Kris Saling @ US Army Human Resources Command | Data Science Hangout

Thank you all so much for joining us today. Happy Thursday. Nice to see everybody at the Data Science Hangout. If this is your first time here today, so nice to meet you, but thank you all for spending the Thursday with us. If we haven't met yet, I'm Rachel Dempsey. I host the Hangout and lead customer marketing at Posit.

This is our open space to chat about data science leadership, questions you're facing and getting to hear what's going on in the world of data across different industries. So we're here every Thursday at the same time, same place, except the week after next, we'll be at the Posit Conference in Chicago. So we won't have a hangout that week. But I'm excited to get to meet so many of you there in person.

But anyways, if you're watching this recording at some point in the future on YouTube, and you are thinking, I want to join them live, you can use the link below and the details to add it to your calendar too. But at the Hangout, we're all dedicated to making this a welcoming environment for everybody. We love to hear from everyone, no matter your years of experience, titles, industry or languages that you work in too.

And every week, I'm joined by a different leader from the community who joins to share their experience and answer questions from you all. So it is totally okay if you want to just listen in here. Maybe you're on your lunch break, or you're kind of having to multitask. But there's always three ways you can jump in and ask questions too, or provide your own perspective. So you can raise your hand on Zoom, and I'll keep an eye out. You can put questions in the Zoom chat, and feel free to just put a little star next to it if it's something you want me to read instead. And then we also have a Slido link where you can ask questions anonymously.

But with all that, thanks again so much for joining us. I am so excited to be joined by my co-host today, Colonel Chris Saling, Director of Innovation at U.S. Army Human Resources Command. And Chris, I'd love to kick things off with maybe having you introduce yourself and sharing a little bit about your role today, and something you like to do outside of work too.

Introducing the innovation cell

No, that sounds good, and thank you so much for that, Rachel. So as you said, I'm the Director of Innovation for U.S. Army Human Resources Command, and everybody always tries to figure out what that is. Innovation has become a buzzword. We always talk about innovation theater and everything else. Our job is to be a little bit of a future concept cell for the commanding general so that we can be a little bit more removed from the daily asks and asks and figure out how we're going to take and update and sometimes transform our processes to get after those future concepts.

So I have a team that is predominantly technologists. I have a lot of data scientists. I have a cyber guy. I have a networks guy. I have a human resources digital transformation subject matter expert and a recruiting team. And that's just the start of us. We're a year old, and I think we're going to expand by another 10 positions or so this coming year.

And outside of work, what's something you like to do? I like to cook. So to me, that's my zen. I'll put on some music, and I'll go in the kitchen and cook something.

So I know you said this is a new team here. Can you share a little bit about how the creation of that team came to be and maybe how you work with some other departments across the Army too?

No, it really kind of started when I was working in, just for some context, I've been working out of the Army people space for seven going on eight years now, which is kind of atypical. But a lot of what we've been doing is digital modernization and transformation. The Army came out with the Army People Strategy, I want to say, in 2019 or early 2020, that talked about how we wanted to modernize how we did talent management, pushed a lot, did a lot of different pilots and prototypes, got a bunch of policies changed. We did some great stuff there and then realized that we hadn't modernized the mechanisms to actually get us to talent management.

We were still basically assigning people and moving people around using the same old systems, and a lot of them really needed to be updated. So we needed a way to look out and see how we could do that cohesively rather than just looking at the individual processes of the directorates. So General Drew made the decision that he was going to establish the innovation cell to solidify our HRC 2030 vision that we have, and to look at how we're going to step forward in our technology and our business practices to achieve it.

Yeah, it's military and civilian. Right now, we're mostly military just because I took my team, I realigned my team that I have for talent management with me, but we've made a couple civilian hires and we're going to make a number of other civilian hires.

Breaking silos and building coalitions

So a question I had wanted to ask you about is, Caitlin and I were talking about how you've managed to kind of break silos between the different army departments and getting people to work together to help achieve the team's vision. And I was just wondering, like, how have you been able to do that? And what tips do you have for some of us who are maybe trying to do the same thing and break silos within our own organizations?

