All the programs have already been written (and other bad career advice)

David Sweenor Hello everyone, and welcome to the data faces Podcast. I’m David Sweenor, founder of TinyTechGuides, and your host for today’s show. On this show, I talk with people who are making data analytics and AI and marketing work in the real world. What’s exciting, what’s messy, and and what’s coming next. Today we have a rare treat. We have Michael Meyer. He is Solutions Engineer at snowflake, and today we’re gonna be talking about modern data warehousing, evolving career landscape and data and analytics. Michael spent decades in the space. He’s been a programmer from Data Architect architecture went to the dark side of Product Marketing. Now he’s back on the technical side. So we’re going to dig into data warehousing, how the world’s changed, and what this means for professionals. So Michael, welcome to data phases podcast.

Michael Meyer Oh, thanks, Dave. It’s great. It’s great to be it’s great to be on here. And I really appreciate the opportunity to spend some time with you and kind of talk about kind of the things that I’ve kind of experienced over my lifetime. So yeah, super excited to have you here.

David Sweenor And before we get into that, can you just tell people a little bit about yourself and what you’re doing over at snowflake?

Michael Meyer Yeah, so interesting enough. Currently been at snowflake for about a year as a solutions engineer. In a past lifetime, I actually was a database architect, or data architect, that brought snowflake into a financial company, and so for me, it was, it was very refreshing seeing the product come out, because it was built from the ground up. It wasn’t just a hey, let’s take this software and move it to the cloud type of thing. So that’s my current currently where I am, but I’ve spent, you know, over 35 years now in the world of, you know, programming and data, especially data, intrigued me early on, and has kept me going through very, you know, a lot of different roles over the years. And so it’s been, it’s for me, at least, it’s been a good run. I would say it’s been a very fun run.

David Sweenor That’s amazing and but one thing you forgot to mention, you have sort of an alter ego. You are also an author of one of my favorite books called Joe’s brew reviews. Tell us about this. Yeah.

Michael Meyer So this is, this is kind of interesting. So the you know when I when I’ve always been interested in writing. So when I got out of high school, I had a decision to make, do I try to go into journalism at that time, or do I go in to programming? And it was kind of funny, because from my dad, I kind of had his logic skills and stuff. He was very much a very logical thinker. But then on my mom’s father, my grandfather, he was very much a storyteller, and I enjoyed listening to him. And then, as I’ve gotten older in my years, I love telling a good story. So you know, the writing about the craft beer is more about the people, the historical significance of places and things like that, than it is about the beer I’ve had just, you know, over the last seven years, I’ve met a lot of people just in a lot of small towns across Nebraska, just being able to tell their story and how they got into doing what, what they enjoy doing.

David Sweenor That’s amazing. Yeah, it’s a great read for anybody who wants it. I’ll put the link in the show notes. You know, one thing about the subtitle, Michael, I’m in New England, so every time you see any I see it’s New England beer, but it’s Nebraska. So just, I

Michael Meyer know, yeah, yeah. That is a little bit of a catch there. Yeah, that is

David Sweenor for sure. All right, so let’s talk about, you know, you know, we mentioned that earlier that you, you’ve been in the this space for quite a while, and so you had, you started out in this sort of baby bi, like a developer role. You’ve been a Data Architect, the dark side of Product Marketing. What made you decide to go from practitioner to more of a go to market role? And you know, what surprised you about, you know, being sort of a doer to more of a marketer. You know, builder, builder to to a marketer. Yeah.

