 Hello and welcome to My Career in Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host, Shannon Kemp, and today we're talking to Kristin Foster, the Senior Vice President of Data Science at 8451. 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. Welcome to the newest monthly webinar series from DataVercity, 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 DataVercity.net. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at DataVercity and this is My Career in Data, a DataVercity 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 help make those careers a little bit easier. To keep up to date in the latest in data management education, go to DataVercity.net forward slash subscribe. Today we're talking to Kristin Foster, the Senior Vice President of Data Science at 8451. 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. Kristin, hello and welcome. Hello, Shannon. Thanks for having me. Thanks so much for being here. And so tell me, you're the Senior Vice President of Data Science at 8451. So tell me about 8451. Absolutely. So 8451, we are a retail data science, insights, loyalty and media company. And we work with the Kroger company, CPGs, agencies, publishers and some of our other partners to create personalized and valuable experiences for our shoppers across the entire path to purchase. We are a wholly owned subsidiary of the Kroger company, which is a grocery retailer, currently operating in about 28 banners, 35 states. And 8451, especially my team, we're really focused on building those cutting edge data science solutions and really focus on fueling that customer-centric journey across all those things, across loyalty, insights, retail, media, health care, all of those different components. And I think, you know, 8451, it's a it's a unique name too. So I can quickly explain what what that is. So we are 8451 is actually the longitude of our headquarters in the Cincinnati office. We do have other locations as well. I actually work out of our Chicago office, but it is our longitude coordinates in Cincinnati. And that is because we are really anchored to Kroger. We are a part of the Kroger ecosystem. And we really like to, you know, look at data longitudinally and really understand the connections that it can make to create those, you know, relevant valuable experiences for our customers. Oh, that's so cool. I love that. I love that background of that name. That's that's very unique. It is. But tell me, so what do you do for 8451? What is your typical workweek look like as the senior VP of data science? Yeah. So we are, you know, I would say there's there's so much to think about when I don't even think I could talk about a typical workweek. But let me kind of quickly first talk about a little bit around, again, what what we're doing and then the role I play with that. So, you know, we have data science teams currently at 8451. We have about 300 data science researchers that fall under the function that that I lead and support. And like I said before, we're really we have data scientists and researchers and ML engineers and analysts and data analysts working with our data asset across many different pillars to to develop those capabilities and develop those solutions. So not only are we doing things in retail media and CPG and sites in some of those some of those areas, we're also really working to build and deploy analytical solutions across the the entire Kroger ecosystem. So that will range from forecasting to influence retail operations for an example or store ordering, assortment optimization to make sure we have the right products in the right store based on who shopping those stores all the way to helping our category managers or our merchants make more data driven decision making. A big thing that our teams do, our data science teams do that, you know, Kroger is often known for is really focused on sending really relevant coupons to our our loyal customer base. So taking the data that we have and really making sure we're we're optimizing the offer to to to thank you and reward you for the loyalty that you you provide Kroger by giving you the things that are going to matter most to you. So we do all of that across all of those data science teams across all those different pillars and we're really lucky to have a really rich data asset to do that with. That's amazing. You know, it's so funny when you're shopping, you know, you don't think about those things, but it is it's so important. And and I love that the Kroger has invested so heavily in data science, absolutely those customer differences. So tell me, Kristen, so was this the dream when you were so like, say you were six years old, you know, yeah, what was the dream when you to what do you wanted to be when you grew up? You know, definitely not when I was six years old. I think when I was six years old, I had a whole I made a whole like a school in my basement back in Iowa where I grew up because I really wanted to be a teacher and I still love teaching and developing people. So that part has never gone away. But I always I always wanted to go the teacher back in the day. But I would say even when I was in college, this absolutely wasn't on my kind of even I don't think any sort of place I thought I would end up is where I am today. You know, definitely not data science or technology at that point earlier in my college career, not grocery retail, not in my bingo part. But, you know, now I've been in technology in an technology role for for nearly 20 years and I really can't imagine doing anything else. And I think where it like kind of all started to click for me was back in college and I went to the University of Iowa and I was in business school and at that point, I just I didn't know what I wanted to do. I, you know, I was thinking advertising, I was thinking finance. I didn't know. So I was doing a lot of different things. But I happened to take one course. It was actually one of a summer session that I ended up staying in Iowa City. And I took one course where I really saw like the power of data and business and the power of data and applying data in the right way and how that can impact your business and impact decision making. And we had the opportunity in that course to work with like a local business where we took some of their data and help create loyalty programs to help them understand how to to work differently or engage differently with their customer base to again, going back all the way to almost 20 years later to what I do today to make it a relevant and