 Hello, and welcome to My Career and Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp, and today we're talking to Nithya Ramamorthy from the Mayo Clinic. 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 of 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 talk with people who make those careers a little bit easier. To keep up to date in the latest in data management education, go to dataversity.net forward slash subscribe. Today, we are joined by Nithya Ramamorthy, the analytics lead at the Mayo Clinic, 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. Nithya, hello, and welcome. Hey, Shannon, thank you for having me. Oh, I'm so excited for you to be here. Okay, so you're the analyst lead at the Mayo Clinic, and I know it's a pretty common company or pretty well-known company in the United States, but for anybody who doesn't know what the Mayo Clinic is, could you tell us a little bit about it? Yeah, for sure. Mayo Clinic is the largest integrated medical group practice in the world that is not for profit. It is a global destination medical center with three big campuses in the United States in Minnesota, Arizona, and Florida, and also a huge network of regional clinics and hospitals as part of their health system, and they also have an international presence with their London location and also a few of their locations across the world as part of their care network partnerships. It is a fairly large company. There are about 75,000 global employees. They are a very science-led and data-driven organization, so you can find data teams well integrated into like all parts of their organization. So I am not just like, you know, I think there are so many other analytics teams across the organizations that are enterprise-wide, federated, where there are so many folks like me who are doing so many great stuff with data. Oh, that's very cool. And I didn't realize it was international. I knew about the local chapters, but oh, that's so very cool. And, you know, I don't think there's anybody who collects and analyzes more data than scientists and researchers. That is true, yeah. There are very large databases created just for research. Yeah, data is the center of everything. Yeah. So tell me then, as the analytics lead, what do you do? What's your typical week look like? Yeah, so I work for Mayo Clinic Center for Digital Health. So that's a department that is solely focused on all digital experiences that are public and patient-facing, remote patient monitoring, their apps, but pretty much everything digital comes under their purview. And my primary role is in product analytics. And I support all of our consumer digital product teams. My typical work week is busy, and it stands across a lot of things centered around data. So it could be anything between like doing deep dive data analysis for our products. It could be working on data strategy and governance for our data platforms, the kind of sets of the foundation, which impacts data quality and enables other analysts to do their work more efficiently. It also involves gathering analytics requirements and kind of being the data liaison for our product stakeholders. I also mentor my junior team members on technical best practices, data hygiene and stuff like that. Also a lot of work on templatization and modularization work for enabling others to do deep analyses and building the basement for building complex dashboards. So the work can be easily reproduced by others and more efficiently. And also it keeps our data democratized for non-superducers. So a lot of work goes into that too. And in general, my role also involves advocating for data literacy and data and product integration across the organization. And that is through presentations, road shows and seminars, lunch and lunch and stuff like that. So all fun stuff centered around data. Oh, that is fun. Oh, that's fascinating. I love that you have a data literacy program. And during the weekends when I'm not at work, I like to spend my spare time writing blogs and documenting thoughts on data science topics. I also like to spend time in professional orgs like women and data and women and old takes. And I'm also an ambassador for women and data science for the year of 2024, where I mentor folks and write mentorship and career content for them. Yeah. Oh, that's amazing. Oh, I love that. As if that wasn't enough. I'm also a new mom to my wonderful nine month old son. So life has been a little busy lately. Congratulations. Oh, I'm so grateful you took the time to talk with us today. Do you sleep? Yeah, I like to sleep and I'm trying to. But it always isn't. It's taken a backseat the last few months and that's fine. I'm fine with it. It's just a season of life that I am in. Yeah. Well, congratulations on all of that. I mean, that is all very exciting and very cool stuff that you're doing, including being a mom and especially being a mom. Oh, my gosh, that is amazing. You are amazing. OK, so, oh, I love all this data and I love that you're advocating for data literacy. What is like what are the what are the things that you're you're teaching and how are you you said it's some lunch and learns and things like road shows to so across the company? Like what is it that's important to the Mayo Clinic that you're teaching others to understand about data? Um, so my organization has a huge data literacy program and in general, all of our data teams are very much passionate about the stuff we do. So we're not necessarily like teaching, but we're just advocating for data to be part of everything starting from like all parts of the product lifecycle and all that. So it's not a hard job to do when everyone else is already good at it. But I like to do that outside work to to just keep just like demonstrating the value of data and like how important it is to like that be like driving all the decisions. Yeah. Oh, very cool. So important. And so many people I know that are in organizations that are struggling with that. So it's always it's always good to hear that that you've got some some ground underneath you for on those initiatives. So sure, for sure. All right, well, let's pack it up a little bit here. And so tell me, so, you know, is this what you wanted to be when you grew up? Did you say think to yourself like when you were six years old? Like, I'm going to grow up to be an analytics lead at the Mayo Clinic. What was the dream? Um, not at all, but I think my earliest memory about like what I wanted to do as an adult was that I know that I wanted a desk job because