 And welcome to Dataversity Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host, Shannon Kemp, and today we're talking to the co-founders of Moxie Analytics, Laura Madsen, CEO, and Serena Roberts, COO. 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 can help make those careers a little bit easier. To help keep up to date in the latest data management education, go to Dataversity.net forward slash subscribe. And today we are joined by two fabulous people in data, the co-founders of Moxie Analytics, Laura Madsen, and CEO and Serena Roberts, COO, or, as appropriately renamed, the Chief Get Shit Done Officer, which I love. We'll get to that in a minute here. 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. Laura and Serena, hello and welcome. Hello. Thanks for having us, Shannon. We're excited to be here. I'm so glad you two are here and I ask you to be here together as the co-founders of Moxie Analytics. So tell me, tell me more about Moxie Analytics. What is the company and how did it come to be? Sure. Yeah. So Serena and I probably should have practiced who said what, but I'll kick off because I think we both sort of agree at least in the origin story at the beginning level, which is I quit my corporate gig at the end of 2018, having no real plan, which, you know, tips for better living don't do that. And then data for a really long time at that point had a really large network figured I'd find a project here and there, sort of started an earnest doing some data project work and then realized, wait a second, I could probably like actually do this for a living. And then started thinking about doing it on my own, which didn't sound as much fun. And then really the only person that I knew in my network that felt like the right fit for me was Serena. And so I started, I started pinging her asking nicely. If she would, if she would quit her corporate, you know, could she corporate gig and, and join me in this ridiculous adventure. So, what would you add Serena? I think it's a pretty good overview of our origin story but I think just touch on like what Maxine analytics is for people that might not know. We are a data analytics consulting firm. We do a little bit of everything. We find ourselves doing a lot of data literacy data governance to Laura's chagrin. That's why she wrote a book on a subject that she hates. She's going to spend the rest for life, not trying not to do data governance work, which is the thing that she's best known for. But really it's about like everything that we do falls under this umbrella of enabling a data culture, right, like why do people build dashboards, why do people govern their data, why do they have self service, you know, efforts, you know, why do they do data governance and like why do they do all these things right they do this because they want their cultures to be focused around data and how they can use that to make better positions and, you know, drive better business outcomes. So, yeah, we're a consulting firm we do stuff for money. I love it. So, it's very fascinating start there. So what is it you do specifically what is the most common thing you do for your clients and how do you work with data in your current jobs. I'll take it on first Laura, and you can we can round it out. So, we do, we do things like build dashboards for people or do proofs of concepts. If they're thinking about you know some kind of data visualization tool, or even like a data catalog, or things of that nature. We have done a number of projects recently that are more around like kick starting data governance initiatives, and data literacy initiatives and when I say data literacy, I mean applied data literacy, and the difference between applied data literacy and just like regular data literacy is that there truly is an app like an immediate application for what you what you learn so the the learning objectives for these data literacy programs are how to use that company's tools to use that companies to access that company's data to solve that company's problem. And oftentimes when we hear from organizations who are interested in data literacy data governance, it just like comes right along in in tow right because you don't want to have a bunch of people using your data without any kind of like guidelines, or you know, safety rules in in place. And there's two projects, oftentimes like they overlap and there's a lot of inter interchangibility. So, and it's also the space that Laura and I get to work together on projects in. Otherwise, we're usually kind of off doing our own things. So, I don't know what, what did I forget Laura. I tend to focus a lot on data strategy. I call myself a fractional CDO or chief data officer. So I support a lot of chief data officers, either because they just started at their job and they need a little support as they get going, or their specific functions that they need support on like data governance. So I do a lot of data strategy, a lot of supported chief data officers and of course, as we all probably know at this point, fair amount of data governance work, despite my love hate relationship with it so We'll come back to that in a little bit so So when you were very young in elementary school, you know, is this what you dreamed of being when you grew up, I'm going to go into and start a company on and help people with their data problems. Okay, so I'll start so I will tell you and my dad would probably tell you that I, by the time I was nine years old started like three companies. I had a babysitting company. I had a, a sandblasting company where I would sandblast designs on things for people. And then over the years right just had a million ideas for businesses. So the business side for me it was always the thing. Some of it may have been my overwhelming need to get out of my small town. I was a runner. I wasn't going to stay. So it was really for me always about business was it always about data it was not although a college professor will tell you that that is clearly how I thought like everything and even now I pretty much make every decision in the context of some kind of matrix. So it's just, it was always kind of there as a latent thing but from a business perspective sure I always wanted to be a business owner and be the boss, so to speak so I like it. So you know what