 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 Emily Washington at Precisely. Visit dataversity.net and expand your knowledge with thousands of articles and blogs written by industry experts, plus free live and on-demand webinars covering the complete data management spectrum. While you're there, subscribe to the weekly newsletter so you'll never miss a beat. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is My Career in Data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help 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 Emily Washington, the Senior Vice President of Product Management at Precisely 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. Emily, hello and welcome. Hello, thanks for having me. So you're the Senior Vice President of Product at Precisely. So tell me what is Precisely and what is it that you do? Sure, yes. So Precisely is a technology and services provider. We provide a combination of software data as well as strategic services to really help organizations ensure the utmost data integrity. We are a leader in data integrity. We have over 12,000 customers and the way we define data integrity is ensuring the accuracy, consistency and context of data. So through a combination of all these things and obviously a lot of what I'm expecting we're going to talk about today, that's the world I live in. I get to drive the product strategy and the roadmaps for the capabilities that we bring to market through particularly focused on data quality, data governments and master data management as well as I get the pleasure of being able to work with taking our portfolio of products across Precisely to more of a SaaS model. So being able to support our customers in this journey to leveraging some of the newer technologies out there. So I drive product management for all those areas. It's very cool. So we'll come back to a bit about how you're working with data. But tell me a little bit. So when you were in elementary school, when you were very young and then you know, is this what you wanted to be when you grew up? Like, was this the dream? I'm going to be a senior vice president of product at Precisely. No, far from it. And actually it took me all the way through to, I don't know about you, but you've been just going through all the way to university. You know, you have this idea in your head. I wanted to be a teacher. I wanted to and I just, it was one of those things. I always admired my teachers in elementary school and I thought it was just so fun to be able to work with kids all day and be able to teach them. So I actually went to school to become a teacher. And then as I got through university, I started to bring more focus to English literature. I have a passion for reading and and I just say, I really wanted to I by that point, I was thinking, OK, high school English teacher. That's what I wanted to do. And I ended up working for a dot com during that whole that whole era in Silicon Valley as a as a job while I was going to school. And that's how I got into technology and, you know, just and how it kind of went from here. So it was definitely nothing I sought out to do. If you were to have asked me then, if this is what I would be doing, I would have thought you were crazy. So it's been quite the journey. I love hearing people's initial passions, right? It's so interesting. So tell me then, so OK, so you got into this dot com job. So how did you transition then from from that and, you know, wanting to be a teacher and to to where you are today? Yeah, so I so when I when I started working for this this website and, you know, I worked in customer support and obviously there's a lot of technology behind that. So I started to work with developers and web designers and the technology aspects really interested me. So as I as I went through, you know, and I was wrapping up getting my degree in English Lit, I also was it was working at that organization. And like the like the tale that many of us have, we the the dot com, you know, ended up folding and I ended up getting laid off. And that's what led me to technology. I I from there, I went to a couple of organizations focusing on software providers, but my journey to data and how I got into data, you know, data integrity and the things I do today. It evolved from, you know, starting at software organizations, doing reporting and a lot of a lot of more administrative capabilities for, you know, our development organization. And then, you know, just learning more about what you could do with data and how software can support that. How do you automate things? You know, we use our spreadsheets and, you know, manual documents and all those sorts of things. And I just had this passion for, you know, figuring out ways to automate some of those activities. And so I landed at another company where we were focused on data quality and data governments. And I have been with that company since. So precisely acquired the company I I had come from back in 2021. And so I've been with the variations of precisely for 20 years now. So it just worked my way into various roles to how I got here. Well, that's exciting. And it must have been really exciting to be in the heart of it. And, you know, at the Silicon Valley, as it started to explode. It was the energy, you know, even, you know, some of the things that we see today with technology and just all the all the innovations that constantly come out to make data better. You know, it's there's so many interesting things that we're always able to do. And so the energy just in this space is is what just drives me to want to do more and learn more. And, you know, and I and I end up getting, you know, new opportunities, which has been really fun. So I love that. So do you currently use your education and teaching skills then and what you're currently doing? I so I have always said it. I always try to tell like my parents this, you know, what? What did you get that English letter to her degree for that? You know, and it's the communication skills. So I so regardless of what role you are in an organization, I feel like communication is key. So while I can't directly apply what I learned about Shakespeare to my data management job today, what I what I do, what I do bring with me is just the communication being able to understand where people are coming from. You know, one of the things that I love about my my job is just how do how do we problem solve? How can I solve it? Bring solutions to our customers that, you know, just it just helps them make their jobs easier and, you know, be able to focus on the things that matter. And so being able to listen, be able to clearly articulate what, you know, we're trying to do, you know, and I really attribute that