that's what all the adults in my life did. My dad worked in finance and accounting. My mom worked on the administrative side of the central government. So it's just all the adults in my life did. So I wanted a desk job and do like adult things. But I think it was around like eighth grade is when I know I wanted to work in like tech and computer science in general. I was I was good at math and my school had a bootcamp program where we learned how to create like applications with Java. And it was not like the like the plain coding. It was a plug and play solution where you could like put things together and create applications. And I was I was just like so fascinated by that. Like so until that point, I knew how to make things fly with like PowerPoint and I knew how to like create like documents and presentations and animations, but like the thought of creating an application by yourself and that you can create a dialog box that doesn't say OK or cancel. You can have it say whatever you want was like so exciting to me. And the more important part was like someone else get to use what you created on like the other side was like, well, that was that that was kind of like really exciting to me as a kid. So this was the early 2000s. So bear with me when I get like super excited about stuff that are very trivial right now, but that's what I wanted to do. Like as a kid, I wanted this job where I could create things that others can use and have it impact their lives. Yeah, that's so cool. I love that that you found that passion early. So so tell me then what did you do? How did you take that passion? What was your next step through school? And yeah, for sure. So my career has been like very straightforward and linear on like display, plainly focused on data. So I knew I wanted to be in tech. So after high school, I got an undergrad in computer science. And then I got a master's in information systems. And then I did my internship in analytics. And data science at Walgreens dot com. And then from then on, it's been all things analytics and data. All of my roles have been either on the e-commerce side of analytics or in customer intelligence. And now I work for consumer insights and analytics. And it's it's been such a great experience getting to play with like huge volumes of data and like finding patterns and like being a detective and like creating impact with the stuff you do. So it's been like pretty linear. I taught like during my undergrad that like I wanted to be in computer science. Like, but what I realized, like as I learned and participated in like externships and stuff like that is that like it was missing the whole like the storytelling aspect that the data carrier has, right? I mean, you don't get me wrong, compiler design is fun. But but it definitely like lacked the whole like pattern finding and just like telling the world about like how the work you're doing is like impacting others. It was kind of missing that connection to the real world. So which is why I decided that I want I want to be in the data space and not in the purest engineering world. Yeah. Oh, that makes a lot of a lot of sense. So I love that. So OK, so where did you go from there? Yeah, after I got my master's, I did my first few jobs in analytics. And then about seven years ago, I applied for a role at Mayo Clinic and it was perfect because they are they're pretty much a premier research and like data organization and like I really wanted to apply my skills and learn more and more within the within the field. And it's been the perfect place to go and I've been there ever since. Oh, it's so amazing. Is there anything you found surprising or anything that additional that you've learned getting into the Mayo Clinic and into a job with data? And in general, I have always been like really surprised by like just the just a variety of things you can do within the like the whole umbrella of having a data career. Like in all the jobs I've taken, my role has evolved like more and more into what I sign more than what I signed up for. Like if I if I if I got to be in analytics, it was not just like doing data mining or like crunching numbers. It's always been more stuff to do like like advocating for like general data hygiene or like even telling and educating your non-data like counterparts in the organization about how how things are working on the data side and how it can impact their their work in plain English, right? Like that whole likely is on part of a data career is something I've been really enjoying. And in all my roles, I've always been surprised at like more and more things you can do within the purview of having a data job. So that's always been consists of a price to me. Yeah, well, you know, that's part of why we wanted to start this podcast. That's because, you know, there is there are so many aspects and so many things you can do with data and so cool that you can apply those skills to data, at least it's like the Mayo Clinic, you know, who are an organization that's literally saving lives, right? You know, and you can do really cool things at any in any industry, any any anywhere any any company, right? Yeah, it is such a powerful resource indeed. More and more 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 Dataversity, 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 dataversity.net. So, so what's been your biggest lesson so far in your career? I would say that. So this is what I would tell my past self, like always remember to zoom out. So as data professionals, it is so easy to get lost in the details, right? We lovingly call it analysis paralysis, but it is so easy to just get stuck in like the very details of the stuff you do. But it's really important to zoom out and like shift the focus into two things, like one, why are you solving for and more importantly, who are you solving the problem for? It really helps to like have some empathy and a sense of purpose for like, like just know your why and exactly like put in simple terms on like, what exactly are you solving for and think from the shoes of the person on the other side and that really opens up your perspective to just zoom out and think about other creative ways to you can solve the problem. So that's been my biggest learning so far. It is so easy to get lost in the details, but it is so, so important to zoom out, too. And I love that phrase, zoom out, because because you're so right. And I found myself, you know, in many times in my aspects of my career, whether I was in data or not, where I so nose and