about you. I have to say there's probably a lot of parallels between Laura and I on that front where like, but I will say I literally did not know that there like that business intelligence that like data and analytics like was a thing until I was like legitimately 24 ish 25 years old right. You know did I want to work with data when I was a kid and my God no I know that would be, I think abnormal. I wanted to figure out ways to just not work at all, which isn't not what I'm doing right now. But yes, I have always had that entrepreneurial spirit. I have always had that sort of take charge leadership just asked my younger, my siblings, who are both younger and older than me but wherever you fell in that spectrum like I was the boss. Unfortunately, for them. I did. My formal education is actually in entrepreneurship so my, my degree is in entrepreneurship. So I knew that that was, that was a thing that you know it was a passion was kind of where, where my life was going to lead me at some point. And I think for a lot of people who are in, you know, certain levels of privileged positions, you do get to a point where you're just sick of all of the bullshit. And then you're like, I'm like screw that I'm just going to go try my own thing. I'm going to just see what happens here. So back to our origin story, it literally had a conversation with my husband. So this was my big that I was in. Before I left my corporate gig. And my husband said to me is like, you know, I knew you were kind of a B word and say that I knew you were kind of a B word when I married you, but this is some next level stuff like you need to quit your job woman, like you're just unbearable. And it was, it was the truth like I was not being good to anyone, myself included. And as like, you know, part of that conversation was just like, well I can't put my job like we have health insurance through me like I make more money than you do like how are we going to live, how is this going to work. And we just were like, well what's the worst thing that's gonna happen well we could, we could have to sell everything that we own and live in an RV. And then there was this moment where we're both kind of like, it really doesn't sound that bad. So it might actually still do that, even if this thing works out. So, anyway, all that same. I did not know that this is exactly where I would end up. But I feel like gosh turn it it's the right place to be. I love it. I can relate to what a lot of a lot of what both of you are saying very much so. So, but tell me, so how did you get from this entrepreneurial passion into data. So what led you to a path of data management. You can go first Laura. So mine is mine's actually a really straight path to be honest with you and when I talk with other people that are in data, you know, for a really long time we had no programs that did anything to do with data, right. And so, for most part, the people that I worked with people that I worked for over the years had no background in data. And, and so I was a little bit different so I went to school initially for fashion sizing, which if you knew me at all would make you wonder what I was thinking because I can barely match my own outfits, which is why I wear black a lot. You know, there was just a different time in my life or I thought, Oh, fun. And that didn't last for a long. And so I had to pick a major and the major I picked was strictly because I was going into a new college and they needed a major on record because I was a junior and I just picked the hardest final I ever had which was psychology. And so I was like, Oh, well, I can switch at any time right and I ended up getting a bachelor's degree in psychology and the unfortunate thing about a bachelor's degree in psychology is it's 100% useless. And I am, and particularly when I was in my 20s, not a very empathetic person. I had zero ability to be a therapist. As a matter of fact I practically flunked that therapy class because everybody that takes the psychology is a program has to take that class and yeah, they pulled me aside and said, boy please don't do this you'd set the industry back many many decades if you became a therapist. I don't know what to do with a psychology degree. I had a really brief stint in the army because I thought well, they'll take me. They didn't. They took me long enough to realize no we don't want her. And I ended up in a what's called applied psychology which is actually an applied analytics degree that use that from a social sciences perspective uses applied data and statistical methods to control for error. An easiest way to think about that is like surveys, right. So I spent two years learning about statistical methods of control for error. And my first job was at a psychiatric hospital, and my family still convinced that I didn't work there I was actually an inmate inpatient wonderful services by the way and very important for our particularly for our healthcare programs in the United States. But I ended up working at a psychiatric hospital and really helped them bring along their data program. And the crazy thing about it at which I did not know I was doing at the time I was creating a data warehouse. So we had a method of inputting all of the intake assessments that all of our patients took. And I was responsible for putting all that data into tables and analyzing all that data. And so that was my first professional job and I've been doing it ever since and that was, that was 1999. Wow. Wow, I love it. So, and Serena what about you, what brought you to data. Well, first I have to say, Laura, I know you glossed over a couple other careers of jerseys in there that will save up for another podcast. Mine was was definitely like I just kind of fell into this world without even trying so I, I had my first child when I was 19. So, and I also have a sister who is younger than I am and she, when I moved out. She moved with me. So I had my, my son, my young son, and my sister who I was mostly responsible for. And I grew up in a small town also, and I was also a runner of like to get out here. This is North Branch Minnesota like, it's not like crazy far, but it was like 1100 people when I graduated from there. So I got there as soon as I could. I moved from North Branch to Lakeville, Minnesota, because that felt like a