 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 Greg Daggett from JR Simplot and Monica Daggett from Cornerstone Whole Health Care Organization. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DBTOX for 20% off your purchase. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is My Career in Data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who 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 for the first time by a father-daughter duo, Greg Daggett, the data governance lead at JR Simplot and Monica Daggett, the senior data analyst at Cornerstone Whole Health Care Organization. And normally this is where a podcast host would read a short bio of the guests but in this podcast your bios are what we're here to talk about. Greg and Monica, hello and welcome. Good morning. Hi, Shannon. Thanks so much for having us. Oh, thank you so much for being here. I'm so excited. Okay, our first father-daughter duo. So I feel like I need to give a little background on how this came about. So I had the pleasure of co-hosting a special interest group at Dataversity's most recent enterprise data world with Kelly O'Neill. And we have seen the number of women data practitioners grow tremendously over the years and we were working to figure out how we can better support them in their career development. And after the discussion, one of Kelly's co-workers came up to me to tell me about this man who was attending and was taking furious detailed notes throughout the discussion. And when she asked about it, explained that he was there because his daughter was getting into data and he wanted a better understanding of what she was going to be facing as a woman in data management and how he could better support her like, oh my gosh, so hearts melted, right? Greg, you became the buzzword across the conference. I believe it. And it was it was amazing. So and then I had the pleasure of meeting you, Greg, and asked if you wouldn't mind talking to your daughter, Monica, to see if she joined in. Here you are. Yes, this is pretty exciting. That was that was one of the best talks, by the way, at the Dataverse of the Event Enterprise Data World. And I thought you did a fabulous job co-hosting it. And I tell you why, if I can interject, one of the reasons why I was taking such furious notes was because of how you started the whole thing. The very first question you asked was, what do we need to do to make this a safe space for sharing? I thought, what a brilliant way to ensure that people can be authentic and and and be free with their feedback. I thought that was very powerful. And that's where I started writing notes and I didn't stop. Oh, well, I appreciate that so much. It's been one of my lessons, career lessons, right? Don't assume that everyone knows it's a safe space, right? You have to ask, and what does that mean for everybody? And so I really appreciate that. So but let's talk about your bios. So I'm really excited because you're both in data. And it's been a journey to get there. I've got a little plug in the beginning for my very first intro to data was Girl Scouts with my dad. So dad was Cookie Dad for our Girl Scout troop and for me in particular. And so my first introduction to creating a mini database in Excel was in the third grade or fourth grade and keeping track of orders and year to year. We were coming back and referring to our previous year's models, keeping track of customer information and orders and preferences and following up and one of one of my dad's co-workers at the time who was an excellent customer one year, I called him up and I was like, hey, this is this is what we had going for for last year. What are you thinking? And he says, OK, let's go with three boxes. And I was like, sir, that's a dramatic decrease from your order last year. Last year it was seven. Has the quality reduced? Were you not satisfied with your previous order? What can we do to bump up to the numbers that we had last year? And I think I stunned the poor man into silence. But that was our that was my first introduction with with data. Dad have anything to add there with with Cookie Dad management. That was well, the data certainly helped in that conversation because he certainly didn't remember what his order was from the previous year. And your ability to refer to that. I allowed you to have that conversation about something pretty meaningful, which was do we have a service issue with our Girl Scout cookies? Do we have a quality issue? And any of that. So you want to you want to get in another seven boxes, which is. Tools in the sales person's toolkit. I love that story. Well, let's talk about where you are now and your current jobs. And so, Greg, you're the data governance lead at J.R. Simplot. So tell me what type of business is J.R. Simplot? So J.R. Simplot is a private privately held company in both the agricultural and and food manufacturing space. So. We affectionately refer to it as a mine to plate operation. So they have mining for phosphorus, which helps to grow the potato products, which is then we turn into frozen French fries that we sell to companies like McDonald's. So we have a lot of other businesses, but from a very simplistic, it's it's agriculture and it's food and it's global. It's a very large company, a great company to work for. Very cool. So you are working with a lot of data. There is a lot of data and they've been around for almost 100 years. So you think of the history