeps me so busy trying to keep up with everything. Well, John, this has been so great. So I'd be remiss if I didn't ask you, you know, if somebody wants to solicit the services of radiant advisors, how would they find you? Well, I think the easiest is, I get a lot of kind of inbound inquiries through LinkedIn. So I'm on LinkedIn. So I get those and respond to those. At events, I tell people just email me and quite a few do, whether it's a simple question or thoughts or if it's services related, that's fine. You know, and that's just John at RatingsAdvisors.com and you'll find me here and we'll get back to you. I think those are the best ways that, you know, if you go to our website, you'll see that we publish, you know, these types of stories and use cases and how we translate that into strategies or frameworks or companies. That's where we try to share things. As well as, you know, some of the good things. So I always talk about the independence in the industry analyst side, but our job is also to help vendors be better. I was a vendor, right? How do you put your best foot forward? How do you connect to the right messaging? How do you build a better product? Because that's what companies need. So, you know, we do help vendors, but our focus has always been on the client side. And when we get into, you know, those advisory, trusted advisor type relationships, they're kind of ongoing. They're multi years and, you know, it's one of the greatest parts of our job when after a couple of years, we're like, you've graduated, you know. You've shared everything with Dan. We got you into the great, you did an amazing job with your platform, you know, congratulations and, you know, we can keep you up to date with the industry updates and trends, but we love when our clients graduate. So, you know, and we're here, it's kind of some joke that we're an insurance policy, but, you know, we're here to help them grow and become, you know, a data management professional, a lead data architect at their companies. And I always look at it as, let's, you know, help you in all the softer side, you know, a lot of data management is influence. It's not authority. It's how do you, you know, get things to happen? How do you make your company successful? And even for us, our mission is helping companies be more successful with data and analytics. Cause I'm like, if they're successful, they build better products. And my life and my family, my friends and everybody's happier for it. They're better products in the world. So hopefully, you know, like today, we're taking a little bit of an area and emphasis in the sustainability world. You know, we feel good about that. That's very cool. I do love that. Oh, well, John, thank you so much for taking the time to talk with us today. Ah, thanks for having me. Ah. Pleasure as always. I really, I always enjoy talking with you. So thank you. And we'll make sure and get all those links posted to the website as well. So. And to- Yeah, I'll see you next month. Indeed, yes. And to all of our listeners out there, if you'd like to keep up to date in the latest podcasts and then the latest in data management education, you may go to dataversity.net forward slash subscribe. Until next time. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity. 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