huge distance to me back then right. And I literally was just like, I need a job, I need a job, support these people. And I wound up finding a role in like sales and marketing, just kind of doing whatever needed to be done for a BI consulting firm that was still, you know, relatively in their startup phase I think I was like 18 or something like that. And it literally wasn't until that day that I knew that this field even existed that you could make, you know, good money doing this kind of work. So I started on sales and marketing in that role. And, and also just like I said, like, when you are employee number 18 you do like whatever needs to be done. So then it was like, okay, well now I'm writing statements of work and now I'm trying to, you know, help figure out who are the right people to be on these projects and like, I'm learning more about, you know, what it takes to deliver this kind of work. And so I just kind of like was able to slide into the delivery side of, you know, data and analytics where I started teaching on how to use some of these programs, and it was just really, it was just fascinating. And as the tools got easier to use, and I became more technically proficient, and, and all of those things that, you know, people always say that, you know, if you, if you can learn the technical stuff, it's so much harder to learn how to deal with people and like, maintain relationships and those. So I've always been able to check that that second box right like that has come pretty easy to me, doing the sales and doing doing the marketing and you know that relationships are selling that kind of thing. And I was able to just learn all the technical stuff and so I came out of that, that 10 year stint at that one consulting firm with, with both of those skillsets and bonus, you know, a relationship with, with lower medicine because that's how we met each other so we first got in contact. So, definitely, not a linear path into this, into this field, but you know, come to find out that's really probably more than normal, more than rolled in the exceptions that people just kind of follow me here. And it's so much a part of our world data, you know, so much a part of everything that we do. I just, I think it's going to be hard to escape it at some point that everyone will work with data in some capacity. I don't disagree with that. And yeah, I certainly don't disagree that there have, there are very few linear paths that I've seen or encountered, which is part of why we're doing this podcast right is just to figure out, figure out what those paths are, what were some best practices and along the way. So, so there's a few things that I want to dive into. But let me get into what is your current definition of data and how do you work with it in the roles that you're currently doing. Yeah, I saw that question and I was like, what kind of ocean is that. But, you know, honestly, I feel like data is like everything and nothing at the same time. Right. I mean everything, almost everything that you do creates data. We are processing data all the time in our brain as our human brains are doing, but so it's everything in a certain sense, but it's also nothing if you can't turn it into information. If you don't have the ability to either through your brain like this car is coming at me, which means I should get out of the way, or you know, you're aggregating millions of rows of data to try to tell you what your next quarter is going to look like. So that in that sense, it's also nothing right, but you know, if you're not able to translate that into information. And, you know, what obviously what we do with it today is help people, help people, help more people be able to take the data around them and turn it into useful information and be able to use it and you know safe and meaningful and in timely timely manners or anything to add to that. It's really good I mean I agree that data is everything and nothing right I have a strong affinity for data. And, you know, found my home really early on right once I realized that this is how I thought. So the idea of like always bringing in data and consuming it in a myriad of ways is just how I kind of operate as an individual. And probably one of my biggest, like, the thing that I love to do with clients is to help, you know, take that chaos and turning it into something that feels like it's, you know, just just a little chaotic right I mean I love the mess so I'm not part of the like, you know quirky personality but I love the mess and I love the data and I just like, I live in that chaos but I recognize that most people don't. They don't want to live there they don't really like it prefer to visits, you know. So, to me it's like, yeah, particularly in a modern age, you know I've been at this for 20 some years now so when I started your data came in an inner office envelope. And now we're all producing, you know, millions of rows of data every day just existing and so you know that ride has been really fun and cool and I've enjoyed it. But for me it really is realizing that because I'm so keen on that part, I can help the people that aren't, and then kind of figuring out how to take advantage of all of that and just live just slightly outside of that chaos so that you really can build towards insights. And, and at some point, maybe even get some foresight. So, with your definition of data is it sound like, even though so Laurie took a more linear path that Serena maybe not so linear to working with data. I think the commonality there and what I'm finding with almost everybody is that it was your passion, you just followed your passion really is what you did. You know, for wanting to build and start a business and be involved in that. You know at the heart of it. But it sounds like you felt passions into different areas and how do you compliment each other and working because I'm fascinated to Laura that you don't like what you have a love hate relationship with governance. I'd like to hear a little bit more about that. So how do you divide up the work and where is that specific passion because data is management is such a broad topic right there's so many different areas so what are your passions and how do you split that up and and and and maximize those passions. Yeah. So, I think we actually have a