Yeah, I mean, one of the biggest skills that we, we harp a lot on command in the military, but that's not the most important leadership skill. One of the most important things that we consider is building coalitions, you know, how do you reach out to your counterparts and get a whole bunch of people crashing together on a common goal or a common understanding? I think one of the places we've had the biggest success with that is with our data literacy program. As we had a lot of people with needs, we had a lot of people who are looking for more data, and we were able to pull people from throughout the army to look at this as a project and get it scaled up. So now it's part of our professional military education.

Yeah, and actually, I think you answered part of it, but, you know, you're, you're, as you're building your team, and this is a kind of question that we hear a lot at Data Science Hangout, did those team members already have data science skills, or are they having to learn as they go?

So I had a mix. One I hired straight out of grad school out of the Georgetown Data Science Program. Another I hired from Notre Dame, the Computer Science Program. So they had a number of different data skills, they had to, you know, kind of cross train into other areas of emphasis. Obviously, natural language processing and texturing analysis is big when you're in the people space. One of them came in with a lot of those skills, the others are having to learn it. And then I have a couple other folks who were very good statistical analysts, didn't have the same kind of experience in building out some of the applications we were building. So we're using a number of different kind of training programs, upscaling, pair coding, to try to get them set on that.

Then I do have some folks who come in straight from the HR side, very limited exposure to data and data science. And it gave me an opportunity to kind of test our data literacy training on them to get them to ask more, you know, intelligent data questions. Not that they weren't asking intelligent questions, but there's less translation needed between them and our data folks.

Data sources and NLP

Patrick, I see you just put a question in the chat. You mentioned text analysis and NLP. What are your key data sources for bringing that in to the analytic pipeline and then also other data sources, more quantitatively focused stuff?

So we use a number of different kind of commercial products. We'll use Hugging Face. We'll use a number of other things you guys are probably familiar with to help us at least kind of break down our taxonomies. But most of the work that we're doing comes from our evaluation entry system, where we are looking at evaluations. We're looking at duty descriptions. We're looking at how we establish our taxonomies and ontologies for job competencies. There are a lot of libraries out there that we could build, but there's not really a spaCy library out there for military competency. So we've had to build, practically build our own.

Breaking down data silos

I'm going to go back to the conversation about breaking down silos a bit. Luke had asked in the chat, in such a gigantic operation, how have you been able to actually break down those data silos to get what you need?

So the first step we had to do was convince senior leaders to be directive. I mean, it's horrible to say, you think we're going to want to cooperate and share, but nobody wants to cooperate and share their data. So we went to the Secretary of the Army and said, I have a directive here that says all the people in the people space will share their people data, period. So it was our first people analytics core directive. We've refreshed that a couple of times, and we've identified our person event data environment as kind of being the semi-official repository for that data sharing. We get a lot of people who are working in that environment. It's probably something a lot of you will be familiar with. It's just a massive data warehouse with a virtual machine environment on top of it that we use to drop different data sets in to do different projects.

So the first step we had to do was convince senior leaders to be directive. I mean, it's horrible to say, you think we're going to want to cooperate and share, but nobody wants to cooperate and share their data.

But yeah, no, we had to basically order everybody to share their data. I'm trying to think how that might translate to some of these students here too. Well, it's like, I mean, I think the biggest piece, we talked about building coalitions. You know, there's going to be, we tried to do some data summits across the Army just to bring people together and talk about what they were working on. And man, it would be really cool if we could just get our hands on, well, there's probably somebody in the room who knows how to get their hands on that data. So we started this little bit of a coalition talking about, wouldn't it be great if we could do all this? And that's how we pitched the argument to leadership to say, look, we've got to, we've got to get here. We've got to be able to do this. And we need your support and endorsement. Ours was very directive. But I think if you have a leader there who does give you that champion, that support and endorsement, that might be a good corollary.

Data modernization across the Army

So we've really got a, just, it warms my heart, data modernization trends. Army Futures Command is a big partner. Their data science division, Nate Parker over there is a big partner of ours in looking at how we can get more agile data science. I mean, I hate to say agile, not in terms of agile development, but something that'll flex better into our different requirements.

