 Hello 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 Dr. Wendy Lynch, the founder of analytic-translator.com. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DBTOX for 20% off your purchase. 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 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. And today we are joined by Dr. Wendy Lynch, the founder of analytic-translator.com. 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. Wendy, hello and welcome. Well, thank you. Thank you, Shannon. I am so happy to be here. Thank you. We're excited you could join us. And so you are the founder of analytic-translator or analytic-translator.com. So what is your company and what is it that you do? Yeah, I have two jobs. So I am an independent consultant. So I consult with large organizations about applying big data to human capital management issues. So that is sort of my day job, I would say, where I actually act as an analytic translator most often. Then as I realized how much we need this role, I launched analytic-translator.com and have a book to help other people learn those skills to try and fill the need. So what is an analytic translator? The easiest way to think about it is it's a person who speaks nerd and business. That's the way I explain it to people who aren't in the field. But it's really three basic skills. One is that you have the ability to understand and listen to a business person or a business team in a way that you can hear and define what problem they are trying to solve that perhaps data could actually contribute to. And that you will translate that into words and tasks that data people will understand. The second thing is that then you know how to understand what the answers are or the problems are or the challenges have been and translate it in such a way that the business can make use of it and really comprehend what you're trying to tell them. And then the third thing that happens as a side effect is to build alliances between these two teams. Because I'm probably not the only one who has watched those relationships start to get tense or worse when you have the business and the analytic team not understanding what each other needs. Makes sense. Very big need for that indeed. I'm sure as we'll talk about more. So but tell me before we get into that. So when you were very young just a child. So this is you know did you think to yourself did you as a you know dream that I'm going to be an analytic translator when I grow up. Or what was it that you wanted to be when you were a kid what was that dream. I was raised in a family of real nerds. And so I had a theoretical nuclear physicist and a microbiologist parents. So I think I only kind of new science and so I thought I wanted to be a marine biologist. That was what I thought I wanted to be because I thought dolphins were cool. That was that was where I started I think so. Yeah. Unfortunately, I get seasick looking at boats so that. Oh no. Yeah, that wasn't going to work so yeah. Okay so then so then how did that evolve so you so where did you how did what did you decide to as you grew up. Um, let's go to college. You know what did you where did you go and what did you think about what was your initial major. Yeah, I one theme that you will hear today is that I changed my mind a lot. So, one of the things that I was really into as a younger person was I was very into sports of different kinds played every sport I was allowed to play. And I really was interested in exercise physiology and those kind of things but I first I changed around to a variety of majors from pre med to engineering to this to that and the other. But I did land on what was called kinesiology which is the study of human performance, and I did an undergrad there and really thought that that was going to be my area. And when I went into a master's program. This was one of the first of two mentors that changed the course of probably my life at least my career. I ended up doing work with the person who was the chair of the department, and he was the stats and research design guy. And what I figured out was if you worked with as the stats and research design person, you got to do research with everybody. And I wonder if this was the person who studied enzymes in muscle tissue, or if this was the person who measured push ups or this is the person who measured VO2 max and, you know, endurance performance. This guy got to be the one that analyzed all the data and could look at everybody's project. And I went there for somebody who changes their mind a lot. And this was the perfect skill. So that's what I ended up doing was getting into the measurement part and the research part and the stats part, and then went to a doctoral program called research and evaluation methods, which was really cool and disciplinary so you did stats from epidemiology and stats from econ and stats from meteorology and stats from all these different things and to non nerds that won't sound exciting but to me. When you got to know all the different languages, you know, an epidemiologist calls it something and educator calls it something else. One will always use logistic and one will always do an over, you know, I mean it just is how they're taught, but when you understand how all of them do it, then it makes even more sense I think. So that was, that was my sort of college trajectory. I like it very impressive, very interesting, because you're right. Yeah. I love it. So, after school then. See, you've, you've got this now deep dive into a lot of data. Yeah. So where did you go from there. Well, continuing on the theme I wandered around a lot. I, I actually have been in large business like a large insurance company. That's where I went first where they were doing the first on site health and fitness facility that ever was put together I think. And they wanted an evaluator on site to look at the impact of that on business outcomes and health and costs. So I started there, but I have since been in pretty much every different type of location. So I went from there to academia, because I wondered