pretty nice. We operate very distinct capabilities and then there's a few spots where we overlap, as Serena had said earlier, because I have existed in data for a really long time right that strategy and the governance and the management. I've led a lot of data warehouse teams. So that is definitely where I live and breathe and those sort of, you know, more data management functions and then the data leadership areas. And that's just because that's where I've always been right. And I think Serena, you know, just doesn't exist in those spaces except where we, you know, overlap so that's really the nice. I'm not being very articulate about this but yeah, I don't know Serena why don't you take a stab at answering that question. I actually like, I have an answer, but it's not coming out right. So we're just going to skip this and move forward. And I think I think there was like three questions in your question. Yes. So first of all, like, I like passion came later. And I, I mean I'll follow the money straight up. I needed to, I needed the money I needed a job, you know, support me in my, my dependence. And when I think about the like the passion that actually led me here with Laura right now, that was more passion for wanting to be the best version of like the sum of all of my parts. Right, I did not. I no longer wanted to accept the fact that I had to compartmentalize Serena as you know the data and analytics professional Serena as the wife Serena as the mother Serena as the sister as the whatever right because we all operate especially, you know, women, let's be honest right like as far as like domestic responsibilities go, we generally take the lion's share of that. And we are more social creatures were like more responsible for the relationships that our family as a collective has with other families. And so I just felt like I was done compartmentalizing those things and always feeling like if I had to want if I wanted to succeed at one that I had to give it to suck a little bit more at the others. So my passion was that you know that led me really where we are right now was was trying to buck that that norm, trying to challenge that status quo not just for myself but for other women who I know felt the same way in similar situations. Um, so I just wanted to call that out on like me what passions, passions actually led me here right and there's never really been a passion for me the work is fun and it's cool and it's interesting. But if I'm being honest, those aren't the things that fill my cup every single day. Right. So now, if I can address like the synergies between, between Laura and myself. Synergy is like the best word to describe it we are like the physical representative representation of the Yang and the Yang. I feel like I should actually be wearing a white shirt right now. Just to just approve that point because Laura is always wearing black. I am sometimes but usually not wearing black. You know, she loves to not be around people and I usually enjoy that. So either are like just from the perspective of who we are as people we are very different, but we share some really fundamental core values. And that that is our foundation and that is our mission and that's our, you know, our true north or whatever people are calling that these days, where we can always come back and in service and alignment on those things. And as the work goes, you know, I think I alluded to this a little bit earlier where the there, like, you can't have a data literacy program without data governance. I mean, you just can't or please don't like a big of you please don't. I mean, all of that effort in into your data governance and data management efforts then like, why are you like, you should probably have more people using that. That's kind of a waste. So again, it's like it's such a synergistic thing, you know, while Laura is never going to be involved on, you know, building a tablet or dashboard, or something along those lines. There's not going to be writing books about data governance or advising chief data officers on what their data strategies are going to be do operate in very different spaces in a lot of respects. But synergy is really just the best the best way to describe how we work together and how we just gel together as as human beings. You know, or anything you want to add now that you've had a moment to process. It's just funny to me because it's so rare that, you know, I, like I talk about myself. Right. I'm always talking about the topics I'm always talking about data and what you should or should not be doing, or you know that kind of a thing and it's really rare that I talk about, you know, how I got here how I think about things. You know what soon and I do on a day to day so it's funny that I tend to not, you know, this is where I get stuck it's like tripping over the stuff that I probably know the best, but just don't have the practice of talking about you know where we got how we got here. And I will say that, you know, what an incredible privilege in so many ways. Yeah, so I'm really fortunate that I have just in general, I'm very fortunate and so privilege is always in the back of my head like I've had an opportunity, because I live in a house to be able to start a company and, you know, my husband carries the insurance and all those other things right, but I also feel an enormous amount of privilege and finding Serena as the Yang to my Yang or the Yang to my Yang right because the reality is, if it, you know if it weren't for her sort of holding me down. As they say, I would, you know, just never be able to be very successful and the reality is I would have ruled the world by now if it wasn't for my crippling anxiety. So, you know, I need that person can help me through some of that and she is she is that person so you know I'm just really fortunate to be able to live in that space. I asked about data governance and how in the world I ended up hating data governance but being like doing data governance. So that that is a funny story. But so I started when I left my corporate gig in 2018. I did sort of a, you know, data, like an assessment of the data landscape. At that point I had been in corporate gigs for a while I led these functions and I just hadn't really like, what is everybody talking about. And through that