So we're working with him and seeing if we can use that instead of our current environment. We've reached out to 18th Airborne Corps who is using our Vantage program, which is built on Palantir Foundry to do a lot of their work, a lot of their data work, informing decisions. We've worked with the Army AI Integration Center that is now up at Carnegie Mellon. We've had a lot of great partners in the West Point. I can't leave out West Point. Partner and good friend, Colonel Nick Clark, who started our data literacy training program that we've kind of incorporated and taken Army-wide.

But yeah, and I think kind of coming back to finding those partners in the coalition, as we figured out who all is working in this space, we've been able to kind of give each other a hand up when people need resources or endorsement, and it's really made things spread faster.

The data literacy program

I know we touched on the data literacy program a few times, and I was just curious, like, what does that program look like?

So the initial iteration was just 10 hours of coursework, basically making people better critical consumers of data. Making people better critical consumers of data. That's expanded to about a 16-hour course that's going to be in kind of our basic professional military education. There's a second level that they're going to get in that next iteration, which is more hands-on, using the common tools that you have available. We'll start folks out just doing basic statistical analysis in Excel or whatever they have access to. We want to start introducing them into R and Python so that it's not scary. We can learn how to code, share some of those resources.

So if they're interested or if their branch has opportunities, they go into level three, which is where we take them into some in-depth analytic training. That walks them through our data competency model, and we basically create learning paths for all the different things that they want to do, if they want to get into different types of coding, if they want to do more NLP. I need more NLP programmers.

That actually leads me to a question that was asked anonymously was, how much is R or Python used in your organization?

So a majority of our folks are Python users just because of our Vantage program being built on Foundry, which is PyJR and PySpark. I've got a couple of folks who do use R fairly regularly doing their analysis. We had a lot of people on kind of the more legacy programs, looking at SPSS and SAS, not to disparage those, but they're expensive. So we want people getting on things where they can more rapidly develop and share. Like my team, we try to use as much of the existing language and share code as much as possible, but it gets difficult.

I mean, most of our personnel data is unclassified, but it might as well be SCI secure compartmentalized information just because you have the personnel, trying to remember what PII stands for. You have that component of it. You have medical data, you have HIPAA protections, you have legal protections, all these different kind of caveats that go around any kind of a soldier data object you create. So we want people being able to use the tools. Yes, personally identifiable information. Thank you guys. I had to go look it up too, because I say PII so frequently, I forget what the acronym is.

Protecting sensitive data

Thanks, Chris, for answering some of that already, but I guess even talking about breaking through these different groups, so different departments, I'm sure, have different things that they consider are protected or secure. So how did you kind of deal with that challenge of trying to be like, these are the kind of tools we've built to ensure security of data within different departments and to assure that this is not just all going to be shared across every team or every group? And was that challenging for you?

So we really spent a lot of time working on our data governance, our policies, and our protections, and we spent a lot of time building that out in the person event data environment. It's a secure environment. You can't download anything. You can't take anything out of the environment. People get fussy about that because it makes it hard to access, and you've got to request access through a project to certain things. But that, at least, when people are realizing that nobody's going to pull everything into a CSV file or a spreadsheet that's going to be sitting on their desktop, that provides a certain amount of comfort. We also just kind of restricted the access to any kind of identifiable information for a lot of projects unless it's specifically needed. So a lot of the work there can actually be done with de-identified information. And we're trying to add more information back into circulation now, get people more access now that we've kind of done the lockdown, but still, it's always a little bit of a thing we watch.

Soft skills and teaching

I'm just kind of interested in what's a soft skill that you've started to really appreciate from your team here? Because we always talk a lot about the technical aspect, right, and the skills that are attributed to being a good programmer and or data scientist. But I think there's so much that has to be kind of focused on about what aids in all of that stuff, right? So yeah, what's a soft skill that maybe you've kind of learned to appreciate more and more with your team?

So a lot of it kind of unearthed itself when we were doing our data literacy, and it's teaching. I mean, we have to have people who have the soft skills that support being able to teach. It's like, how can you connect with somebody? How can you kind of empathize with them enough to be able to see where they're coming from with their question? How do you put the process that you're trying to describe or the result you're trying to describe in an accessible context, kind of do those understanding checks? I mean, it sounds very soft in this context, but it's a critical piece of being able to translate our solutions to a decision maker who, if a decision maker can't really use our data or decisions or understand it, it's a tree that fell in the woods. So we've really kind of emphasized that teaching ability.