valuable experience for them. And that's where I was like, you know what, this is pretty fun. I really like this. I like the analytical aspect. I like the data, but I like the application of it. I really, you know, could never be in a role that was too theoretical because I think being able to see it come to life is I really enjoy it. So I went back and I was like, are there are there careers in this? And that's where I started to get into to my first job, which was actually at an insurance company where I learned stats. I learned modeling. I learned a lot of the kind of foundational components that are critical to kind of a foundational data science background in that role. And that's where that's how I got to start the start of my career, at least. That's amazing. And kiddos to Iowa for having that class because so many, you know, especially in business, they leave the data part out, you know, and it is so critical to making good business decisions. So that's very cool. So OK, so tell me, so you get your first job in insurance. Yes. So and that's and you said you started modeling and and so what was your how did your passion develop from there? And then where did you go? Yeah, that's a fantastic question. And I have, you know, like I said, I've been in roles in technology that have been on data for for my whole career. But I have done a few different different things across that. Like I said, it's probably been across three main companies that I've worked at. The one I'm at today, 84, 51, I've actually been here a little over 12 years. So this is where I've spent most of my career today. But before, you know, I, like I said, I started in that insurance industry. And that is where I learned statistical coding and data analytics. And it was in a marketing department. We actually were taking kind of at that point, direct mail and really helping to to optimize those campaigns, optimize the targeting and response modeling that that we did, as well as then measure the impact of them. And, like, again, that's where it almost reinforced how much I enjoyed data analytics and technology and wanted to continue down that path. That was in Des Moines, and I actually just wanted to move to a bigger city. So that's where I kind of took my next step was, you know, wanting to to get to somewhere a little bit different than than Des Moines. And I found myself in Chicago, which I've now been ever since. And then the next company worked as a market research firm in downtown Chicago. And there I also loved it. It was a real fun laid back culture learned so much, a really a culture focused on on development and learning. And there I had the opportunity, which was really good for me to try a lot of different types of discipline in this space. So, you know, I first one was really around, you know, marketing analytics and some of those components. And at my next company, where I was for a little over six years, I was in one role that was really heavy programming and automation really skewed more almost. It was still, I think, technically in the like data science, analytics function or discipline, but really skewed almost towards the engineering front and programming. I learned a lot. I learned a lot about kind of how all the data fits together, how to optimize code, but it was not my passion. But it was really good for me to do. So I did that for a while. Actually, I was in a product manager, product owner role there for a while, where I learned a lot more about the importance of the user experience, how to ensure we're iterating and developing over time and continuing to improve our products and what that kind of road map and cycle look like, which again, I find so much makes me so much better at my job today that I was able to do that. But again, not necessarily where I found my passion. And then I went back to another role at that company where it was more on taking the data and doing the analysis, telling the story, helping our clients understand the impact and how they could use that data differently. And that's again, almost reinforced similar to my first company, how much I loved that aspect. So it was a lot of different paths that helped me kind of reinforce then, OK, now I'm going to my next spot. What do I really want to focus on? And that's where I then decided to come to. At that time, it was called Dunn-Humby, but now 8451 where I've been ever since. And actually, I was introduced to the company through an old mentor, an old manager of mine. He's now one of my very, very closest mentors and allies and friends who was like, you know, you've been working in insurance and financial services so far, like, let me tell you about this data asset I have within grocery, what he was doing and he was talking about it because you think about the grocery data, how much it is, how massive it is. And you think about the regularity of grocery shopping, the frequency, the power of what you can do with that data was so exciting to me. So I ended up coming in and have, like I said, been here ever since and then despite being here 12 years, still going across many parts of our business. So I've been on media. I've been on our insights. I've been in our merchandising before I got into my role now, leading the function. That's so exciting. I love that you have explored so many of the disciplines within data management because there are so many, right? So many different aspects and so many different ways to play with with data. So you mentioned data storytelling, and that's kind of been your passion. Tell me a little bit about that. We haven't touched on that a lot in this podcast. So I'm just telling you a little about data storytelling. So some of the components of, especially in some of my of my roles around I think, you know, a lot of times you think about data or you think about the models or the science that you're building. They don't have the the the the value and the power without having the, I guess, acceptance and adoption of the business on what it's doing. So even if it's like a model or data that's integrated into a system, being able to make sure people are understanding what that data is doing, what that science is doing and how it can be applied is so valuable to get that adoption again, whether it's fully automated or whether it's something that then they're using the output to make a decision on. So I found that like being able to take all those components and actually be able to work back and translate what it means and what it matters being one of the most critical