to just, you know, that background that I have, that love for communication as a part of what I do today. It makes so much sense and so important. And I love that you have a passion around automation, you know, is that that resonates very well with me. I love efficiency and automation. I mean, imagine all the time we've wasted with our spreadsheets and the frustrations. And so, you know, there's got to be easier ways to do this. Yeah, yeah. With a robust catalogue 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 catalogue at training.dataversity.net and use code DVTALKS for 20 percent off your purchase. I love it. So tell me, what is your definition of data and how are you working with it today? Yeah, so so I my definition of data, I don't know if it's oversimplified, but it's just a collection of of attributes to allow me to, you know, get insights into, you know, information that I need to be able to solve problems, you know, and so so I do, you know, when I think about data specifically, it really is, you know, those specific elements. But the combination of those is what gives us the information to make those decisions. And so, you know, in my role, I get the pleasure of dealing with data from two different perspectives, that, you know, obviously, there's the products that we deliver. And so a lot of what I focus on is, you know, trying to better data quality processes, implement better data governance programs in a more automated fashion, but also for me personally in my role in how I make these products better is, you know, the data that we use with our, you know, understanding our customers information and, you know, everything from the customer satisfaction to all of the metrics that we have around, you know, how our business is growing. And so, you know, being able to so I it's an interesting thing for me because I I take some of those frustrations and those things that I do with my own data and apply that to how we get to develop solutions for, you know, for our customers. And so, you know, at the end of the day, whether it's precisely or data versus you, we're all dealing with data every day. And so so I get to kind of deal with it on both sides to see if there's better ways to do to do things when it comes to data. I like it a lot. That's very, very interesting. And so as you're working with data, especially with the, you know, touchpoint with so many customers, you know, do you see the importance of data and data management, the importance of data management and the number of jobs working with data, increasing or decreasing over the next 10 years and why question. Yeah, no, that's it. And it's an important question because I think, you know, there's there's a lot of things that we've implemented over the years to make data processes better or to make data more available to users across across the industry. But one of the things that I don't I think that the jobs will increase, but the way that the jobs are the types of jobs that are needed are going to evolve. You know, you look at some of the automation tasks, you know, at the end of the day, I always look at it as, you know, the intent here is not to it's not to replace jobs with with tools. It's more about how do you make those jobs more efficient and focus on the the true analysis, not having to fix the problems or be able to manipulate data to get it to the point that you need those pieces should be easy. Now, what can you do with it? How what insights can you derive? So I think the roles in data are going to continue to increase, but it's going to continue to take different forms as we as we keep going and as more automation is built in. That makes so much sense. And what advice would you give to people looking to get into a career in data management? Yeah, you know, one of the things that you asked me at the beginning, what, you know, is this what I sought out to do? And, you know, one of the things that I realized naturally is just that that love of information. I am just curious. And, you know, one of the things that worked for me is, you know, just being able to better understand any data that's in front of me and taking ideas to, you know, you know, folks that I worked with or for, or, you know, I've always had a nice support system in terms of, you know, collaborating with others around data. I think everyone is hungry for more insights. And so, you know, whether you're on the technology side of things or you're a business person who's looking to gain more insights to drive, to drive more intelligence and, you know, come to those decision-making abilities, it's really about, you know, just inserting yourself in, being curious and trying to try to get more sense out of, you know, the data that you are looking at because, you know, everyone is trying to get those insights. And so I have found, you know, whether it's me, you know, my personal experience or working with it with different people across organizations, it's just that collaboration, getting your hands dirty, if you will, around the data that's available to you and being able to drive those insights. And then what happens is, you know, maybe more questions come up and then you have this ability to get, you know, get more into other programs or other projects that involve data. And so, you know, it doesn't really matter where you're coming at it from. You know, my experience alone, it was, I was just doing reporting for engineering team. And, you know, I was curious why we had so many bugs. And, you know, it's so that I start to recommend, you know, some new ideas. And that's, you know, that's what got the ball rolling for me in getting more and more into, you know, data management and a lot of the things that I get to do now. That's such great advice. And it seems to be a running theme that we seem to come across with a lot of the people that I've been interviewing, you know, it's being curious, you know. And it's okay to be curious. It's okay to ask questions. It's okay to not know. And I really do think people appreciate when you bring those questions of, you know, it's it, you know, because that just as much as we're curious, we're also wanting to help one another, you know. So if you're on the receiving end of that curiosity or question, you know, that ability to help someone and, you know, start to work together, you know, that's what triggers and all these great ideas and these projects that we can keep working on. Because at the end of the day, we're all just trying to improve the way we work with data, you know. And there's multiple ways. And the great thing is more and more individuals across the organization have that opportunity. You know, that's also why I think, you know, the number of jobs will continue to increase. You