if maybe that would be a better fit for me. And what's what you find is in academia you can ask any question that you want. So you get a lot of freedom, but you don't have a whole lot of money unless you can get a grant. So in business, when you're starting out you don't get to ask everything you want because someone tells you what to ask, but at least it's funded. So I bounced around a lot so went to academia, worked in startups, worked for large consulting firms, worked in big industry, worked on my And it was funny because people would say, I thought you worked in healthcare, or I thought you worked in insurance. I thought you worked in HR. And my mom would be like, I can't explain what you do what do you do. And it was the same thing that happened to me in school is I was just interested in so many different things. And I didn't realize that what I was doing was actually the same thing all the time, which was, I'm a sense maker, and a translator. And it doesn't matter what the topic is. If there is somebody there that can help each side talk to each other. It starts to make it adds value to the situation and I didn't even realize that until five years ago that that was what I was doing. So what did bring you to that conclusion. The other mentor that I'll mention that changed the trajectory of my career is a person who was in the Department of Family Medicine with me. And her role was to help new physicians, the physician students learn to talk more effectively to patients. And she had studied all of these different aspects of effective communication. I mean, just about, you know, 100 different theories of communication. She was so good that I just glommed on and said, could you tutor me, could you help me understand what it is that you do that makes it work so well. And we've ended up being lifelong friends. And she, and I co authored a book that came out in 2017 called get to what matters which is a communication book, which feels very adjacent to what it was that I was doing and I felt compelled to get it written and disseminated because I feel like those skills have probably been as if not more valuable in the field that I was in than the analytics skills. It wasn't until I heard a podcast actually in 2017, and the podcast host said to this woman her name is Greta. I'll get it to you anyway. I was listening to this podcast, and she said that the host asked her, are, will we not have enough data analysts, you know, good data scientists to fill the need. And she said, Oh, no, no, no, we're going to fill that need just fine. What we don't have is we don't have people who know what questions to answer and know how to figure those out. And I just went, Oh my God, that's me. That's what I've been doing. I've been translating this is so cool. So that's how I figured it out. And then during COVID. I actually was thinking about what I wanted to spend my time doing while we weren't traveling and doing conferences and stuff. And I really thought, you know, it's time to put it into a format that people can use and learn from. It's very cool. So, and just a couple of highlights here, I mean, I love that you have so many interests. And of course, data enabled you to explore so many things because that's what it's so great about data right is everybody say everybody uses it. And the diversity of, you know, companies in our conferences, it's just amazing always it just, I love, you know, some of the companies that we get a chance to work with. And then, you know, what brought you here is being curious in exploring and being open to new possibilities, because I love that I love that. I found somebody who just inspired you and motivated you to reach something for something new and tie that back into to data. Yes, I think I, I think it's important, especially if people are just getting into their careers. I realize that there are so many people that you will run into who have something incredible to teach you. And you will learn from what they offer you. And you will learn from what it is that they are doing and what they've accomplished. So it's, I think we often get into our own heads about what we're doing, and we can learn from what I mean, learning from you guys at Dataversity, the incredible things that you guys are all doing. That's what's so wonderful about your platform. Oh, and likewise. 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. Wendy, with your background, so what is your definition of data? Yeah, yeah. Data to me includes signals signals of an event or of an amount and something that's detectable that documents that a behavior or an event or a occurrence has happened. And usually that ends up being collected and put into a format that's usable. But I want to expand that just a little bit, because if I'm having a conversation with you. And you are telling me what you need to accomplish with a particular business question. I noticed that Shannon has a confused look on her face. Or I noticed that Shannon doesn't sound completely confident that we are getting to the point that she's trying to make that that is that is also a piece of data. And so we are always observing and whether I record. Yes, I think she gets it or no I think there is more for us to talk about that is still data. So it's, I was thinking about that question. And I know that there are official definitions but any signal is a piece of data. I think that's very true. I love that definition. So any signal, and you may use it or you, you may not you may record it or you may not but recorded structure data obviously are the things that we get to make sense of and connect with other pieces of data and those are the things that are so important and useful. And so these other human ones that we have to make use of too. And we do whether we know it or not right processing so much, right, right every day. You may find yourself very irritated with somebody and whether you're noticing exactly what signal irritated you or not. We do take all those signals. Yes. I love it. So, and you see the importance of data management and the number of jobs