 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 Kempe and today we're talking to Hillary Ashton of Teradata. 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 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 Kempe 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. Now today we are joined by Hillary Ashton, the Chief Product Officer at Teradata 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. Hillary, hello and welcome. Hi, Shannon, it's great to be here. Thank you so much for having me. I'm excited to be joining the DataVercity podcast. Wow, we're so excited to have you here. So you're the Chief Product Officer at Teradata. So what is Teradata and what is it that you do? Teradata is a cloud and analytics data leader. We have a new cloud native platform that we just launched and it's really the market's most robust analytic capability for driving AI and ML at scale. We serve some of the largest companies in the market. Our customer base includes like 17 of the top 20 global commercial and savings banks, eight of the top 10 retailers. So we have got really big, big, big data customers. When you think of big data, you can think of the data that Teradata has. We've been around for decades. We were born on premises and over the past several years we've made a really exciting journey into the cloud, really transforming how data comes to life through AI and ML in the cloud at scale. I think you also asked what my role is. So I'm the Chief Product Officer and that means that I build the product strategy and direction for the company. But I don't do it by myself. I have a brilliant and really dynamic team of super smart people who are experts in data and analytics. They've solved some of the largest, toughest problems with data in the world. And I'm also a mom and a wife and a big Boston sports fan. Oh, very nice. I love it. And just to clarify, for those who may not know, AI is pretty straightforward and common, but ML is machine learning just for everybody's information. So, you know, tell me, Hillary, you know, is this was this the dream? Like, did you dream I'm going to go be a Chief Product Officer for an analytics company? And when you were a little girl, I mean, this is what was the dream? My ceiling, thinking about being a Chief Product Officer at Teradata. No, as it turns out, that wasn't what I was doing at that time. But it's really interesting when I was growing up, my dad actually used to call himself the database guy. He worked in data and I'll say analytics, but really, at that time, it was much more data and computational stuff. He was actually an intelligence officer in the Navy and worked on some of the largest mainframe systems in the 70s. So he was really very sort of my first spark, I guess, I'll say, comes understanding what data could do. And, you know, I remember as a little girl driving in the car to a soccer game and he would kind of tell me about what he was working on. I was like, oh, that's kind of cool. I'm going to go play soccer now. So, you know, all throughout my early life, I had kind of an education in data and kind of the power of data. But it wasn't until I was a senior in college that I started to become much more interested in the early opportunities in the internet and really some of the early opportunities for data at scale. So that was sort of the beginning of my journey, I guess I'll say. That's fascinating. That's very interesting. So as you started really making decisions about what you wanted to study, what was that? What did you start focusing on? Oh, my gosh, you're going to kick me off the podcast right now. I was a government major. So I was a brilliant data scientist and analytics and engineers. But I am a liberal arts, a product of a liberal arts education in government. Wow, very cool. That is very interesting. How did you progress from there into into what you're doing today? What was the path there? How did you what what what did you learn? And where did how did you transition between jobs? I think when you look up non-traditional career path, like maybe my name will start to come up at some point, probably not. But so I, as I said, I started getting involved in some of the early Internet work that was going on. I'm dating myself a little bit here, but I built a work of teams that built some of the first, first large websites in the world. Really early days and really interested in things like web analytics and really understanding customer journey mapping. And then I joined a company called SAS. They're still around today. They're one of the originators of data and analytics. In the big data arena, I spent 10 years there really understanding data and analytics and certainly absolutely the technology had a knack for really understanding the the logic behind technology, but also really understanding the value that data and analytics produces for large, large scale customers. So that was my that was my really early part of that from there. I don't know if you want me to go on. I can tell you just a couple other stops. Yeah, please. No. Yeah. From there, I worked for a software service company based out of Bangalore, India, which was super exciting and learned a lot about, you know, taking a company that was founded in in India and how to make it relevant in the US market. So I ran the the team here in the US. And then from there, I joined PTC and PTC is an IOT and