I mean, it sounds very soft in this context, but it's a critical piece of being able to translate our solutions to a decision maker who, if a decision maker can't really use our data or decisions or understand it, it's a tree that fell in the woods.

Typical analyses and forecasting

The question was, apart from the NLP example, what are some other typical analysis that you do?

Well, one of the programs that we're working on is just a big neural net that takes in all our personnel data and rolls out a prediction vector of how likely someone is to retreat from service over time. So we do a lot of retention analysis. We're trying to figure out how to improve on an existing simulation that shows how our distribution could get backed up. We want to try to forecast what our force is going to look like, our force inventory is going to look like over five years, because obviously, you probably heard in the news about the recruiting issues. Issues sounds too kind, but I'm on camera, so I'll just say recruiting issues. But that's going to leave a little bit of a trough in the force, even if we recover from it in the very near future, it's still going to be something that we have to build our inventory around over the next five, 10 years.

So we want to be able to take a look at what that looks like and prioritize skill development and assignments accordingly. So we're trying to build some of those forecasting models. Then we're trying to work with a number of different elements, we're working with the Army Software Factory on just getting data shared into applications that'll make it easier for folks to do things like finding childcare, PCS, et cetera, permanent change of station.

Showing value and quick wins

Yeah, that was one of the biggest things we had to do initially was to show value. I mean, we wanted to turn on, find projects that were kind of quick wins that we could say, hey, look how much easier this is to answer with data and trying to make that business case to leaders.

One of the things that we used, I worked for about two years on a structure to personnel mismatch program because we were trying to figure out with all the rapid changes for our new structure, why did we have a force that looked very different from the structure that we required? And the answer was, obviously, the senior leaders can say, I want to build this type of organization. The requirements are built, and it takes much longer for the supply to catch up. So as an argument for data-driven talent management, where we look at attribute-based assignment versus branch and a grade, like, look how much we can reduce the gap in what we have versus what we assign, because we have these skills here. They're just coded under another specialty. If we make some of these positions more immaterial, then we have a lot more assignment options and the ability to fill that talent requirement. So that one was fairly easy. We did it with existing data, existing position analysis.

Data governance and sharing platforms

So we've rolled one out. It doesn't have the kind of use that I would like it to have. It's the enterprise EDSC, enterprise data system catalog. So it should have everything registered in it that we have and that we're collecting. And if we could get more people accessing it, I think that would go toward a lot of our problems. The hard part is we're not using the tool directly in with the data systems where that data is stored. So we have to manually update it. But it's something that we've considered. We've identified that as a need. And having it, one of the things I'm looking for is a more intelligent data catalog that we could roll out.

So a lot of it is no point to point transfer. We do just extract transfer load and we've got a number of our systems talking to each other through APIs. But otherwise, we just have to figure out all kinds of different ways to get the data sent to the data science environment that we're using. It's not always easy. I mean, we were at the point where they were emailing or they're not emailing, but where they were physically mailing hard drives. So we're trying to get away from that, obviously.

Building professional portfolios with protected data

An anonymous question, interesting question on Slido is how does your team build their own professional portfolios for when they may move on to a new job? Is how can they show what they've done without sharing protected info?

Yeah, we've we've talked about that a lot. And the way I've kind of treated the work my team does and because I've been in the position and even though this is a new team, a lot of the team I'm working with was one I worked with for three and a half years in Army Talent Management with Army People Analytics. I treat all my new hires like like they're grad students coming in and they're going to come, they're going to explore the data, they're going to get familiar with the data sets we use, get familiar with the business processes, and then they kind of will find something that works as a thesis project.

So like what is the thing that grabs them? What is the thing that's the most interesting? What's most suited to their capabilities? And we've really kind of built things around that. We've been lucky in that there's been so much to do that we could align people to the projects that they are most interested in. The thing that we're trying to work out is, you know, what happens if we have projects that are prioritized that don't have the same grab? But that's kind of how I have it. I have this whole laundry list of things that I want to get after. And I kind of like, you know, your grad advisor did with their research projects and like, hey, let me point you in this direction, go research this a little bit, develop this program or this problem a little bit more. Let's see if that grabs you.