skills I've been able to develop because that's when the power comes. That's when the value comes with the data is when people understand how to use it and what it can do and how they can make decisions with it. So, you know, I, whether it's through kind of some of the the visualization that we're able to do or even some of the articulation of what it is and why it matters. I found a lot of interest in in my current role, which is a little bit different because I'm not doing as much of the day to day. I found like one thing I love doing is continuing to help people improve like data literacy and understanding the impact. And so much of that has to come through through that storytelling aspect of taking really technical things and making it consumable for for everyone that needs to be using the data and insights to make their decisions. And your your initial passion of wanting to be a teacher certainly comes out right. Very much so 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. I love that throughout your career, you have been very curious. You've explored different options. You've followed your passion. You have a mentor. And so you're learning and getting advice and getting help. And I think all very important things. So so tell me what. What was your biggest lesson so far in your career? You know, that's a great question. I think it's probably a few things. I don't have the biggest one or few things I've almost already touched on that I can that I can reinforce the first one. I there's might be a few so you can tell me if I'm going too many. The first one is like there is no one path. And I think that's what's really exciting, especially as you start a career in technology. There's just so many different directions you can take it. So don't be a for me at first. I was like, oh, can I actually try this? But it was so good for me to explore and try different things. Because one, it helped me help me determine my passion. It also helped me determine like what I was good at and confident at and maybe what I wasn't as as strong in. But all of those things really helped me kind of round me out to be where I am today. So, you know, everything I've tried, I think it's just really made me kind of better at what I'm able to do today. So the fact that there's not one path to follow. And then the second one that I think is really at least been really, really important to me personally is around that network of people surrounding you. So I, you know, I think having I have really strong mentors, but not just one, it's almost like, and I heard this term used many times and I like to use it and have adapted it myself like your own personal board of directors and really making sure that group of individuals is going to count that one, they can help lift me up when I need to be lifted up because I do, you know, there's always always that confidence. Like, am I, do I have a right to be here that creeps up with me that it's good to have those people like being like, Hey, remember, you did all these things. Yeah, you have a right to be here. You proven your, your kind of ability to do this, but also not be afraid to challenge and help be, help identify the areas where, you know, I might have gaps or I might need to approve or call, call me out when they're seeing things that maybe go against either, you know, my personal values or what I, where I need to continue to grow. So that's been another thing that's been really valuable and a lesson that's taken me, I've always really valued relationships, but the importance of having the diversity of different backgrounds, different industries, different 10 years of people has been something that that's been really valuable to me. And I'm going to say one other thing, which is like advocating for myself. I did not do that really in my career. I did not, I just kind of went with the like, okay, I'm not going to, now I advocate for myself. I'm not afraid to highlight what I've done. Well, not afraid to speak up about what I, what I want to do and why I believe I have a right to do it. So that's probably the third lesson that I've gotten much better at in recent years than I was when I started my career. Oh, all great lessons. And I love that you have a board supporting you. That's actually the first time I've heard that. And that is, oh my gosh, that's so fantastic. That is, that is amazing. And I love that you are, you know, that, you know, we always, so many, I, you know, I talk about this on the podcast all the time, that, you know, I, you know, had this perception that, you know, you have to be perfect, like I had to go into my job and have to be perfect and I had to know everything and had to know, you know, but, but letting go of that and learning and, you know, and, and it's going, hey, where do I need to learn? It's just so important and so nice to learn early. Yeah. And I found that like when you are willing, like no one knows everything and no one's perfect. So being able to like be humble enough to recognize where this isn't my, I don't know or I need some support or, you know, I'm not the right person to do this. Actually, I think really helps build your, build trust with your teams and your peers and that way too, because I think it is, it is so true. Yeah. So tell me, Kristen, so having worked with data since the college, then, you know, what is your definition of data? Oh my goodness. So that's, there's, there's so much, like now, right? The world is so, so digitized, like it's just everything feels to be data right now. I will say here at 8451, since, you know, we, we're a data science company that is our bread and butter that, that's the heart of what we do. And we like to call data, the, the lifeblood, like this is the lifeblood of our organization because everything that we're doing is leveraging data to be able to solve these business problems. We want to use that to make sure we have the right products at the right store, at the right price, make it as easy as possible to shop and both in store and online to be able to, you know, really reward our customers with the things that matter most to them and making sure that they're able to get that experience that they expect of us with the, the loyalty and trust they provide. So it's really all fueled by data. And then when you think about it and it's like data and it's like raw form, right? There's facts, statistics, information and being able to cobble it all together into the right thing is where I think there's just, there's so many, so much that you can do with