Michael Meyer So it was a, it was an interesting kind of transition. So when I was working at the one financial company that I had mentioned before, I had a manager there come up to me and ask, Would you be interested in helping to shape and drive our our data management program? So data governance, data catalog, you know, think things like that, instead of just being, you know, strictly doing, you know, bi engineering and things like that. And I said, Well, yeah, it’s something I’ve never done before, but I would definitely be interested, because it was kind of the passion thing of, I’ve always wanted people to get more out of their data. I wanted them to be able to understand their data better. So it seemed like a, you know, a really interesting role. And so, you know, this leader, she really, she was the one that opened my eyes, because her approach to everything was very she communicated very well. And it was almost like doing internal marketing to a company, right? So we did a lot of different things in which. We built up our data program from creating a fictitional character that helped us kind of show each step of the way from data architecture to quality. So yes, we had his name was Walt, which was kind of funny. Okay, it was not an acronym. It was just this unopposing looking character. And so when we talked about data quality, Walt was the janitor pushing the broom through. And, you know,

David Sweenor okay, so those cartoons I see are true of the data janitor

Michael Meyer job, yeah, and so that’s how we try to get people to really be able to relate to, you know, why are certain of these things important so, so as as we’re building out this, the program I really took a key role in trying to really enhance our communication internally. And so then after, you know, working with our data catalog, which at the time was elation, there just happened to be an opportunity, when I was talking to one of our field the field marketing folks from there, because I had been doing different events and speaking at different events for relation said, Hey, we are looking for a technical product marketing manager, and I thought that would be an interesting way to use some of the past skills that I’ve learned over the years, and also be able to use kind of my passion for writing and storytelling. So that’s that’s kind of how I crossed over to the world of Product Marketing, okay?

David Sweenor And what’s sort of the biggest difference in your mind from, you know, being a practitioner, hands on practitioner, to more of a technical product marketer. Was there big differences and how the work gets done, and what, what the type of work is?

Michael Meyer A lot of it for me was mind shift in the fact that I’m, like, I like talking, and so when I write, sometimes I get a little verbose, right? So you’re, you know, you tend to, tend to think, start writing on how you would talk. It’s like, oh, world of marketing. That is absolutely a no no, right? You want to be very concise and very direct. So I think, you know, that was one of the, one of the keys and changing mindset there, and just kind of how things go together. The other thing was, really, again, was, instead of constructing, you know, the logic of, like, a flow, like, of a data pipeline, now you’re you’re trying to think of like, when you’re rolling out something, what’s the story and what are all the steps that you’re trying to take to make sure that people understand that story that you’re trying to tell about that product? So they took a little bit of time to transition over to that to that mindset. I would say it was, it was a little challenging at first, but the things I really liked about it was, is I understood the product because I’ve been using it for such a long time, and I really enjoyed getting out to events and being able to demo and talk to other customers that were using it. One of my main reasons for getting out of just, you know, the getting out of the world of working at one company was I wanted to work for an organization that’s helping multiple companies with data. And so that was, that was really the intriguing part. That kind of said, Okay, this is an opportunity. And, you know, from that, that’s, that’s why I stepped in that direction. It was really

David Sweenor interesting to me. You know, you mentioned this early on, you know, in the intro, sort of, your passion for storytelling, your passion for data, and so now you jump into marketing and Well, essentially, you don’t have enough characters to tell the story you want, right? You went, you said, Hey, you can be too when you write a novel or book, you have more space when you’re writing a web page or some copy. Do you think you can tell an effective story on a web page, a B to B web page as an example. Oh, no, yes.

Michael Meyer And it took a long time for me to get there, and it was for me, it was really listening to customers. And there would be key phrases that would come out that they would say, and it was those key phrases, if you could use those, especially, you know, within, within what you’re trying to portray, that that’s where you could get the hook. The other thing was, is, to me, was knowing that when somebody looks at a web page top down, you better get them interested right away. And that first little intro. It’s like, how do you again, get to that emotional side of somebody so that they like, oh, wait a minute, yeah, that’s me. I need to read more as to what’s going on here. And so I would say that part probably took me close to a year to really get going. But after spending some good time with some really good mentors like yourself, and, you know, talking with customers, it started to become more clear, instead of just kind of the old ways of thinking of pages, just feature, feature, feature. It’s like, what is that really doesn’t to me, doesn’t do anything. I for myself. If that’s the case, I’ll tune out really right away. I want some. That’s going to grab my attention so that I stay on that page? Yeah.