Navigating OPM requirements

Yeah, office personnel management. So we really just had to get into the same way we did the military side, the letter of the law. It's like, what are these things actually say that we must do and what is written in DOD or army policy or something that's just more of like a legacy? Like we really try to go back to some of these things and talk about what the ground truth is. We haven't really had much of an issue modernizing, at least on the data side, because of all the direct hire authorities that we have.

We've really tried to push also towards doing more return hires, which scares people in government. They're like, oh, you mean they're not going to make this a career? People are working in the space, don't want to spend a lifetime in the same job anyway. You know, they're going to come in, they're going to do a thing, they're going to pick a next thing. And you can see if that piece is, you know, that particular position is validated at the same level because the projects mature. You might need some entry level folks or you might need some folks to do data development, data cleaning. And then after that, you'll realize you've got a different use for that data, different project, the composition of your project team is going to change.

So we've really tried to use the most flexible processes we have within the existing policies. But we're not afraid to kind of go up to OSD and say, hey, guys, we might want to rewrite this thing a little bit. Look at what we could do if you rewrite it. Here's our proposed language.

Creating a culture of experimentation

I noticed in your bio, you talk about your team conducting research and doing experiments, the prototype as well. And I was wondering, how do you create that culture where it's OK to do these experiments?

Getting your bio, like let's not throw a bunch of money at this. Let's try it first. I can try it for you for free. And you can figure out how much money you want to spend on the result. I mean, some of them are looking at different data integration in the selection boards. As we are doing that, we just have the board members stay on a couple of extra days, do a quick rundown with them and pilot some of the new look and feel of the board and see how much it changes their voting from the previous version. And we are looking at things like voter inter-reliability. We're able to explore things that we haven't been able to explore before.

And then we bring the findings back again, kind of looking at the quick wins. We find the things that are legit findings, bring those back to senior leaders and go, hey, guys, we tested this. But look at all this other stuff we found. There's more experiments. Let's go after this. Let's get them excited about it. We hype it up. And some of it's like it's not necessarily world changing right from the get go. But again, kind of the teaching and communicating piece we were talking about before, we've got to sell them on like, hey, here's this is just step one. This is just version one. If we keep developing this, think about what we could do.

Handling data gaps

So we have a ton of data doesn't necessarily mean it's the right data. And as we're going through these models, that's that was actually what kicked off our assessment program. It's like I don't have enough in people's files to be able to say what their what their talent attributes are. And so we go over to our research entities, Army Research Institute, Army Research Labs and say, you guys got any kind of existing studies that we can peel a couple things off of and take a look at and maybe experiment and go collect some more data with you guys on this? We've had a really good partnership with them on it. It's also allowed them to kind of modernize how they're approaching research because they've got these things that they can peel off and show quick wins from their findings rather than it's like this five year study that all the leadership is going to change out before it's done.

Work-life balance and taking time off

Chris, I saw something you had posted on LinkedIn. I think it was maybe like a few days ago about like the importance of being able to take time off and to be fully off during that time. And I saw you were asking some advice to other people from your LinkedIn network, too, and was just wondering like what your thoughts are on that and what you're you're learning from your own network, too.

Well, that's kind of the fun part about this network. I love throwing questions out there and just saying, hey, guys, I'm human. I don't I do a lot of things right. I've been put in a position because, you know, you can wave your awesome flag all day. But it's also like I screw up a lot of things. I am horrible at taking time off. You know, I am I will compulsively go and check things. If not, you know, I've got my Mike Carroll, who's our lead for procurement, but he's also my accountability buddy there. He's just like, I see you. I see you on teams. Get off. Go home.

Um, so I love throwing things like that out to the community and just saying, hey, you know, I'm not the only person who's dealing with this. And this is a professional wellness thing that I think all of us can benefit from the discussion on. Let me throw it out there. You guys tell me what works for you. And a lot of it kind of came back that same thing. It's like we need that time to do an active or inactive recharge. Sometimes it's like doing yoga or cross training when you've been trying to ramp up for a race. You know, you've got to do something, but make it something that's not work. So, you know, trying to figure out those good substitutions or those complimentary activities. And we talked earlier about, you know, was one of my favorite things to do. Not at work. I love hiking, love cooking, love going to the beach. And I think we've got to let ourselves know that not only is it OK to have hobbies, not everything has to be work and hustle, but those things actually complement your ability to refresh, recharge and do your job.