down, but, you know, all down that, yeah, yeah, right. That's great advice and great lesson. So tell me then, you know, I mean, a whole career in data, this is so exciting. So what is your definition of data? I would actually go back to the basics. Well, what we learned in the books, data is a piece of information, but also to add more color to it, data is, in most cases, an objective source of truth and it is definitely better than opinions and assumptions. And that makes like such a huge difference. And I like to say with enough context, like data can move mountains, right? So one data point is the fact, but when you have two data points, you can try to do correlations. When you have three, you can try to fight a pattern, right? So data with enough context, it can really be a foundational and integral part of like all business units and like even all parts of an organization to create impact. Yeah, it's kind of like the underlying fuel that like moves things from the top. Yeah, that's what I would say. Yeah, yeah, for sure. So do you see then the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years? And why? I am a very hopeful optimist, that's just been my personality. And even without that objectively, I would say that it is definitely increasing because like across the industry, more people are talking about data governance and data engineering and there's a clear delineation of like the types of roles available in data instead of just bucketing everything into like one role like a data scientist, right? Like now people know what data engineering is, what data governance is, what data quality association is and stuff like that, right? So but that with those kinds of improvements coming to the industry, I see that there are only more jobs in the future. And with like the advent of like AI and like synthetic data and all that, we're generating more data than ever and that requires the expertise of data management to step in so you can extract value from all of it because like volumes of data without a meaningful way to extract value from it is kind of useless and they need humans to do that and humans were experts in data to do that. So I can see that it would certainly be increasing in the future. And another aspect I'm like really excited about is that there are so many potential ways to like automate the not so joyful part of like your data works. So we all have stuff that we love to do as part of our data jobs, but also stuff we have to do to give it like clean data, right? So I feel like a lot of that with the advent of like the new tools and technologies, like can we automate it? So precious analysis time can be set set set apart for people to do like deep joyful analysis work. So not only that there are going to be more roles in data, but like existing roles are going to be going to become like more joyful and more impactful because now we've got more time to do the stuff we love to do. Yeah, indeed and very, very true. And do you see so for those who may not know, and he defined like a couple of different data roles and the delineation between those that you mentioned, like in data governance and data engineer. Yeah. Once you're setting out to be being in it, be just wanting to carry the data, I think so back when I got my master's, it was focused as focused on information sciences, right? Like, but we learned like through while doing the courses that like it's not just like the statistical data science part of it. There's other stuff out there too, like business intelligence. And with business intelligence, you've got business intelligence analysts and also engineers who enable and enable all the data flow that gives their output to the analysts so they can do their work. So there are definitely like a lot of roles like that within the data ecosystem that helps each of the route to make bring like their best output and the most like valuable output out of data. So that's just a quick example. But there's also like data engineering that sets up like the foundational aspect, especially with the advent of big data, right? Like you got to know how to implement the schemas and like how to make sure like your foundation is perfect so things can be scalable in the future and things can be more useful for the users of your data and also like non-super users who like derive meaning out of your data and all that. So that's an important aspect too. And then big organizations, data governance plays a huge role in like just keeping stuff up to standards. So that's also a role that's part of the big data ecosystem. So it's one big umbrella, but there's so many different rules out there for folks who are like just dipping their toes in. I love it. Thank you for helping to clarify because it is so many is it is so exciting about how many different roles and that are available, like you say, within data. So depending on what your passion is, what you want to focus on and where you would like to fit in, it's so it's easy to find a place within data to fit. Yeah, if you're curious enough, and if you've acquired the technical skills, like there are so many beautiful things you can do out of data, right? I think I forgot to mention about the glorious discipline of data visualization. It's equal parts art and science, right? So like once you know what to bring in to your data plate, there are so many different ways you can tell the story in easily digestible format and data visualization is like such a discipline that that just brings like technical expertise to masses who are consuming the data. So yeah, like so many flavors of rules out there. It's true. Yeah, you're right. The visualization is such an important piece and is a special skill set to be able to use both sides of the brain right to to have that creativity and that visualization and tell that story of this of the analysis of the data, right? Yeah, that's my favorite part of my role. Yeah, it's awesome. I totally get that. I love that. So what advice then would you give to people looking to get into a career in data management? I would say one of the important things to do is to be part of the communities, both at work and outside your work. I've enjoyed and benefited greatly from being part of like data communities at work, be it like a community of practice focused on literacy or even like a technical tool specific user groups where like so many people are dealing with the same tool and battling the same problems and stuff like that. So I've definitely benefited a lot from that. But even outside work, I've