process. Identified the data governance really had not been conceptually updated since the late 90s. And had been identified as like the reason why everybody was tripping all over themselves. Oh gosh it's data governance. And I used to joke if it's not true anymore but that when I googled it at that point, the definition of data governance was like two paragraphs long. It was almost a page long. And it's how and I knew in that moment was like those one of those Disney moments right where it's like everything sort of coalesced and the sky opened up and it's like, Oh crap I got to write a book about data governance because we suck at this friends I think we're just doing this all wrong. And in the, like the very first sentence I write in that book is like, I really hate data governance and I hate it because we just keep getting in our own way. You know, I, I'm very passionate about data and what it can do for organizations but my gosh we just the, the, all the things that we do to get in our own way from, you know, preventing ourselves from using the data and finding the data for sites for the data is it that list is super super long but for whatever reason that day I picked data governance. And here we are. Here we are. Companies are considering investing in data literacy education, but still have questions about its value purpose and how to get the ball rolling. Introducing the newest monthly webinar series from data versity, elevating enterprise data literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at data versity.net. I love it. Well, you know, I kudos to both of you for finding that synergy for, you know, my own philosophy of business. So many leaders build teams that look exactly like themselves. And, and how do you and I think some of the biggest challenges is finding, you know, and accepting people who, who do balance out our weaknesses there's so many, you know, leadership books and such that say that we should have good strengths and, you know, leaders, good leaders always, you know, are always conscious of what they're good at. And I would argue with that I would argue that really good leaders. Yes, no, their strengths, but that's easy to know our egos just as human beings allow us to know what we're good at. You know, it's finding those weaknesses and filling those, you know, I think that's the biggest strength that good leaders haven't and kudos to for, for finding that and being open about that. And, and I really love that synergy that you guys have and have built on. So, so with that, you know, in your careers and do you see, 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 I obviously I think it's going to increase. Right. I just think that there's, there is never going to be less data. There's never going to be less of it. And there are, so, you know, the complexity is will always be there the amount of it will always be there. And with that comes the problem of if you don't manage your data. Then you're not, you shouldn't have any expectations that you're going to get any value out of it. Right. So, yeah, I don't see that going anywhere. But any young people that I talked to now. My advice is usually like find a career and data. Go. There's so many different things that you can do, whether it is data management, data engineering, data visualization, the literacy like so many things that you can just like, get on that bandwagon. It's not going away. It's just going to transform and more right with the, you know, the artificial intelligence and machine learning initiatives and all of them, like the moral and ethical dilemmas and applications of that. You know, so there are certainly more forefront areas of the data and analytics landscape that you can be on. I just think like the table stakes of that work are going to be there in space. And the first thing we'll see is right when data, when data first started when it became a thing that organizations and we're still dealing with this because we're sort of at that place in terms of our life cycle, where we haven't figured out where data belongs organizationally, but it's like a department, right, data is a department and we've always put data as a department under it, because that's where all the specialists come out right it's where you find your flock right all the crazies around, you know, I joke all the time, especially in healthcare, you put it in the basement, and usually data people right next to the more because that's where they cool things, including your data center. And so, you know, it's like that that is shifting over time. There are people coming out of college that is that they're, they're used to living and breathing and technology it's just part and parcel to their day. So we're going to see, I think a lot less of those sort of centralized functions and, and it is a function will become a lot more federated, living out into your organization rather than centralized. And I think over time data will follow that pattern as well because everybody knows about data right. You have podcasters at 12 years old looking at their trends and looking at their data and they're, you know, figuring out well you know when I said this this happened so I got to say more of that or you know click bait stuff right, but it's data at the end of the day. And so I just think it's become so much more ubiquitous that over time that model of you know centralized data team and an organization will become less and less valuable and we'll see it become, you know, a little bit more ubiquitous. So then the challenges are different right where it increases our ability to actually have touch points, where we're making sure that we're doing all the right things in a standardized way, even though it's kind of everywhere. And you already see those pain points now in a lot of organizations, but we keep trying to shove it under Wallace, it belongs here. Okay, you know, so I it's not going anywhere it's just going to get spread around. Okay, so and what would you advice would you give them to people looking to get into data whether in a specific role or in general, my advice. I mean it really depends because there are so many different areas of data that you can work work in. I would say you kind of have to take a baseline of what you know already, right for people that are more creative, more