and CAD. If you think of like coming up with the design for large scale machines, they do a lot of CAD work in in that space. And I was a general manager of their augmented reality business unit. So I had all kinds of exposures to computer vision data, which is really understanding if you use your phone to scan something in the room and understanding the room. Now you can actually do that with machines as well. And so we took IOT streaming data, massive amounts of data. Some is useful, some is less useful and really making sense of that data through computer vision and also through IOT sensor data. And then I came to Teradata and had this great opportunity. I knew about Teradata when I was at SAS. I knew about the power of Teradata. And I had a view for how we could really transform the value that we bring to the market into the cloud. And I've been here for the past three and a half almost four years now. Amazing. So what skillsets do you find that you're using in your job and from the transition from that, you know, nontraditional career path? You know, as you say to us, as most of us who get into data, right? To follow this, this is not ever a straight path. But so what we're so you have this passion for analytics and so you're bringing all that and what skills are you using in that job today? Yeah, I think it's a couple of things. I have always been super curious about how customers, our customers, get value from data and technology. And so it's not really data for data sake or technology for technology sake, but really what could you use that data and technology for? And so can you use it to think about a cure for a disease? Can you think about it to identify fraudulent behavior? Can you use it to figure out what's the right best offer to give somebody when they're at a checkout line or something like that? And so really understanding how these different data sources come together, being super curious about technology and how it works and then how it provides value. So I think that is one of the super skills that I have. I also really understand, I think, the where the market is going from an artificial intelligence and machine learning perspective. A lot of our audience has probably heard about generative AI or maybe they've heard about GPT. If you haven't checked it out, highly relevant in the careers that you're probably working on right now. So the idea of disruption, the Internet was a big disruptor, right? I think computer vision is a big disruptor. I think that generative is another disruptor. And so how keeping an eye on value in the time of disruption is also something that is super important. And I'm pretty good operationally. I'm a pretty good people manager. You've got to ask my team if they agree with that, but really focused on developing empowered teams, operational rigor and agility and execution. I love that. So, you know, speaking of teams, 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? Absolutely increasing and increasing in ways that I don't even think we can begin to imagine right now. I think whenever we have some of these disruptive things that happen, I think there's a concern that jobs are going to go away in the technology space or in the knowledge worker space. That generally hasn't been the case. I think what we see is people evolve really, really rapidly and new jobs become available. I'm speaking specifically about generative right now, which I'm really excited about. So I think that is, I think will be increasing. I think that the team piece is also going to be increasing. And so if we look at the maturity of data and analytics workers, we've kind of gone from an ivory tower, you know, super data science, PhDs. And if you're out there and you're a data scientist and you've got a PhD, you are awesome. And also there's a whole bunch of data users who don't have that level of expertise. And we need to bring that understanding of data to the business and to really building that bridge between what data is, how it works, why it's useful into the business driving really amazing outcomes for the business. And so that understanding of data growing, those jobs are going to grow massively. And also the more that you can understand the business, I think the more successful you will be. And that requires collaborative skills and seeking to understand the data and the business side. 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 DB Talks for 20% off your purchase. I love to hear that, especially since you've mentioned artificial intelligence a lot and, you know, specifically chat GPT, which is, you know, hits mainstream media and there's a lot of discussion around that is whether or not it's going to eliminate all the jobs, you know. Things, I mean, you know, you're an expert. I think it's going to change things pretty substantially for people, but humans, you know, if we think of ethics and we think of trust and we think of ethical data, ethical AI, ethical ML, that there's there's a whole bunch of work that humans will have to do around the trust of data and the trust of the AI and ML and generative space. So that's go liberal arts students. If you're a liberal arts kid out there and you're thinking about data, I certainly would encourage you to join, join those together. I love that. So what advice would you give people looking to get into a career in data management? So it's a very exciting