So this is a personal experience and I agree wholeheartedly that time off should be time away from work. But the kind of personal anecdote is how many times do I see a quick question? Hey, I've got a quick question on Teams and I'm like, oh, yeah, I can answer that. And it'll only take a couple of minutes. And I'll just help this because that will unblock that person. Right. So you just fire off a quick, oh, this is where you find that thing. Right. But the other thing is that if you don't do that, they don't sit there blocked for the 24, 48, 72 week long that you're at the office. They find the solution themselves.

You know, so it's almost like, well, yes, I've been very helpful and have answered this question quickly, but actually I've taken my brain from doing the thing that I was doing to answering a work question and then I've got to come back to the kind of out of office brain. It's hard.

Well, there's a big part to that, too, where it's like a little bit of parking that's the wanting to be helpful, but also the you know, there are lots of people who could do this. There are a lot of people could answer this question or there are lots of people like on my team. I've got a team of amazing, awesome people who can handle almost like ninety nine point nine percent of the things out there. It's like there's a little bit of guilt. Am I dumping my work off on somebody else for this time I'm taking off? But at the same time, it's just like, you know, I need I need to give them the same the same respect when they go on leave and know they're not dumping off their work. I'm helping them carry stuff or at least kind of put it in a holding pattern until they get back.

Hi. Yeah, I just I'm I'm a strong advocate of people being away when they're away. And something that I've noticed, people will use your your email inbox as kind of a placeholder for things that they want you to address when you come back. So you come back to an inbox of, you know, three hundred and fifty emails, you know, a mixed bag of things that you need, things that you don't need to see, you know, things that you're copied on just for an FYI. So I put in my out of office explicitly that I'm not responding to anything that's sent while I'm on PTO and that if people want me to address something to just delay send it until I get back.

Impacts of the innovation cell's work

I just wanted to ask, are there any like impacts that the Army has changed due to the work with this new team that you have or maybe people analytics that team that we see in the Army today that are directly from the work that you were doing?

I think the biggest one is just getting rid of the additional selection boards. I mean, we've taken those and that's about up to eight weeks of time where we have a bunch of sitting in a room, which is like, OK, well, just they're going into an assessment based on this invitation that's going to get the in-depth detail and they're going to go into interviews. They're going to go into all these things. The file scrub, the automatic kind of scoring of these things that they spend all this time on, they don't need to do that. We can automate that. And we did we automate it. We validate it with the branches just to make sure it makes sense. We get that human back in the loop. But that's going to free up a ton of time, but it's also uncovered a lot of things that we need to do with performance management.

Otherwise, I mean, just kind of the introduction of the the two way market that HRC is using, using the preference assignments. We've we're about to pilot a randomized controlled trial for retention incentives using the retention prediction model that we have as kind of an a priori statistical indicator whether or not someone is eligible for retention. And then if they take one of these incentives and sign an additional duty service obligation, we know it's like, OK, that's a win. We can get a statistical lift prediction from these pilots on how likely these different incentives are to keep people. So I think once we get that rolled out, it's going to have a lot of impact. But we're at the point where we've got the model built. We've got the retention incentives kind of picked. Now it's time to do the experiment.

Personal journey and career advice

It was a it was an interesting ramp up because undergrad, my undergrad was in operations research. I commissioned as an engineer, so I really didn't touch anything until I went back to grad school and went back to grad school and studied complexity science. I did a lot of event simulation, probabilistic design stuff that doesn't really come into the people enterprise, went and taught that at West Point for a while. And then my first job as an operations research and systems analyst was working theater security cooperation and building partner capacity, kind of counterterrorism stuff for Indo Paycom.

Then my branch pulled a fast one on me and said, we're going to send you to the Army G one. So he sent me to G1 for I don't know anything about HR. I don't know. I'm not a personalist. OK, well, I guess I better figure this out. And that kind of was the best thing that could have happened to me because I was the Napoleon's corporal sitting there in the corner going, why do you do this? What is it? This doesn't make sense. Why is this thing here? Why are we using Excel spreadsheets where we've got an environment where you can house all this? Oh, because it doesn't have the data in it. How do I get the data put in it?