with data increasing or decreasing over the next 10 years, and, and why I think that it will increase for sure. I think the reason why jobs that need to incorporate data will increase is just because it's becoming ubiquitous and every organization is understanding and learning how to make better use of the data that are available to them. The difference that I'm hoping will happen is that we won't necessarily silo the jobs into data jobs. I mean, I know that we all get irritated that data sets are siloed makes us crazy that we can't put these data with these data and I mean it just makes you nuts. In some ways, we silo the data people to. So there's a group of analysts who report to a lead analyst and those analysts, then get, you know, deployed to go work with other groups. And in some ways, wouldn't it be better if you had folks who have dual roles. Rather than just being data people and some companies have that to a certain extent or you may have a data division that's within one group that's different than in another group. But I do think we're going to have to be dual trained in the future, much more so than we are now, because it doesn't work data aren't useful, unless they're in the context of people and the organization. And you don't have. You don't want to have people only certain people who deal with people and only certain people who deal with the business and only certain people that deal with with data. So I, I'm hoping that as we grow. We ask for people to find two things, let's say, or, or more that they, they want to do. So what advice then would you, and we've kind of touched on this a little bit but what advice would you give to people looking to get into a career in data. Well, I think. First of all, realize that no matter how smart and trained you are today. Everything that we know and do today is going to be outdated tomorrow. So that's not a bad thing. But if I still, if I still only knew how to do the things that I knew how to do when I got my PhD in 1986, we would be in deep trouble. Everything moves so quickly and it's getting faster and faster and accelerating accelerating so don't think of yourself as I am only X, I am X on my way to whatever. So think about what you're interested in and where you want to be continuing to learn. So I think, I think that's the first thing is know that this is, this is a journey this isn't that you haven't gotten there and you pretty much never get there. And once you think you're there, you're in trouble. And I've seen people decide, well, I'm going to retire because I, if I don't retire. I have to learn something new. And what a sad situation but that there's a lot of people who are like, look, I'm too old to do that. Well, you better not be, especially in this field. There's just so much to learn. The second thing is also more of a perspective issue which is no matter how unique a question is to you. Somebody has probably thought of that question before, and has learned a whole bunch of really cool things, and may have applied it in a way that you haven't thought of. Be a student of a new area, because it doesn't reflect badly on you that you can point to how it's been done 10 other times. It actually makes you more valuable because you save people a lot of time not reinventing the wheel. You have a perspective. You have somebody saying, well, why don't we try X you can say, well, you know the four people who tried X didn't do very well but if they, you know, shifted it a little bit, they did that it worked even better. So I find that we get quite myopic about our company and our approach. But if, and now there's no excuse and we don't even have to go to the library anymore which is what you used to have to do. You can get on to Google scholar you can get on to research gate you can get on to a variety of different sources and just wander around there and find it. So, you know, find the thing that you're interested in, but keep your eyes wide on the things that you can learn from everybody else. That's great advice. And I recently read a study that you continually learn and go after trying to learn something new almost daily. Have a reduced risk of dementia and Alzheimer's. Oh, wow. Yeah. That's pretty cool. Right. It's good for you. Yes. Health benefits. And I'm finding that that advice is pretty, pretty common. But yeah, it's great advice and it's so hard to push our boundaries and it's so hard to, to be curious and to, but yeah, you're right. I've met some people who think they are the, the center of knowledge and but there is there's so much to learn from so many people. There is. And I have seen some people report research findings to an audience that's been around a while, who are just looking at them like really you think this is new, you know, you did that back in 1997, you know, or whatever. And so it helps you make sure that you are aware and standing on the shoulders of people who can teach you something. Indeed. Well, Wendy. So if somebody wanted to reach out to you for consulting, how would they reach you. Well, you can reach me at Wendy or Wendy at analytic dash translator calm, or Wendy at Lynch consulting LTD calm. I'm also on LinkedIn easy to find. So, and I'm always looking for other people who are curious and interested in trying to solve tough problems so happy to hear from anybody. I love it and your books are on your website. They are on the website and they're also on Amazon. One is become an analytic translator. And one is get to what matters. I love it. And we'll grab those links from you and post those on the podcast page as well. So, Wendy, thank you so much for taking the time to chat with us really appreciate it. All right, thank you Shannon I really appreciate being asked. And to all of our listeners out there if you'd like to keep up to date on the latest podcasts and in the latest in data management education you may go to data diversity net forward slash subscribe till next time. Thank you for listening to data diversity talks brought to you by data diversity. 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