and where data has been curated, collected, created over the time. It's in many different forms, but we're embarking currently on an SAP, a transition to SAP. And so there's a lot of structure that we're trying to get our arms around and launching this ERP system. And as the data governance lead, one of the primary roles that I'm faced with is how do we make sense of the creation and maintenance of that data that's going to be used? What data is going to need to be transformed as we put it into the new system? How is it going to be maintained? Who's going to own it? So those are at a very high level. The roles of the data governance lead at J.R. Simplot is what we do. Fascinating. I love learning about different industries as we do these podcast interviews and just working with the community, right? Because everybody deals with data, right? Every company. And it's so interesting, so fascinating. The different lines of business. And Monica, so tell me. So you are the senior data analyst at Cornerstone whole health care organization. So tell me about this company. Yes, Cornerstone is a nonprofit dedicated to improving health care, equity and access, primarily in rural communities in the Pacific Northwest. And we bridge for special projects a lot of different different funding from everything from insurance payers and private contracts to federal grant funding in order to achieve very specific deliverables in order to close gaps in these rural health care settings. And so what I do in the data side of it is pretty varied. I support some of our health IT projects such as improving diagnostic and procedural billing for appropriate payout and tracking in a patient's electronic health record. I then also flip side and managing some of the data that goes to back to our federal funders and reporting on efficacy of grant grant success. And so there's design that happens in in each of those spaces. And a lot of what I do is translate very nitty gritty data into usable insight and narrative for future planning, for trainings, for alignment between stakeholders, because in our health care system, there are a lot of disparate stakeholders with a lot of varied needs and kind of a weird. Well, resource allocation pathway. It's it's not we're very unique in the United States and in how we allocate resources. And one of those resources is data. One of the issues that we see in health care in particular is that a lot of the data design is not happening with actual health care workflows and processes in mind. There's a there's a language gap between the people who are building solutions and the people who are needing solutions. And so having someone who knows enough of each in order to bridge the gap and make sure that the solutions, workflow improvements, a lot of quality improvement processes come together in a way that's cohesive and functional and then sustainable long term. So that is a lot of what I do. And I work with an amazing team. We've got a couple of folks doing similar work to to myself. And it is it has been really cool to learn from everyone. Because this is my first this is my first role. I shouldn't I shouldn't say that Cornerstone is my is the first company where I have been doing data exclusive work with. And so I've had a couple of different roles within the organization. And so it's been very neat to grow with Cornerstone and to learn from from my colleagues. Yeah. It's amazing. And so so interesting that you're both working on a lot of data governance, even, I mean, in terms of what you're doing as an analyst, even, but in such different ways, the different companies with different, you know, business needs. Very, very, very different. It's kind of it's kind of incredible even when dad and I talk shop over family dinner, whatever. And cross reference on what the other person is doing. The contexts are are so different that the solutions can't be similar. So it's really interesting to learn from him to within those processes and that he's working on and see what what is applicable in this industry. What isn't what are the core? The core structures that have to be in place in order for the systems to be functional, safeguarded, accurate, you know, all that jazz. Well, it's also interesting to understand the dynamics of data ownership and the stakeholders that are involved and what are the requirements and how can we effectively meet those requirements through data solutions? And that's going to be truth in in any industry. But yeah, it is kind of fun to actually have these conversations with Monica, because she has a level of data literacy that I don't know that I had when I was early career, you know, in this space. So it's it's neat to see the provasiveness of these our ability to talk about data and meaningful business outcomes. Yeah, yeah, it's very, very true. You know, and part of why, you know, I we started this podcast is you know, to show, you know, especially for maybe our generation, you know, how there was no linear line and no direct path into data like we didn't, you know, but so let's talk about more of that. And how exciting, you know, I think about it makes me think about a conversation I constantly have with one of the consultants we partner with. And she's always talking about how her family just has no idea what she does, like she's trying to explain it so many times. And how nice that you two can actually have some language and have some understanding about