been part of communities like the IEEE and the Women in Analytics and Women in Data Associations and it just gives you a general warm feeling that you're not alone out there and everyone is out there working towards solving problems. And that's been a practice that has stuck with me since college because it was a lot of participating data hackathons and case competitions and poster presentations and stuff like that. So I've taken like that part of my academic time into like my professional career too, to like just be part of data communities. And so you feel like you're not doing stuff alone because no one can do anything alone. Yeah, I'm a big believer in that. Right, it's so very true. Gosh, and I wish I had learned that so much earlier in my career. Right, you know. Another thing, yeah, great. So I grew up just thinking you had to do it by yourself, you had to like go ahead, asking for help was, and for advice was weakness, but it's not like it's so essential to growth. No, not at all, that is so true. Yeah, what you said makes like 100% sense to like just, it's okay to ask for help because you're both collectively solving a problem that'll impact someone else out there that is outside you and them. So it's important to look at the purpose rather than just trying to do it all alone. I would also advise maintaining a kudos book, what I call a kudos book. So this is something I learned from like one of the seminars, they organized at my work. I think it was from the data literacy group. They did a session on imposter syndrome. So definitely document anytime when people say nice things about your work, but more importantly document what worked. So you can look at that when you're having a bad day or when you're battling like imposter syndrome, but also like I have relied on the intelligence of my past self so many times on days that have been exhausted and brain fried, right? So if you've got a big book of learnings and stuff that has worked like specifically for you, you will save a lot of time trying to reinvent things. And it'll help you on days when you're trying to get stuff done, but you just don't have the energy to. So that's like having a nice like white old friend who's giving you advice based on what worked. So that's been like the biggest learning that I've had from my career and I tell that to like everyone who's storing a data carrier because that's a game changer. Oh my gosh, I love that. As a person I might have heard of something like that and I think that is so fabulous. That is really a great practice. So I think for anybody in any career. That is true, yeah. You can just write yourself like self-affirmation cards based off of stuff that has worked for you. And that'll go a long way and like just being more assertive and confident even while doing data presentations, right? So it's a really good secret that everybody should be doing. Yeah, indeed. Oh my gosh, that is such great advice. Because there are, we're human, right? So there are always those moments of do I deserve to be here? Like you say, the imposter syndrome, do I deserve, am I good enough to achieve this, am I gonna, you had that self-doubt creeps in. But I love that to go back and say, hey, here's a list of things that I've done well and here's what I've rocked at. Even if this not going well, I know I can achieve these other things and learn from it. Yeah, it would be so easy to repeat what you've done already. Yeah, that's amazing. I really like that practice. Yeah, so, and through this, you know, you said one of my favorite words which constantly comes up, especially with data people is being curious. I think that's so important and so natural to data people. It's just that curiosity of what else out there, what else can I do and what else can I learn and what else can I take on? Yeah, that is such a classic personality. You trade for analysts everywhere and especially data people and you're so privileged to use that trade into doing work and doing impactful work, right? So I think that's just a beautiful thing that you get to use that to impact so many other lives out there. Indeed. Oh, Nithya, this has been such a pleasure to get to know you. I am so grateful that you came and took the time to spend with us and share your thoughts with us. And I'm so glad that you're here. Thank you so much for having me and letting me share my thoughts. This has been a wonderful experience for me too and I am so glad there are folks out there listening to what we've been talking about and like nerding out in the same way that we do with data and I hope to inspire people from what I've shared too. So thank you so much for this opportunity. Thank you so much for being here. Yeah, absolutely. Thanks for coming. You're very welcome. Thank you. Thank you for doing the volunteer curiosity. I think it's really fun to be here and sharing. Especially in your busy schedule, like, oh my gosh, you again, superwoman. You are amazing. Thank you for coming into this. Thank you. Oh, gosh. People want to learn more about the Mayo Clinic, where can they go? They can just Google Mayo Clinic. and find more information out there. Yeah. As you say, it's a great resource for data and for information on health and wellness. It's so great. Yeah. Oh, again, thank, yeah. And if they want to connect with me, they can go to my LinkedIn. I also have started to write on Medium and sharing my thoughts and opinions on data science topics. So that's another resource out there. Is that where all your blogs are in LinkedIn? No, in Medium. I think I shared my LinkedIn to Medium too, yeah. Awesome, and we will get those posted to the podcast website as well so everybody can look at those and start following you. Thank you. Oh, thank you. Nithya, thank you so much again for taking the time to chat with us today. So I really appreciate it. Thank you so much for taking time. And I hope you have a wonderful day. Appreciate all the stuff we've talked about today, yeah. Likewise, and I hope everyone gets that piece of advice on the kudos. I hope they start taking that out. I'm going to, I know I'm going to. If a game changer, trust me, yeah. For sure, yeah, absolutely. So again, thank you so much. And to all of our listeners out there, if you'd like to keep up to date in the latest podcasts and the latest in data management education, you can go to dataversity.net or subscribe. Until next time, stay curious, everyone. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational webinars at dataversity.net forward slash subscribe.