And it just you know, this is kind of the culmination of eight years of curiosity and building things and asking questions and trying to get things simulated and big problems in space that wasn't really heavy in the data sphere being answered by data. And now we have got secretaries focus on it. We have a lot of different focuses coming in that are leading us to be more data driven. And when the opportunity came up to not just keep working on it, because I started people analytics and, you know, we got I didn't start it completely from scratch. We took it, the human capital, big data program and rolled it up under an existing office that was actually going to do make use and figure out how to build some serious models out of some of this data we were using. I did kind of the same thing when I was a talent management. I built a little startup within there that was churning out of these data products. And to me, this was kind of the opportunity to do the same thing within HRC is to sell my other startups corporate. And I'm going to go over and do this other little startup.

And it's making me think of a question I love to ask people is, is there a piece of career advice that you've either given or received that you'd like to share with us?

It's a tricky one to give, especially in the army, where you're like your first five to eight years. It's all about, you know, conformity and compliance. And after that, all of a sudden they kind of expect you to flip a switch and go, OK, now you're in charge of things, be creative. And a lot of people who just kind of get gummed up in the works, they don't know how to do it. I think I tell people to find, you know, we were talking about the hobbies and the other things that we want to do to exercise our creative energy, kind of those opposites, muscle, mental muscles. And I just tell people, you know, it's going to sound silly, but, you know, exercise your creative pursuits, find a way to exercise that creative energy so that, you know, you can keep the brain, the brain flexible, especially if you want to go into spaces like this where it's not charted. It's very ambiguous.

There's not a whole lot of the last three jobs I had didn't exist. They were created and I got stuck in them. And there's no book, there's no notes, there's no anything about how to do this particular job. I mean, so I had to go out, you know, like we were talking about before. If you're not there to answer a question, the person's going to go find the answers. So I'm going to go out and talk to other HR professionals and see how they're doing this. I'm going to go talk to other data science professionals and see how they're doing this and put together, you know, kind of a hodgepodge answer to try out and experiment. And I mean, this is just a kind of a great place for it. I saw a pop up in the comments. Data science is a great place to be curious.

There's not a whole lot of the last three jobs I had didn't exist. They were created and I got stuck in them. And there's no book, there's no notes, there's no anything about how to do this particular job.

If I were in my home office, I have a poster of Sherlock Holmes from the movie up behind because he's always like, data, data, data. I know I need data. I need to solve problems. I want us to go and, you know, dig into this. I want to solve a case. I'm going to be a data detective. So it just gets you excited. I mean, find a way to exercise those creative pursuits because they're fun. And you'll find, I think you'll use those to find that kind of creative fun in your work also.

Staying current and book recommendations

Conversations like this, getting out to, you know, I've made the argument that the army, because we've pulled back on conferences for a while. It's like, we need to go to professional conferences. We need to go to industry conferences. We need to go talk to people who are doing this work. We need to go back to school. We need to go do training with industry. You know, we need to be able to go and find the people who are leading the edge and create those networks. So, you know, when we do have a problem and we're like, okay, what is, what is industry doing, we go, who's leading in this edge, what kind of technology could we take, or what do we need to develop and how do we do it that you've got a phone, a friend available.

I've got a whole laundry list. Um, uh, it's probably been brought up a few times, but if you're doing any kind of transformation and modernization, the unicorn project is a must read. Um, I'm reading, um, farther, faster and far less drama by Janice Frazier. Right now. It's about change management and transforming organizations. That's another good one. And I got recommended, um, Jeff bears, um, by, uh, it's called hug your haters and it's about customer experience and customer service. But the reason I love it is it teaches you to seek out actively seek out criticism and critics and find the people who are just making noise, but also find the ones who have a legitimate complaint and figure out how you can solve their problems for them. So that's, I love that.

Well, thank you so much, Chris, for joining us today. It's been a pleasure hearing from you and learning from your experience. Thank you for all that you do. Well, thank you so much for letting me join. This is awesome. Have a great rest of the day, everybody. Thanks so much for joining us. We'll see y'all next week.