what it is you all do. And when I started out, my my I have an MBA with an emphasis in decision support systems. And at the time that was kind of an emerging field, they talked about what what would be really great after decision support system, which is modeling decision making and bringing in data to support that. The next layer up was expert systems where you could actually program in decision making. So it wasn't to support somebody making the decision, but it was making the decision on your behalf, which is a little scary. But it kind of sounds similar to how maybe AI is being deployed and put in in today's world. But at the time I didn't once I graduated, I actually didn't utilize those skills and those learnings in my first many years of my career. It was good background. It was on a shelf somewhere. One of the first times that I used that I was asked to do a supply chain project. And I was like, I'm not exactly sure what supply chain is like, not a problem. We'll explain it to you at the time I was working with a computer distributor. I was working for a hardware manufacturer. We were wanting to redo our innovate our demand planning and order process. We had a channel inventory issue. There was demand signals that were erratic. We had exposure to channel inventory because of falling prices. When we would do price protection and that was a financial risk. And so we took a look at what data do we have that made available to us that we can improve on the situation. And so we worked with some doctoral folks from Stanford, came up with a whole inventory replenishment model, put that in place. And I was like, yeah, this is this is kind of decision support systems. What are whether how do you generate your forecast? How do you know what your supply is? How do you do your supply to man matching and bringing that all together? And we had great benefits. We drove our channel inventory, which was the rule of thumb was we need four weeks of supply. If you have four weeks of supply, you can have great service level. Well, if you're actually using mathematical models and you have good data and which we did, we were able to bring channel inventory down to about two and a half weeks of supply while increasing our fill rate. So it was a great application of that. And it was like, wait, decision support systems, they are out there somewhere. You're just not called that. Well, I love that you're getting into the data. But I want to back it up a little bit even further grade. So like, tell me, like when you were six, say six years old, like, was that the dream? Like, I'm going to go into data governance and what was the dream? In fact, it wasn't. If I was going to say what it was, I wanted to be a professional baseball player. Nice. But what was really exciting when I was a kid was I would pour over baseball digest. It was mailed out. I don't know if it was a weekly or a monthly. And I'm not sure that there's a sport that has more statistics and more ways of diving into data at batting averages and earn runs and steals and hits. And I mean, all sorts of ways of measuring performance. And I would keep track of a lot of that. It was fascinating to me. And so I think these are the things that were sprinkled in my head even though I wanted to be a baseball player that didn't materialize. But this affinity for understanding maybe the intricacies of how that sport works and what makes it successful and the data that supports that. Yeah, it's very true. You know, and although there's a lot of data in all sports, I think baseball has always had that at the forefront. It's always been something that they've published and really brought attention to. And yeah, so I agree. So, OK, so, Monica, we've heard a little bit about Girl Scouts. But tell me, was this what was the dream when you were six? So was it was it to become an analyst? I floated. I think that I floated through a lot of different ideas. Science was really interesting to me always, but I was a dancer and in high school was in pre-professional spaces and training with professional companies during the summers. And and right before my health decline, I was really, really involved in making decisions about where to apply to college. And so I applied to schools with the idea of pursuing a professional career in dance and then transitioning into creative movement therapies, physical and occupational therapy in the creative space for adults and children with either degenerative or developmental nervous system issues. And that was that was the plan. I was very fixed. I was very fixed on it. But health health declined. And I switched gears, moved into more of the environmental space. I was very passionate about that. But then another health decline and what led to my my current known diagnoses, I then needed to figure out how to work remotely. So it's been it's been a varied career. And I think it's kind of cool that I've ended up back in the health care space peripherally in a support capacity, but that the it's been a pretty varied career for only being 10 years old, a decade old or so. So it's it's kind of gone all over the place. But yeah, I wanted to start with dance. Wow. Yeah. Ended up in some place very different. Yeah, as many of us, many of us do. Yeah, so. Fascinating and and and I take it and I. So you've had a lot of challenges that you've had to come through and overcome. And you've had a lot of so how hard was that to change to change