David Sweenor So no substitute for the voice of the customer. And I think some of those, you know, this key phrases you mentioned, you know, cornerstone of Emma Stratton’s punchy marketing book, who’s also been on the show. So definitely hear you there. So now you’ve, you’ve went back to the technical side. Human and marketing. Now you’re, you know, primo Solutions Engineer at snowflake. What pulled you back? And now, how does marketing change the way you approach, you know,

Michael Meyer your technical role again, yeah. And so, you know, there was, there was one thing. There was two experiences in terms of software companies that so when I was working in the financial company, the approach that both Alation and snowflake took to their customers always had me. I always felt like I was priority to them. And then they always worked very hard, you know, to get the solutions I needed. And so I always thought of them also the community, the user community around them, was always so strong, and so is it was one of those that I’m like, Well, if I ever had, you know, a chance to work at either I felt lucky. And so I’m extremely lucky to get the chance to work actually, at both of them. And so switching back to the solution engineer, more of the technical it’s like, since I, you know, I wasn’t using snowflake on a daily basis for several years, it’s like, oh my goodness. How much has changed, you know, and it just has evolved into this, this massive platform that you can do so much more in. So that was really interesting. And my role is really to help. You know, here in Omaha, Nebraska, I’m helping about five key enterprise customers with their use of it. So right up again, my passion is trying to help others with data. So it’s if, you know, it falls right within there. And so I thought, right that that was really a good offer opportunity. And I’ve known a lot of the people around Omaha since I’ve been in the data community again for, you know, 35 years. So it seemed to be a pretty natural fit.

David Sweenor Oh, I love that it’s you are you are living the dream and then following your passion. There. Let me switch gears. You know, you mentioned this, you know, this great platform that is snowflake, and all the things that are changing, you know, one of the things that’s really topic, does your hot topic is the semantic layer. It’s getting so much attention. Number one, what the heck is it? And why is it heating up now, you know, compared to, like, I don’t know, five years ago,

Michael Meyer yeah, and you think about all the old products, even 20 years ago or 15 years ago, where they were, you know, saying they had to have us. You know, the semantic layer was the key. But you really couldn’t get any traction to have more of a and it will, and maybe someday, but to more of a globally, one that can be shared by everybody. So it’s kind of talking that way, but really 20 years.

David Sweenor Hold on, Michael. But when you say 20 years ago, you mean like the BI, like the BI side Framework Manager. I’m like, Oh, my God, I had to define all these definitions for these weird columns that nobody understood and all that stuff. Yeah, exactly.

Michael Meyer And so people didn’t. Made me think of it that way, but it really was. And but today, you know, the emphasis has can has been there over the years, in bits and pieces, like you said, it’s everybody, platform specific, whether you’re, you know, a thought spot or Power BIs, you know, Q A and different things they tried to do with different things over the years, but nowadays, the difference for AI, you know, we all know, is context. Well, okay, so if I’m going to talk with my data, I need to talk to it in terms of how the business speaks with the data, not technical terms, not how another financial company talks to theirs, but how my organization talks. And that’s really the key to it, is that the fact that now when you go in and ask questions, you don’t get the well, things have gotten better. You don’t get the like, wild answer, like, well, that doesn’t even come close. Or the, I have no idea what you’re talking about, because now you have the ability to really teach it, your your business semantics, and I think that’s really the keys to it, you know, in in past, you know, prior to AI, there are a lot of, okay, you know, natural language to SQL, but it’s this, the idea of semantics and really being able to give more information so that the AI has a better understanding and has a really great opportunity to really be highly accurate, then as to what the answers they are giving back. And I think that’s truly is the key. Now we’re still at the crossroads that everybody we’re getting a few organizations that do believe in having a kind of a universal semantic layer, but we’re far from getting, like everybody on board with that. But I think of it too as a positive in the direction that if I have a semantic layer that AI can talk to, I also want that semantic layer to be able to be used by my BI tools. Two things like that, so that now I have consistency across the organization. I don’t have one group saying, you know, adjusted gross income is this, and another one says, No, it’s this. And having differences because of having different, you know, semantics and even metric definitions around it. And that’s been, again, to a constant fight every organization’s had over the years. So hopefully, as things get going, having a semantic layer, and I personally believe having it closer to the data makes more sense than having to try to, you know, have it defined in multiple tools or constantly having to update it back and forth, okay?

