#93 How Data Science Drives Value for Finance Teams

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You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization. Join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello, this is Adele, Data Science Educator and Evangelist at Data Camp. Whenever we think about data science within organizations, I think it's easy to get caught up in high profile, super difficult to achieve use cases like computer vision, algorithmic trading, self driving cars and more. However, I'd argue that a large portion of the value data science provides is providing an insight layer for all of the organization to use. Arguably, a team that has been providing a lot of the insights for different teams before data was a thing is the finance team and this is what we'll be discussing with today's guest. Brian Riccardi currently leads the Finance Data Science team at Stryker. He has over 15 years of experience as a finance leader supporting multi billion dollar businesses. He has a passion for leveraging data analytics to maximize finance, a strategic partnership and optimize business processes. He's led several data analytics initiatives and teams over his career and he specializes in various business areas ranging from finance, R and D, manufacturing, marketing and sales. Earlier in his career Brian started off in pure finance role and earlier in his career Brian started off in pure finance roles and ascended the ranks to financial leadership roles and now to data analytics. Throughout the episode we discuss how finance teams in the past provided a strong insights layer that many data teams provide today. Where data teams and finance teams intersect and where they do not how with upskilling, finance teams can be a strong talent feeder for the data organization How Brian transitioned from Finance to Data and more now just a small warning. The audio quality today wasn't as good as we hoped it would be, but it's still definitely audible. If you enjoyed this episode, make sure to rate, subscribe and comment, but only if you like it. Now let's get started. Brian, it's great to have you on the show.

Yeah, thanks for having me. Excited to be here.

I am excited to talk to you about the intersection of finance and data science, how finance teams are the hidden gems when it comes to data for many organizations today and how you've maneuvered the transition from being a finance leader to a data science leader. But first, can you give us a bit of a background about yourself and what got you to where you are today?

Yeah, sure. So my, I guess it started my undergrad was in economics. I had a minor in music and I started my career pretty much right out of school at Domino Shooker. And that was still I was all, you know, based in New Jersey and there were kind of two key things there that happened and I was very fortunate earlier in my career that helped shaped my progression and my trajectory. One is I started as an analyst and primarily a BI analyst and then I transitioned over time all within finance, but transitioned as the head of commercial finance for the US after I was concluding my career there after almost 12 years and we were 100% on SAP. So that exposed me to a lot of SAP modules and tools all around analytics. And we had a really, really good culture, robust culture around self service analytics from the top down. So I did a lot there with master data. I even owned some pieces of master data when I was there, did a lot of like report creation and deploying those tools to the business that all functions pretty much in the business, touched and consumed. And the second thing there was that I just had amazing experiences of working with people and kind of people who were kind of took under their wing to help guide my career and always had a foundation in finance but was fortunate these folks coached me and taught me the importance of an enterprise wide perspective. So kind of using things with a finance lens but seeing it from the, from an entire company perspective. And data analytics was, was a really key, a key part of that. And then from there I went to Stryker and you know, with my background in analytics and finance I did a lot of proof of concept work in data science and we were exploring, you know, standing up some data science capabilities and it was all around AI and forecasting for finance that eventually led to the creation of the team I'm leading now, the finance data science team.

That's so great. So I'd love to set the stage for today's discussion and Lay the groundwork for the rest of our talk. You mentioned here your experience starting off in finance and growing into a data leader because of the different experiences you've had and the amount of exposure you had in data and the finance function. The prevalence of this volume and richness of data is the standard for a lot of finance teams today. So I'd love if we can first break down all the ways a finance team, say, you know, 10, 15 years ago, was to a certain extent the de facto data science team in many organizations. And can you describe the areas of overlap between these two functions as you've seen them evolve?