gears? Oh, man. So I switched gears the first round after my after dance be got cut from from the table. And environmental science was amazing. I was super passionate about it. I had been working in regulation and compliance in Idaho and then moved to Arizona to do some natural resource management in the water resources space to talk about data and trying to bridge with stakeholders who weren't ready to hear some hard information quite yet. But I was pursuing grad school down there and everything really collapsed. And when I so I have a connective genetic connective tissue disorder that then is accompanied by some super fun nervous system mismatches that create together a kind of whirlwind of unpleasant systems and make it so that I can't stand for long periods of time. If I can I can walk, I can move pretty well most most of the time. But standing in a lab or going into the field for field work was really out became out of the question. And so thinking about what are my transferable skills? I can work with Excel pretty well. I have done some database management with my water resource work. We can we're going to take a stab in the in the data realm and I can work remotely and be present and able to produce valuable work in a setting that allows me to be accommodated well, which has been a challenge, yeah, to find in other in other types of careers. So, dad, you were along for the ride with some of that. If you think to some of our conversations about career transition, what comes up for you? Well, you know, it was interesting. And I think you hit the nail on the head on a few things, Monica, and that is what are the transferable skills and your ability to understand the details at the data level, as well as understanding at a stakeholder level, what is it that people need or want or are expecting? And so you've got this nice balance of not only just being a data nerd, like some of us, but being able to have the conversations with people that maybe don't need to know the level of granularity. They need to understand that what I would say, the information or the insights from that data and your ability to navigate from the detailed data to the to the insights, the information and insights has allowed you to manage through these transitions that were driven in large part by some health challenges. Yeah. Well, I was lucky to have a cheerleader in my corner the whole way through even, yeah, today. So that's pretty, that's pretty awesome. I'm a lucky daughter. That's awesome. And I will say, I don't know you well, Monica, but but I would say Grace would certainly be one of those transferable skills that you're carrying along. So, yeah, thank you. I appreciate that. And, Greg, so you talked a little bit about baseball. So you've been into data from the beginning, really. And so but and then you talked about your your degrees. So your degree that you so tell me a little bit as you transferred into college, you know, how did you choose that? Where did you start developing your passion and your your path? You know, it was pretty organic. There wasn't an aha moment of what it is that I want. I know that if I look at some of the strengths that I have in some of my interests, I'm analytical. A little plug for Strengths Finder. It's one of my top five skills. And I can appreciate that because to me, those those strengths mean I do it a little bit naturally. It doesn't take a lot of extra effort or a lot of extra energy. And for me, I do a lot of analysis when it comes to data because it's very concrete. And so moving in from as an undergrad, as an econ major, economics major, there was certainly a lot of math and numbers and trying to understand that that led me into the decision support systems as a graduate capacity. But from there, it was quite frankly, what jobs do I feel like I have some skills to to meet that I could support a family? And this supply chain role that I was in was years later from my my graduate work. So it was like, oh, OK, I can do that. And if I fast forward through a lot of my career journey, I want to have been working for a computer hardware maker and their support organization. And one of the things that they wanted to do was to improve the customer service for for the call support. So if you think about when you make a phone call, you say, OK, I need I've got something busted. I need it fixed back back when you actually could talk to somebody. Well, I was asked to improve that. So free reign. Now I was trained in a six sigma methodology. So in order to do six sigma work, you need data. And so one of the first things that we did was to do a time in motion study, which is how long is it taking our agents to walk through the end to end process of a phone call of a call support? This is the first foray that I had, which was how important it was to get buy in and trust from the people that are giving you data. Because when we ask people to log the amount of time that they're taking doing a task, they're thinking, I'm going to get in trouble. They're thinking, if it takes me longer than I was supposed to, I'm going to be penalized as fear mentality. To my answer was there's a lot of things that are outside of your control as far as what that time is. We're just trying to set realistic expectations. And for me, the thing that I really wanted to get to was how variable are the times spent in each one of those process steps? So it's not if it takes an average of five minutes. OK, we're