know, it used to be more of an IT centric role. Now, regardless of who you are in the organization, you know, you hear so much about, you know, data first and we're a data-driven company. And, you know, so everyone has a role to a part to play in how we work with data, how we make data better. And ultimately, you know, drive those, drive those efficiencies and business decisions we're getting at. So true. So many jobs, no matter what the title includes working with data now. Exactly. And that's what I love about this space. It's just so, it's so fun to look at any role in the organization. And, you know, when you think about that definition of data, you know, it's just, it's a bunch of attributes. It's, you know, but regardless of who you are in the organization, you're always looking at it. You always need to rely on it to do your job. And, you know, so how do we just make that better? So true. I think one of the, the favorite parts of my job is, you know, we get to work with every industry. Mm-hmm. Yeah. Across the globe. Yeah. And to your point about data people and answering those questions of curiosity, I have found the data community to be so supportive of each other and loving. And like you're talking about just loving to network and loving to help each other out and lift each other up. It's just really an amazing space to be. There's so many lessons learned. You look at the different industries and, you know, the different geographic regions. And, you know, we've, there's ways that we've all gotten to where we are and there's certain drivers for implementing certain types of programs or, you know, efficiencies. And so learning from each other and how we've done that and, you know, take some of those best practices, whether it be from a certain industry or certain type of organization to another. You know, I think we're all just so hungry to figure out, there's got to be an easy button for some of this stuff. Like there's got to be an easier way. So when you're looking and you're curious about automation, is there a kind of a thought process, a standard thought process that you go through or is there, you know, how do you discover those things to automate? Yeah, you know, it depends on, you know, when you think about the different types of capabilities associated with data management or in my case data integrity, where we're focused on, you know, improving the accuracy of data, it depends on the use case, right? I mean, you know, we'll take data quality as an example. I think, you know, there's these general assumptions that, you know, it should be easy to recommend where you need data quality based on historical patterns. And while that is true, it should be easy and there are ways to do that. You know, a lot of the, you know, and there's amazing technologies out there to be able to drive that automation, but you're never going to get rid of the true human in the loop aspect of it. There's always going to be some level of intervention to make sure that, you know, that this is right. And that's where I think the automation with, you know, the users who are actually working with that data, you know, really complement each other. So when I think about improving the automation, again, it's, you know, taking out some of the repeatability, you know, some of those tasks that are really, you know, repeatable, you can do that a lot. And also point, you know, let's say machine learning, AI to that ability to recommend based on the historical behavior, but it's the behavior of also the users. You know, we have so much like historical knowledge built up in our heads and how we've done these things in the past like data quality initiative, how many times have we had to quickly fix an issue and then you move on to the next one? You know, so how do we start to capture that to make the automation smarter? So I look for those types of use cases where you can start to see, you know, where we are storing certain types of user behavior and information or build those repeatable tasks. And so, you know, that's it. That's one from a data quality standpoint. And, you know, when I look at a data governance, which is much more cultural process, you know, there's other dimensions to implementing the data governance program. That's where, you know, automation is more about, you know, how do you surface the right information to the right people at the right time? And, you know, how do you build automation to improve some of those activities? So it's a little bit of a long winded answer, but, you know, it really does depend on the use case. I think, and, you know, there's different approaches that you can take. You know, well, Emily, I really like your story and I don't want to, like, oversimplify it, but, you know, again, we're finding these themes in this podcast of, you know, following passion and, you know, being curious is just, you know, and being willing to learn new things and explore new things just as you're talking about, you know, you wanted to go and be a teacher and then you found this path that led you to data and you just dove into it. It's just, it's really a great- I wouldn't have it any other way. I love it. And, you know, it is one of those things where, you know, the job is never done, you know, when you think about data and all the opportunities we have, you know, there's always room for improvement. And so it's a fun place to be. Indeed, indeed. And like you said, those soft skills, the communication skills are so important. It's not just about crunching the numbers, it's not just about being able to communicate the data that you're looking at. Yeah, yeah. Well, I would be remiss if I didn't ask, you know, if somebody's interested in Precisely, how do they find Precisely and what you guys do? Yeah, feel free to go to our website at www.Precisely.com. And we are so excited to work with our customers. We're just about to, you know, kick off a number of efforts here. We just had a big release. So there's a lot of cool things going on with our organization right now. So we'd love to chat with you. Congrats and exciting. Thank you. Very nice. And we'll be sure to put it on the podcast page, the links and for that so everyone can reach out. Thank you. Emily, thank you so much for taking the time to chat with us today. Thank you. I really appreciate it. I love talking about data. So thank you for having me such a great opportunity and love the work that Dataversity does. I'm always following you. It's a great place to learn about data. So love it. Thanks so much. Really appreciate that. So and thank you again so much. And to all of our listeners out there, if you want to keep up to date on the latest podcasts and 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.