David Sweenor And maybe we could just double click on just semantics, because I think it confuses a lot of people in the days of old, you know, it was mostly taking a technical description of a field in a data warehouse as an example, maybe Ts, and maybe you just say that’s that represents timestamp, or something like that. But you’re talking about more than, as you said, AGI adjusted growth, income, like what? It’s more than just putting human names to these things. Can you give us a little more like, on what, what? What other things are in there? Is it tell you how much calculated, what columns are used like, what is it? Yeah, so,

Michael Meyer so it really kind of works off of, think about, if you know, in terms of, like, a dimensional model. So, what are my what are my facts? What are my dimensions? So, facts being like my numbers, metrics being the calculations that I’m doing on those numerical values, and then my dimensions are things that I want to slice and dice that data by, you know, common things like, you know, daytime, geography, you know, products and categories and all, all those good things, but then it’s really defining them in a format, in a condensed format for context, so that it also knows The relationships between all your different facts, dimensions and metrics. The other thing that can extend a good semantic model can be extended by things like as as you’re working as a team in verifying queries. Let’s say yes, we know that these results are good. So now you give a query a question that was asked in the query that was created and said, Yep, stamp of approval. Well, now that makes the AI even smarter too, because it says, Hey, that question, that new question that came in, looks very much like that one. Now I have some capabilities there. I think in addition, semantic models also need the capability to really be able to define, or, you know, whether it’s at the model itself, or maybe a step higher at the agent, some of your business rules and how you go about doing things with these things, so that, again, it’s, it’s providing it just more information in order for it to really be able to understand and and to deliver, you know, not only you know when you’re, like we said, answering the sequel part of it, and coming back. But what about beyond that? So, you know, you get answers back. You say, Okay, well, that’s great. I got a chart, I got some data, so I got the what, but why? Why did this happen? So being able to go deeper into that, that why, and helping guide, you know, these llms as to what’s important to you from a why perspective.

David Sweenor And then maybe another question, how does one go about defining a semantic layer? And what I mean by that is how much of it is sort of automated, and AI just does it, and how much of it is human in the loop, if any you know what percentage of like, 20% human, 80% ai, 9010 like, like, what do you what is it? How do you go about this? Yeah, I

Michael Meyer would think, you know still, nowadays, it’s probably at least human wise. It’s probably still about 70% if you want a good semantic model, because, again, you’re trying to take the knowledge of those subject matter experts, and really, you can have aI generate descriptions and stuff. Yeah, they’re going to be okay, but they’re not going to be like somebody that’s worked with us with that data for several, you know, many years, and be able to tell the you know, what’s going on. I always think about, I worked with a person early on at a leasing software company, and, you know, when we wrote documentation, stuff and stuff in the code, it was it was okay, but a lot of times we would go back to this person who had so much industry knowledge that we would tap in on that and make sure, like in designs and stuff, that that we had that lens, and having that really specific lens Just makes the experience much better for the end user, again, for those business users that are using it. Because, you know, the one thing that’s always tough with any technology, AI included, if you step into something, ask it a couple questions and it falls flat on its face, well, what’s going to happen? They’re not going to come back and use it. They’re going to say whatever it’s. You know, and move on. So you really want to be able to position it so that there’s, you know, really good amount, I think, where the additional things for AI, where it’s coming in today is it’s able to look at things like it says, Okay, over time I see that, you know, people are asking questions about this, and that, you know, you could add this metric, and this would enhance the, you know, enhance your model, or you could do this. It was early on. It was just telling me, hey, hey, hey, there. You don’t have any descriptions on this stuff. People aren’t going to understand. So those are the obvious. But getting to the point where you’re adding more value, added metrics and things like that. So I think AI helps that, and I think AI can help also to extract information from other sources to help build a better semantic model. So whether it’s from your BI tools, or whether it’s from, you know, query logs or anything else that you may have, let me, let me extract that. Let me really start to understand how people are really using this data. And I think that, you know, that hasn’t changed in my career. All the places I’ve worked, I worked in a one very large data warehouse, and we were constantly, you know, trying to analyze and see how people were using the data to make sure that that we were working on the right thing. So, yeah, I think, I think that the human is still important so that other humans can understand the data enough to be able to