just going to say that's a five minute task. But if sometimes it takes 10 or 15 minutes, we know something's gone really bad. And the only way to know that is by capturing the data and measuring that and then analyzing it. So as a result of that, we put together a course correction project, which included training in certain spaces, new tools that were required to help with diagnosis and the validation of resolution of issues. There was an effort to get our service people to sell something. And so that was increasing the amount of time of a call. We say, well, let's evaluate that. Some of our reps really care and want to sell because they're motivated by that. And some of them are like, I just want to solve people's problems. I really don't want to sell. Guess what? We had a wide variety of a huge variation in that selling port of the process that we re-evaluated. Do we really want to have that? Because it's it's not going to be standard. And if it's not standard, it's not going to be best in class. And if it's not best in class, you have the potential of having it be a negative experience. So that was another example of me being in a role where I wasn't managing data, but I was certainly utilizing data and procuring data and and using ensuring that we had trust in that whole system so that our outcomes could be leveraged. So that was that was another big step in the in the progress. And the data journey, yeah. So how did you get from there to into your current role as a data governance lead? That's a great question, Shannon. Stimplot is based in Boise and it was a company that I was really wanting to work for. And I had applied for a number of different positions and for whatever reason, wasn't making much headway. I talked to the recruiter and I said, is it something about my background or is it something about my experience? He says, no, no, no, this one that they hired internally and this one you applied a little late in the process where they were. And, you know, it was like, no, no, no, you're fine. And then one day he calls me up and he says, you know, I've been thinking about your profile. Have you ever thought about data governance because we have a position available? What's data governance? I don't even know what that is. He says, well, let me send you some information and and I'll have you talk with the hiring manager just to kind of get an idea. And their hiring process, they had me do an exercise. They gave me a data set. They wanted me to evaluate it and present on it as if there was a need to make some changes or, you know, what was my assessment of the data and what would I do and propose? And I looked at it and I was like, I don't know much about data governance, but I can tell that 35 percent of these records are missing critical information. And, you know, why wasn't that put in when it was created in the first time? And, you know, now all of a sudden I know what the data governance terms for that means now. It's like, well, what are your origination processes and how do you make sure that you have data validation at the time? But I just inherently understood the value of consistent data and the issues when you don't have it. So they hired me and they said, we will teach you the data governance, but you don't understand the business impacts. You can you can have the conversations with the business stakeholders internally. So it began my journey of learning data governance and which is why I was at my very first conference just a couple of months ago in California where we met, Shannon. So that was a fascinating conference that allowed me to realize, A, there's a whole lot of data governance that I don't know because it's a very broad field and B, the things that we are doing, it's simple, we're doing very well and and it was very validating to see the things that we've we decided we're going to focus on. We are we've got some best practices. That's that's so great. And I love that story. And and it's it's interesting. You both kind of fell into your roles for very different reasons. And as it goes with data, right, we discover like these different aspects that can really speak to us and and how we can work in these roles that we become passionate about. That's very cool. 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 tell me, you know, Greg, I'm going to start with. You so what has been your biggest lesson in your career? One of the lessons that I come back to is keeping the end in mind. One of the very first professional development activities that I did was I went to a Stephen Covey seven habits of highly successful people. And one of the things that one of those seven things is keeping the end in mind. What are we trying to build towards? What are we? What is our objective? And it it has an amazing power of aligning work teams, work projects, organizations, if you know what you're really focused on. And so many times and I get myself caught in this. It's like, let's just get to work. Let's just get going. Let's just build the data. Let's just, you know, whatever it is that we're cast to do, let's just get started. But unless we really know what we're looking for in the end result, it might be the wrong direction. It might be the wrong build. It might be the wrong everything. So for me, one of the biggest lessons is always having to focus on what does this thing, what is whatever we're working on? What does it look like at the end? We might not