space right now. I think you have to find a place that you personally find really exciting and interesting. I think if you're doing it because someone told you to go do it or you think you're going to make a fast buck over here, you know, careers can be long and data and analytics can be tough. And so you really need to find an aspect that you think is exciting. It could be that you think you can help cure a disease or you think you can help with poverty. You think you can help with certain aspects of how data can really drive data for good. It could also be that you think the math is really cool around analytics and you don't want to get super involved in that or you love technology and data is just a part of technology in your frame. So find something that excites you, find a reason to get out of bed and go get after it every day. And then I think look for places where you have a unique skillset. You might be the fastest programmer out there. You might be somebody who really thinks deeply and solves big picture problems. You might be awesome explaining some of this to new workers and to customers and really solve problems through the data. So lots of opportunities out there. Find something that you're passionate about that you want to really focus on. Such great advice. And it's a common theme that we're hearing for many people is find your passion and get curious, right? And do you think curiosity is a skill that can be learned and practiced or is it something that you either have or don't? I think it's something that can be practiced and improved on. I think humans generally tend to be fairly curious. Just as, I mean, it's part of what's kept us alive as a species for so, so long. If we weren't curious, we'd all have been drowned in a river or I don't know, burned by fire or something probably a long time ago. So I think adapting and being curious is part of our deep, deep DNA. I think sometimes in cultures, meaning corporate cultures or even family cultures, that curiosity can be tapped down a little bit or maybe frowned upon. Maybe you shouldn't be a hand razor and ask questions. So I think in order to flourish with your curiosity, you need to find a good environment and maybe you're part of someone who can create that environment for yourselves and for your teammates to really ask the why, ask the how, ask the what else could we do here? So I think making sure that you're putting yourself in an environment where curiosity is rewarded and where mistakes and errors are, people are just curious about it and then figure out what we're gonna do differently. And in that case, I think framing that for teams is super important too. I think it helps us innovate much, much faster when we're curious, we learn and fail fast as they say. But really be curious about the failure so that everyone can learn from it. Oh, important, such good advice. So many people think that the failure is, you try to avoid it, but that is how we will learn, right? Absolutely. So one of the first people to bring up some of the cool things that data can do, right? Like you mentioned, improving health, solving global problems, what are some of the cool things that you've seen come as a result of data? Yeah, I mean, I could just reflect on a few areas and we could talk about this for like another two hours because I love use cases. I use cases of how data solves world issues and local issues is pretty exciting stuff. So during COVID at Teradata, we had customers who had very different data needs, right? Some medical suppliers were spiking in their need to understand data and analytics. Supply chain became a massive problem in getting materials and goods to healthcare workers, frontline workers, but also automotive supply chains, airline supply chains, right? And so being able to help our customers with the data that they needed during the Black Swan event, right, another data Black Swan event tied to the terrible COVID problem was something that really led to some remarkable transformative opportunities for customers. I think in other parts of the business, there's, you know, if you think of banking, I think fraud is a super interesting area and it could be super nerdy. We have a customer that is doing something to prevent remote access takeover. It's called RAT and it's where someone's trying to pose as you on their website. So they log in and they say they're Shannon, but actually we can detect through data and analytics in real time that that person is not in fact you because you're not in Japan or maybe you don't normally browse the website in this particular way. Maybe you don't wait on this particular page for so long. So through biometric information and web analytic information. So there's a lot of interesting places that data and business problems can come together and really help solve big problems and medium sized problems and even small problems. So those are some examples and we could talk more about retail examples. We're doing a really cool a generative AI use case with smart shopping carts in the Middle East where we're looking at understanding the sequence like a sentence is a sequence, which is really generative a sequence of what you know what people might normally buy in what order in a store. And that's on the planogram, right? The layout of the grocery chain. And so if you're online, we know how to promote things into your basket because