David Sweenor use it. I like that. Yeah, the message I sort of walk away with. It’s still an assistant, not an autopilot. So that guidance, it can help automate a lot of things, but it needs your expertise and judgment to help inform what it’s going to do absolutely. All right, let’s talk about AI in the future of data work I was noticing on LinkedIn that, you know, you’re posting about vibe coding and snowflake cortex. How does AI, you know, vibe coding or AI assisted development? How does that change what data engineers do, and what does it do well, and where does it maybe fall short, yeah.

Michael Meyer So over the past month, I’ve been doing a lot with this, and so we had released some things around what we’re calling which are new from going from copilot and snowflake to cortex code, and it’s, it’s interesting because I’ve worked with other tools too, and they’ve done a pretty good job, right, generating codes and stuff like that. But what I see this next evolution is, is that it can help you think from the very get go, from a design perspective, what am I trying to do? You know, to get going there, you can spin up proof of concepts very rapidly. And so for me, it’s still about, you know, my days of being a very productive, you know, whether it was bi or just, you know, application developer was having tight feedback loops with the people I’m working on a project from the business users. And so you can think of being able to spin something up, you know, in a matter of hours, that maybe would take days to weeks, so that you can start having those collaborative sessions. I’m not saying that this code is production quality code at by any stretch of the imagination, but what it really gets to is the art of the possible very quickly, right? And, you know, and there are things that we’re not. I mean, there’s so many different technology things. We can’t be experts in everything. It’s very difficult to be so I think that helps from that perspective. So like one day, I start out, I’ve never done true machine learning. I’ve never built a machine learning pipeline from Indian so I started out with cortex code and used a prompt that one of my other fellow, SES, was using it snowflake to get started and trying to find something valuable out of this, retail proof of concept, retail data proof of concept. And it came back with three ideas. And said, these could be three areas. And said, Oh, okay, so let’s look, you know. And basically took the one around potential, being able to detect issues with delivery, late delivery times, and things like that. So I went through the whole process, let it design it out into a, you know, into a notebook. So I had the Indian pipeline, ran it through, and it says, Okay, here’s based upon this. Here’s your metrics. They were like, like, 50% you know, accuracy and all the other key ones, right? And some are like, well, I know this is, I mean, doesn’t take, for most people, it doesn’t take you know, you’re going to your mind logically, this cannot be good. But you don’t want, you know, you don’t want just anybody to take that and say, Oh yeah, let’s start in production. That would have been Jim, would have fell flat on its face, right? So I go back to cortex code and said, you know, hey, I’m assuming, looking at this, this is not a very well performing model. Sure, you’re right. It’s not. So let’s, let’s create a couple extra steps, and let’s do some more feature engineering, spend some more time training and get to this. And so then, after doing that. I went and got as high as 85% you know, you know, accuracy is what it was predicting. And so those are numbers you now you’re getting into the number ranges that it’s a it’s a possibility, right? I still in my infancy of working with this. I would never turn into production. I would use it as a proof of concept. And then, right? Data scientists and say, Is this seem like something that could be, you know? So there’s where I still think, there’s a lot of people where they think they can just hit the button, spit out the code, and then they’re done. They don’t have to worry about anything else. Just start using it. And I’m just like, Well, no, it’s still not, not there. It’s a really great productivity tool. Can really get concepts going, get collaboration going faster, help identify performance bottlenecks, all those kind of things. But it is still not, to me, is still not a magic button to replace a person that’s, you know, an engineer that’s coding. I don’t we’re, I think we’re still very we’ve made huge strides in the last year, but we’re still a long ways from that ever being the case.