get there tomorrow. It might be different phases, but always having an eye on the vision in the future. Very good. And Monica, so and you, what's your been your biggest lesson so far? Flexibility to start. Things, things don't things. You have a plan and then you have plans A, B, C and D. And sometimes that doesn't, none of them align out. But I think I think within that, one of one of the things that's been really difficult in transitioning as someone who's pretty dynamically disabled. So on my good days, I'm doing great. And when it's not good, it's really not good. Is that ADA accommodations with employers are very difficult to navigate. And it's something that you don't really think about until you need them. But they generally are pretty poorly enforced and pretty vague. And there aren't a whole lot of. I will say one of the things that I'm so grateful for with my work with Cornerstone is how flexible they have been able to be with me and making sure that my skills are prioritized over my time. But in the seat kind of a thing. So I'm able to work flexibly and well within, within reason. But that is a challenge to find organizations that are really willing to work with. With people with disabilities, generally, and to make accommodations work for everybody. And so that's something that as I move forward, that's good to know. It's something that informs how I network, how I interview, how I track my time and my progress and my performance reviews. And it isn't something that I don't think a lot of folks who don't have to navigate it have to think about. But it's something that's pretty, pretty top of mind for me. And disability is interesting because if you live long enough, everyone will experience that state of being at some point or another, either through age or temporary injury or chronic illness or traumatic accident. It will, it will happen to everyone if you have the opportunity to live long enough. And so I hope that more people can become aware of what is actually protected, how people can protect themselves, how people can collaborate with their employers, but most importantly, how employers can collaborate with people who have a lot to give and a lot to offer in within their organizations. Yeah. Indeed, are you how are you educating yourself on those issues? Oh, man. So took a deep dive into disability advocacy history and some of the some of the efforts, how difficult it was even to get the ADA and instated and what barriers exist there. Looking up, I know that this isn't super like fun, special interest deep dive reading, but looking into the specifics of social security, disability and leave policies, FMLA and understanding that those programs are very limited. For, for example, if a person applies and is accepted for social security disability, which is very difficult to achieve, you have a cap of assets. You can't have more than $2,000 worth of assets to your name. So savings isn't possible. You lose it if you should you get married and transitioning then from those services into back to work programs is pretty jarring. There's there's no way that you can prepare for a gradual or a or gentle reentry back into the workforce if you're able to to resume in those spaces. So that that was shocking for me to to learn. And I think I think if policy, I don't understand reading policy isn't most people's favorite thing to do before bed, but just just take a gander. Just yeah. And if and if you see any books by disabled people, media, social media or films, documentaries created by disabled people and disabled advocates to interact with them sincerely is, I think, another really neat way to just not not just expose oneself to a different way of being but to learn from people who have really valuable lived experience that will probably apply to you at some point in your life. If you're lucky to live that long. Yeah. Indeed. Oh, I love it. Um. Hmm. I'm going to kind of switch it up here, you know, as we're going as we're getting to these conversations, you know, so it sounds like both like communication has become a big thing, right? So whether it's data governance or, you know, and explaining the data, right? And you kind of mentioned that a little bit, Greg. And Monica, so are you want to ask about communication? So what are your how have you developed the skills like how have you developed the skills, Monica, then to educate your employers and find that niche and and advocate for yourself? Well, honestly, I I wasn't able to communicate verbally for. About nine months coherently and one, I think the advocating for oneself is important, just as important is to have people in your corner. And my dad was a person in my corner. We joke. Sometimes he's got to put his sharp elbows on if he's got to go, you know, marching through through the crowd, kind of a thing. And and one of one of the things that I've learned from dad is listening to understand and to capture. Like the minute details really matter at the small pieces of data are things that can can tip the insight into any direction. And so paying attention and asking so. So many questions. And sometimes this is something that then we've learned, I think, together is how to ask questions in a way that communicate to the person who you're speaking with. How you need to move or where that what the end goal is in mind as as dad was saying, keeping the end in mind. You can ask a question with the end in mind that's very clear. And