people focused, you know better at those relationships you know finding getting themselves prepared for roles like being those barriers between you know business and and it being able to gather those requirements and and all that good stuff. Being able to develop data visualizations business analyst kind of roles where you're, you're able to, you know, consume, consume that data colas that day and then like feedback someone doesn't know anything about it and just like human if that's more, you know, someone's strength, then I would tell them to prepare prepare themselves for those type of type of roles and the you know the, I think the barriers to those kinds of roles are probably lower as well. I mean, there's so much retraining out there on Tableau Power BI Excel, all of those end user tools that people use to actually interact with the data there is just tons and tons of training out there, either free or cheap, or you know, even if you wanted to go like full fledged boot camp kind of thing like it's not something that you need to put your entire life on hold and work at your house to attend. And then there's you know the more technical roles and maybe you need to, you know, have a really deep understanding of SQL. You know the data engineering piece of it, maybe there's, you know, more of that like strategic side of things, data quality data management like there's so many different areas so I first try to help people figure out what like what is a general direction that feels like is best aligned with that person's strengths. But beyond that I do think that that your first stop should be trying to find a short cut between where you are, you know your point A and your point B, that doesn't involve a four year degree. You know look for it for the boot camps and look for mentors a lot of these things like you can actually build a portfolio. And at the end of the day doesn't matter what certifications you have what you know what your diploma says it matters what you can do to build a digital portfolio and that becomes your proof of here's what I can actually do to actually build is not capable of. And, you know, as an employer like that means a whole heck of a lot more to me, then, you know someone certificate or, you know paper that's hanging on their wall. So that's my, like my, my, my biggest sort of general advice is like, don't feel like you have to go to a formal degree, you know through a formal degree to enter into this space there are many many other ways, cheaper shorter ways of doing it. Ideally, like, we, there's such a great need for good people in this space, like we can't wait, we can't wait four years for the next, you know cohort of great data people to come along like we need to, we need to find a way to shortcut that. It's funny. I used to get this question all the time. And then, as I've aged out of the like, it's like I got to the point where I like people are like oh she's too old to really really be able to give me guidance so I haven't gotten this question in a really long time and I find interesting because it's one it's always been one of my areas of interest and passion so the first thing is if data sparks joy for you for in some in some capacity, you know, go because like Serena said, there's a million ways you can get into the space you don't necessarily have to be like super super technical. I joke all the time and maybe it's slightly jump maybe it's not I'm not a technologist right most technology will break when I come into a room. Oh my gosh, it's crazy right. But the reality is that I, I, you know, I was trained to bang against data, and that's how I spent the first, you know, probably 810 years of my career. The tipping point for me was when the executives realized I could talk to them in a way that they understood. And so when, when I think about guiding people. The main thing I don't care what your degree is I don't care if you know some you know multi linear regression. What I care about is that you're passionate enough to think about how to present content to your audience. Because it does not matter what role you have. If you're a data architect data engineer data visualizer. If you're a data manager for data, if you can explain that if you can explain what you're doing and why you're doing it, and how you're going to do it succinctly, you will go places. Because that's what we're so missing in this space is everybody can get into the gobbledygook right because it's like oh look how smart I am. Very few people can boil that up to, you know, the bullet points, and to be able to drive that forward towards something for your organization. And that's really what we need, because that's what you find would be really successful in your organization I think that's why a lot of people see, you know they bang against their heads all day trying to get you know some data repository up and running and then somebody buys a piece of software, you know, brings it online builds a beautiful dashboard and everybody thinks they're brilliant well because they spoke to the audience and use cases and what that organization needed. So, to me that's really, you know, where you spend, where you spend your time is figuring out how to communicate that. It's great advice from from both of you. So before we wrap it up as we're kind of running low on time here but I just want to make sure and ask and anybody would like to solicit the services of moxie analytics how would people reach out to you how do they find you. Thanks for the plug Shannon. Absolutely. You know, you could go check out moxie analytics calm, get a better idea of the services that we offer. You can find us on LinkedIn. Yeah, I mean, we're out there, we put ourselves out there so you can find us. And I know I'm at both of you and I, as you presented at our price analytics online conference. And Laura, you most recently presented at enterprise data governance on lines, we have recordings of both of those, which were fabulous so I can attest to your teaching capabilities as well. Well, Laura and Serena, thank you both so much for taking the time to do this and talk about your careers it's really been insightful and valuable so I know you both are very busy. And thanks to all of our listeners out there. 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