David Sweenor Yeah, totally agree with you. It allows, I think people without the skill, you know, they don’t, you don’t have to be a machine learning engineer, if that’s your domain. Or, like, you know, like art. I’m terrible at art, but I can, I can iterate, use AI, iterate on, you know, 100 designs pretty rapidly, you know. I like that, that that works, push these together, take that out, and then give it to a real designer. Because, here’s my idea, it allows me to conceptualize or visualize my idea, and I can give it to a professional that where they can, they can take over. So, you know, given that Michael for early career professionals, you know, we hear this, this thing that AI is going to destroy roles, and it’s, I’m certain, it will destroy certain roles, and then new roles will come about. So what do you think early career professionals need to focus on, to like, like, what attributes you know, would you look for if you’re hiring somebody today? Or what should they focus on? You know, is to be sort of future proof themselves against, because the raw coding skills, you know, maybe those aren’t as important anymore, the raw way I can know how to build Excel formulas as an example, if I’m a financial analyst, well, AI could probably build the formula for you now and more sophisticated than you’ll

Michael Meyer ever do. Yeah, you know, in the world of data, I think it starts, it goes back to fundamentals. And this is just an area that I mean from the very get go, understand data modeling. What does that mean when you know what is, what is a what’s good data look like? Because for most of our AI endeavors to provide good information. It needs to be good quality, well designed data. And so I think, you know, starting there, understanding, you know, the basic concepts of what it means to go from having, you know, raw data to something that’s really consumable, you know, by by a business user, and kind of the process, you know, the common processes that are there, you still need to understand those. Because if you get into, you know, a scenario where you can have a tool generate, but you still can’t validate, or you don’t know how do you test it, you don’t, you don’t know the basics, where we where do you write? And so I think that’s key. I was talking to somebody recently that’s working with a lot of AI engineers that have never experienced working with data. And I thought, you know, that’s interesting, because if you think about a lot of the things that we do and where a lot of the benefits are there, it has to learn from something and has to learn from good information, so unless it’s maybe just feeding it, you know, step by step, doing minor things, but not understanding data and how it works and how it can actually, you know, is used in the world to, you know, drive business outcomes from, you know, any tip, any kind of industry. I think even as an AI engineer, you really need to understand some of those concepts to really be, to really be able to stand above others. I think anybody it’ll become that people, yeah, I can write the best prompt, and then somebody else will say, Oh, yeah, now I can. We can use this prompt, and it does all this. Okay, so it did all this. But what does that? What does that mean? What’s the outcome this? What’s the business outcome you’re driving for? And what’s happening in the background to make it happen? I think that’s some of the keys that sometimes now we’re just so excited about the new shiny thing, can quickly, you know, produce something for us. But what about understanding, really, under the hood, what’s going on

David Sweenor and that, yeah, yeah. I think, I think those fundamentals are hugely important. Like, I always tell everybody if, if you know what you’re doing, whatever your domain is, AI could be an incredibly powerful tool, you know, to your point, like, I’ve used AI to analyze surveys. And I like, I’m looking at the results, it spits out, like, Oh no, it did the calculation incorrectly. You know, is maybe going across rows versus columns, you know, stuff like that. And so if you don’t know what you’re doing, you’re going to get erroneous. I think results, I think, must be, must be hard though, to be a early career professional. And like, you know, I see it with my kids, like, what’s the point of learning this? Because I could just have ai do it. But, you know, if you don’t know what you’re doing, like, if I ask AI a question about painting, or, you know, medieval literature, I don’t know, make something up. I don’t know about it, I get a very plausible looks, great answer, but I don’t know what I’m doing. I can’t, I can’t challenge it and push back,