that also holds people accountable to to that end and to the whatever answer that they give. And I think that that is not just true for communicating around disability and employment, but also in terms of getting to accuracy within within data, too. So I talk about transferable skills that that goes all the way across the board. Dad was communication. What are what would you have to add on on that front? Mr. Sharp elbows. Well, I would. I would go back to the Stephen Covey, you know, seek first to understand and then to be understood. And so that's why I generally will start with asking questions. I'm really trying to understand what whatever it is from the other person, other person's perspective, whether it's a data challenge, whether there's an issue that needs to be resolved so that once you have a clear understanding, then you can take action. So for me, communication is starting with listening and understanding. Which is what you clearly have demonstrated and why we're here. Yeah. When dad told me that he had walked into the he called me the next day and was like, I went to this really cool session and we're going to dinner tonight. I didn't I didn't know that I would get invited to dinner afterward. But like this is so cool. And I'm so excited to learn all of this. I'm finding all of this out after the fact. And I'm like, dad, you are you are my hero right now. Just walking in with your notepad. I love it. Awesome. It's so awesome. OK, so I could talk about this forever. But so I do want to make sure we we get to all the questions here. So. OK, so back to kind of the data center piece of this as you both work with data. What is your definition of data and how do you work with it? I'm not sure who to ask first, because I don't know, Monica, if your definition is if you guys have had this conversation as a family, like what is data, you know, like if you're in sync on this or is there a differing opinions on this on this question? I think I think the way that we describe it is a little bit different. So I I think of it as like the pixelation of a varied resolution image or the 4K TV. Like how detailed can you get to get the clearest big picture? But that is that's how my brain works. I think dad has better technical terminology, so I'll hand it over over to you, dad. Why I find much of I'm going to have a technical answer for that. But for me, it's if you think of a house, the data is like the bricks. It's it's your building blocks for for whatever it is that you're trying to build. And in any business, you've got a ton of data. I know that our company I mentioned is going to an ERP transformation. They invested heavily in the data team. And it was because the businesses run on data. And if you have the data wrong or incorrect, it's a surefire way of having failure for your business. And one of the things that we that we learned about at that conference was the failure of Target in Canada was all around how they were creating material master data in SAP. And they I would just say that I think they had some failures with that. And it prevented them. Their their shipping of inventory from warehouses to stores to customers. It was such a disaster that after a few, I think it was a few months, Target Canada just threw the white flag and said, we're we're shutting this down. So data is incredibly important. It is the foundation for business processes. It's the foundation for decision making. It's the foundation for insights. It's it's the foundation for innovation for your research findings. So I like the pixelation because it's, you know, it's the it's the core. And, you know, if that spot needs to be green, but it's blue, we have a problem. Right. So it needs to be accurate. It needs to be defined. It needs to be managed and I'm putting my data governance hat on right now. But we need to know where it comes from and where it's going and who has access to it. But it's it's at the core. Very nice. And and what advice would you give to people looking in to get into a career in data in any aspect? And Greg, maybe I'll start with with you. Well, I would say to begin with, you need to have data literacy because it could be that you will work in an IT organization where it's very specific, that you're working with data. And it could be that you're in the business. But if you're in the business, you need to be data literate because if you're putting data in the wrong places, I mean, just basic things like if you look at a spreadsheet and somebody's putting in some content in there, is it usable content? Can you slice and dice it? Can you can you really look at it in different ways? And if you have some basic data literacy with with certain skills, you can apply them technically in an IT organization or you can apply them throughout your whichever business area that you work in. Finance, accounting, marketing, you know, talk about sales data, salespeople. You know, operations, it's pervasive, so not not to be afraid, not to be afraid. How does one become data literate? I think through exposure and I think some things require some mentoring, you know, to break down barriers because it can be intimidating. You know, I know people that say, oh, my fifth grade math teacher scared me so much. I'm afraid of it. And it's I don't think it's all about arithmetic. I don't think it's about algebra, but it's just if it can be broken down in the in the more simpler concepts for people that they're