there are no aisles online. There's just sort of a website, but in a store, you maybe go to the fruits and vegetables section and then you hit the dairy section and then you're in the middle of the grocery chain or the grocery store looking at box goods, right? And then you probably hit the bakery on your way out. So the sequence in which you might buy things is really a sentence structure. We can look at the basket that you have based on the shopping sequence that you've done so far and we can recommend the next word in the sentence to you or the next item that you might wanna buy. So that's a really cool and exciting use case of data and generative massive language models coming together and so that's an exciting area too. So we'll probably stop talking about that because I could tell you many, many more stories, but I think where data comes together and solve super interesting problems is pretty exciting. So exciting. And I don't know that it's one of my favorite things about being involved in data is, we get to work with so many industries and across the globe, it's just relevant to everybody. And so I love that idea of somebody coming into data, mirroring it with a passion and just, it's just a really happy place. And so, and I wanna ask you about this too because it is such a topic, chat GPT. So you say, go check it out. So what is your take on it and the use case for it and where is AI going? I think that we are, again, I'll pull the lens back out and say, I think it's a pretty significant disruptor for us, a massive, massive disruptor for us. Normally when you're in the beginning of a massive disruption, you actually don't know what the use cases are. You think you do. You have hypotheses, awesome. You should always have a hypothesis, but oftentimes I'll just say either it's a dead wrong or it massively evolves and you couldn't even recognize it when you look at it from far away a few years later or a decade later. I think funny story. When I was really, I think it was two years out of college. I'm now totally dating myself here, but I left a company and I joined a company that was really focused on the internet, as I said. And my boss at the old company was like, oh, the internet's gonna go the way of the CB radio. It's really just a way to like talk to people and it's really not that, you know, I guess big of a change. So I think that there's, in a similar way, I don't think that several decades ago, we would have imagined what the internet was gonna do. I think generative is gonna be the same, but I do think it's gonna have a substantial shift in how we do our work and how the world works. And with that, I think there's definitely some ethics that we're gonna have to think about. There's some pretty serious trust issues that we're gonna have to think about. And I think it's gonna be up to corporations, like Teradata, to think through and help customers think through the implications of the super exciting technical capability with what you want to do with it. And so we see some of that friction in the market with some thought leaders who have had some pretty significant data points to read out to us. So I think the world is watching. I think we're gonna have to be excited about the technology and the art of the possible and also think about some pretty big macro issues like humanity and what we want from a trust and ethics perspective. What kind of world do we want to live in? And what kind of world do we want to create for others to live in? How important. And I'm so glad to hear that that's at the forefront. Yeah, I agree. So I'd love to, you know, I could probably have that conversation for hours too. That's such a hot topic in the world of data right now. It's about data ethics and making that a priority. Oh, Hillary, this has been such a great conversation. But I would be remiss if I didn't ask, you know, how do people find out about Teradata and the exciting new products that you have? Well, thank you for asking. You can ask chat GPT. You can also go to teradata.com. You can hit me up on LinkedIn. I'm pretty easy to find. I'm Hillary Ashton at LinkedIn and yeah, we're doing some exciting things we are hiring. So we are a profitable growth company. And so for folks out there who might be looking for their next opportunity, you can take a look at our careers website. Oh, very nice. I love that. So we'll get those links from you and we'll put that on the podcast page so that everyone has access to those links. Well, Hillary, thank you so much for this interview. It's been such a great conversation. I really appreciate it. Anything else you want to add before we sign off for the day? No, Shannon, thank you so much. This has been fantastic. I love what you're doing with Dataversity. I think that the opportunities for folks out there, no matter where you are in your career, is stay curious, find something that you're super passionate about and then help others, right? Help others in their journeys as well. And we will take advantage of some of these new exciting areas and we'll also be thoughtful in terms of the implications for them. Thanks. Oh, pardon. Thank you. Well, Hillary, again, thank you so much for taking the time to chat with us today. And for 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, go to dataversity.net forward slash subscribe. Until next time. 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.