Michael Meyer yeah, and then that’s kind of the scary part, right? So if you don’t have any understanding of how to test and verify, and if you’re just taking everything AI does as being 100% accurate, that’s gonna quickly, that could quickly actually become a career ender versus a career, you know, stepping into it because, you know, you don’t ever want to be part of something that’s especially making huge decisions and not knowing what’s going in into those decisions. And I think that’s, that’s, that’s that that’s, that’s key from that perspective. And it’s funny is, is I go back to when I was leaving high school, my guidance counselor says, I said, I’m not going to university. I’m going to a trade school to learn program. Well, all the computer programs that have ever needed to be written have already been written. You’re going down the wrong path here. So, yeah, there’s still, I mean, there’s, there’s going to be more and more great things that we can, we can do with with AI. And so I would say, too, for the early on, you need to be a constant learner. You just can’t take and you know, you got to, you got to be able to say, every day, I need to learn a little bit more about this and apply it. How can I get hands on opportunities to test it, apply it and get more familiar? Because that’s that’s really what’s going to also keep you for longevity, you know, in a career like this, is being a Constant Learner. Yeah, I

David Sweenor think that that that curiosity and that that challenger sort of attitude. I guess I always tell people, I’ve teach classes on AI to a bunch of my clients and, like, never take the first output. If you take that first output you’re in trouble, challenge it. You can ask it to critique itself and give me, give me, you know, 10 alternative explanations to whatever you’re having it describe and it will, it will do that, you know, very dutifully. And you know, it’s up to you to use your brain, but you got you gotta, you gotta, you gotta use it wisely. So we’re getting pretty close, Michael near the end. So for someone at, you know, early on data career professional, they’re feeling a little bit stuck. What’s the one piece of advice you want to give them based on your path, from, you know, programmer to architect to marketing to technical like, what kind of what’s your advice to them? Yeah, I would,

Michael Meyer I would say, you know, again, find something that that energizes you, what, what are some of your strengths and, you know, lead into those, and try to try to play towards those. It took me a long time to really get my writing into that, into my, you know, into my career. But it can be done as as I’ve been able to do, so find something that really drives you, because you don’t want to be, you know, stuck in, in that nine to five where all you’re doing is something that just doesn’t excite you, and you just, you know, you feel trapped. Yeah, and I just meet people. I mean, get out there and actually meet people and see people, understand what people are doing. The biggest thing, I would say, in the last 10 years, specifically in the last five, then the networking thing, and all the amazing people I’ve met really help, you know, keep you, keep you going to and keep you grounded in what’s real, and also keep you energized in what you’re passionate about. So I think those are some of the key things

David Sweenor I love that get out of your virtual world. And because I know you people might not know, but you do a lot of meetups there, and I think that’s hugely important. So you know, last question before, before we wrap, if I happen to be in Nebraska, where am I going? What’s, what’s your favorite beer, their favorite brewery? If you want to,

Michael Meyer you know, you need to. You need to come to Omaha. And I can take you around around this to several really, really, really good ones. And we could have a, definitely have a great time. And I can tell you all the stories about them.

David Sweenor So, okay, do you have a particular style you gravitate towards?

Michael Meyer You know, I think I’m pretty open about anything except I’ve never quite understood sours. That’s okay, one I’ve never gotten into. So, yeah, so it’s fun, like, this weekend, we have a local breweries doing a Bach fest. And, you know, there’s all those different like, seasonal things as they come around. I think that’s always kind of fun, too. About the. About that. And some of them tried to bring, you know, in some of the traditional, you know, things of why these celebrations existed, and bring some of those things, you know, back as into their own celebration. So I think that’s kind of fun, too. Again, being a history buff, I think that’s that’s part of the curiosity I have with some of these things.

David Sweenor Too, awesome. Well, Michael, thank you for being here. Next time I am in Omaha, I will talk to you about my cranberry lambic like beer I used to, used to brew, but thank you for being on the data, databases, podcast, your your experience is second to none. You shared some great practical advice. And you know, folks want to find you, where should I LinkedIn? Is that the best Yeah.

Michael Meyer LinkedIn is definitely the Yeah, the best channel. Yeah, definitely also. And thank you for getting me into I think you helped push me into writing medium articles, and I’m having a blast starting to do

David Sweenor that too. So all right. Well, very well. I’ll see you on the flip side. Thanks for joining the data faces podcast. Thanks. You. You.