not afraid of it, that they can engage with it, that it's part of their fabric. And I think if you can have a conversation with anybody, whether it's their own personal budget, whether it's, you know, the money that they're spending and at a store, I mean, there's ways of creating data literacy that that can bridge that gap. So your point, I think would be a way to to help with people that do find themselves afraid or uncertain. Yeah, to your point, dad, the education piece, I think is is pretty key. I had. So health issues number one and two were both pretty severe concussions in high school, and it changed my math track pretty, pretty significantly like mid year, and I had a very frank conversation with my pre-calculus teacher, and she said, yeah, I would recommend that you move to my AP stats class instead of AP calculus for your senior year. Let's just make this happen. And then we still had to do a lot of one on one work. And, you know, she was she was making accommodations for me in ways that we didn't even have language for yet around the things that I now know came down the pipeline later. But having someone to pay attention to strengths and then be able to effectively reconceptualize things in order to open up doors. One of the if we think about women in data work specifically, one of the places where women start getting pushed out of data is in middle school and in high school math classes. And so having teachers who can work with all different kinds of students and really meet meet them where they are is important because math, I was never super strong in math, but I understood relationships. And so statistics was actually a perfect bridge. Geometry was a perfect bridge because I understood how everything was relating to one another. And a lot of data is about relationships. And so even though calculus did not work out for me when I tried to take it again in college, we were able to to read to readjust in in those spaces. And the other thing that I think teachers can do and mentors generally when we talk about access is paying attention to social dynamics. I've been significantly harassed in in classrooms where I've been one of of few women in the room. And I've had mentors and teachers who shut it down or were able to work with me in an extra capacity. And I had others that said you're on your own kid kind of a thing. And those drastically changed. I was able to interact with the material. And so I think I think that mentorship piece is vitally important. And Mrs. Gazzdick, thank you. I think about you pretty much every day at work. And I appreciate you teachers and mentors are our key. Yeah. Yeah. And finding those good mentors. Absolutely. We're actually looking to start mentorship programs. So if you'll have any ideas on that. Super exciting. Like an industry level mentorship program. Oh, that's that would be fantastic. Yeah, I definitely have some ideas about some ways of matching, you know, creating profiles and in the selection process. So getting into the weeds a little bit. But yeah, there's ways of doing it. But in the end, it's a relationship. So it's it's it's a it's a requirement for both the mentor and the mentee to commit and to deliver. So recruiting of those would be very important. Exciting. Love to hear that. We'll see it's been it's been in the works for a while. But as we continue to launch this initiative to support women in in data management and governance, then, you know, we can't launch that without a mentorship program. So it's just forcing us into finally doing it for everybody. Right. We need to we need to get that going. So to be to to be announced in terms of when we'll get that launch. But it's coming. Our eyes and ears open. Yeah. Oh, Greg and it's been such a pleasure. I want to thank you so much for you both taking the time to chat with us today and chat together. Thank you for having us. Great experience. Thank you. Yeah. And Monica, especially, I want to thank you for getting vulnerable and sharing some personal stories and challenges. So thank you very much. I think that's very full to a lot of people. I am so excited to have the opportunity. Thank you for giving us the the platform to to share some of these experiences, because I think they're. Sometimes you don't know what you don't know. And sometimes it lands in your lap and changes your life. And I hope that anybody else with similar experiences to mine. Just remember to take it one day at a time. And there are spaces in the world for you and people who care about your success and your careers. And Shannon and Dataversity are among those numbers. So thank you for having us. Thank you. And Greg, thank you for being a supporter and advocate and being a superhero here. You know, we we appreciate you. Well, I I love it when people can bring their best selves forward in whatever capacity, whether it's work, life, you know, and the best way to do that is to support people from who they are. And you can you win you win dad of the year for like now until forever. Just saying, just saying. I'm so proud to be your daughter. Thank you. Oh, this has been such a great conversation. And, you know, I wish we had more time, but that it's all the time that we have to chat today. And thanks to our listeners out there. If you'd like to keep up to date on the latest podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe. Stay curious, everyone, 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.