it. With grocery in particular, you can all, we can all, we all grocery shop and we all eat so we can all, everyone pretty much can relate. But when you even think about the grocery receipt that you get when you leave the store, think about all just that one piece of paper, how much information is on that that's so powerful, you know, like the time of day, you know, the store location. If they have like a loyalty card, you know, kind of that's tied to it. And then you can look at each individual, each raw, like metric that exists and learn so much, so much advice that was paid and everything, then you can start to create derived metrics and derive data from that to say, oh, this person bought 10 things, but six of them were vegetables and fruit. There's a heavy produce trip and then start to make it. So that's another kind of format data and then go to the inferred data almost to say, all right, they bought, I don't know, tortillas and avocados and peppers and chicken and beans and rice. They're having a taco night. So you can start to think about all that you're able to do. And that's just one tiny piece of paper. When you think about how often people grocery shopping, how everyone grocery shops, it really kind of lends itself to think about how many different ways you can develop, derive and create data from those from those experiences to really then create the things that matter most. So true. 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? You know, it's really like interesting. I definitely see it increasing. And I think I mentioned it before, right, the amount of data we have today was 10 years ago or 10 years prior. It's just like drastically, drastically higher. So I think it only continues to like increase or highlight the importance of managing data effectively, efficiently is going to be. And the other thing I think is really important because of the increased data, whether it's a role in data management or any other role that someone takes is the importance for everyone, regardless of if they're in a data specific role or not to continue to increase like that data literacy because it can't be limited to technologists anymore. So that's another interesting kind of lens that I always consider as we think about how much data is increased in addition to to data management. But I do think, you know, the the number of roles in that data quality data management is going to only continue to to increase, you know, my role is data science. So I know that what we're able to build and create is only as good as the data that's that's being that's being captured and the feedback loops on that data and the ability to access that data. That is that is what matters most for us to do the things that we need to. So with so much more data at our fingertips, across no matter what industry you're in, I only see that becoming increasingly important. Now, I do think most likely that there's going to always be, which there always is shifts in the types of data jobs that exist, especially as you think through technology and how much has shifted with, you know, different technologies and how that might change, you know, data cleansing and other components of the data management life cycle. But I don't see that changing the importance of the number of roles and the types of roles that that will exist. Indeed. Yeah. You know, there's so many questions, you know, with generative AI out there and, you know, how is the landscape going to change? But I think you're right. I mean, we have seen and maybe you have some advice for, you know, even we've seen so many companies try to stand up machine learning and try to stand up AI. But and then fall flat because they forgot to do the data prep. They forgot to do the cleansing. There was no data governance or quality built in. And I was like, oh, we need a data modeler. We're seeing so many data modeling jobs come be available. So so many brand new data modelers because suddenly they're like, oh, we need a data model in order to stand this up. Is there a how have you been able to stand up that that new tech and keep up with a new tech? Yeah, it is, you know, the tech's moving so fast. It's like every day we're learning something new and there's something continuing to come forth. But I think quite what you're highlighting is exactly where we're at in the sense of, you know, especially as more and more of our business counterparts are hearing about the technology and the impact it can have like so much of our time right now is like, absolutely. Like, yeah, Gen AI can do so much even when you think about access to insights and ability to derive from data, but it's still based on that foundation. So we're doing a lot right now with like roles like the data modelers. They're some of our data analytics teams to focus on how can we use some of that technology, especially some of the advances in Gen AI as an example to help with our like backend data components. So then we can start to show the value that can have to then get to a different user experience or kind of front end changes that we're making as well. But there is so much changing on a daily basis and so much kind of foundational things that need to be true in order to get value out of that technology. And again, you highlight data governance and we have science governance, which are such critical components of that foundation to have really established before trying to kind of deploy anything with those new technologies. Yeah, very nice. Well, Kristen, this has been fantastic. This is just amazing. Thank you so much. So I'd be remiss. So how would people find out more about 84.51 and what you all do? Yeah, so feel free to reach out to me on LinkedIn. I'd love to chat and connect with anyone that's interested. And then I'd encourage people to go to our website, which is 8451.com. You can see a lot more about us. You can see roles that we have across data science and engineering and other technology roles that exist and find out a lot more about us. Amazing. Well, thank you so much for taking the time to chat with us today. Really have enjoyed this. This is some great advice for everybody out there. Yeah, thank you, Shannon. It's been fantastic. And to all of our listeners out there, if you'd like to keep up to date in the latest in podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe. Until next time and stay curious, everyone. Thank you for listening to DataVersity Talks, a podcast brought to you by DataVersity. 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