Yeah, it's a good observation and it's true. I mean, in finance, you interact with data across many domains, right? From customer, product, suppliers, traditional finance, chart of accounts. And typically the finance folks are asked to bring it all together, right, to tell you what's happened or what's going to happen. So it's very important to be complete and as accurate as you can because you never know how that information is going to be used to make a key decision or lead to other activities. Right. So the finance folks, they know all the ins and outs of the data, they know where there are gaps. And usually I can speak from experience. In my career, I found ways to bring things together from multiple sources and knew where I had to do a little bit of more kind of work behind the scenes to either clean up the data or, or normalize it if I was pulling things from different sources. So because of that, you really know from start to finish how it's created and where it goes to, where ultimately it ends up with you. So whether it's daps in quality and accessibility, how it's being used, going back to things, like I would go back to the points at times when an order was created and ask, how is this done? Because on the back end I'm seeing, you know, that maybe the margin or something isn't matching up to something else I had. So you kind of have a full, full range review of all those things. And we rely on data from a ton of sources, right. And we consolidated a lot in Excel back then. Today that's becoming more of a job for SQL. I'm finding I see more finance professionals coming out of school or just having that skill they picked up on their own. But it really goes back to, you know, you can have a great experience and skill with SQL, with Python, any, any kind of coding or data science practices, but what's really powerful and really critical is that you have a domain expertise and that was something I think in finance you pick up just organically through your day to day. So understanding what the data means, but also how it's used and then getting, understanding how the business operates. So understanding customers challenges. I was always in a finance analytics function with really large data sets, some of them even coming from external sources. So when I would join those together, I would kind of have to know what it all meant and how they were all linked. So it was a lot of stitching things together from, from different sources, talking to a lot of people. You build a lot of relationships along the way because, you know, I certainly did have all the answers. I relied heavily on those functional experts and SMEs and tying that all together. I mean, something in finance that I think doesn't really isn't apparent to a lot of people is there's a ton of project work. So a lot of project management. I've done a lot of like process optimization. So that's, that kind of gets integrated with finance. And I find it being integrated more and more now as people are trying to maybe improve their data or improve processes around analytics. There's a lot of projects that get spun up that kind of lead to those results or those kind of new tools.

That's awesome. And I think in some sense the finance experience also gives you a crash course in business acumen that a data role wouldn't necessarily give you, which is very advantageous for a data leader given what you laid out. What are common use cases finance teams worked on in the past that you think would surprise listeners to learn that they were owned by finance?

Yeah, I think the analytics side of finance is less understood and it's becoming more and more prevalent. I think. You know, when I came out of school and started working in finance, there still was a heavy focus on like traditional accounting practices. Right. And I was fortunate. Again. I had great leaders and coaches who were, they were extremely analytical. The person I worked for was the first person who started or was part of the analytics, the sales analytics team at Domino Sugar. And that was 10 years before I even got there. I feel like they were really setting the trend and ahead of the curve. There's always some link to the financials that goes outside of finance, like marketing, manufacturing and every, every finance function at every company is different. Most people were surprised that a priority for me was ownership of master data. One of the first things I was asked to do when I moved from being a BI analyst to working more core finance was build a product hierarchy for, for the entire business So I had to go to every division leader, ask them how they looked at their business, and then replicate that in our BI tools in our master data environment. And they loved it so much, they came back and said, can you do the same thing for customer? So I built a customer segmentation hierarchy or infrastructure that we then linked through SAP. And that was really powerful because we could look at all of our customers through different slices and all the products. So we could. We had a ton of different ways we can combine and look at it. So really through that, you. You take the input from the business and what they're looking to solve, what questions they have to answer. And for me, I was fortunate. Like, I didn't just come back and give an answer. I was asked to work sometimes directly or indirectly as an advisor. So there were times which was pretty cool. You know, I'm out of school a couple. Couple of years, and a lot of folks that I was working with have been working in the sugar industry longer than I've been alive. And they're asking, hey, what do you think about this? How should I communicate this to the CEO or to the board? What would you say? Or do you think I should say? Can you go back and go a little deeper on some things? Let's talk about it. So you really felt like you were integrated in the business? Finance is great and I love the career I've had in it, but I never really felt like I was just a finance person. I really felt a part of the sales team or the marketing team or the R D team. And I was fortunate. They really embraced me too, as part of their team as well. And we worked together with our strengths and with that, you know, we just led to projects, a lot of project management on, hey, this is great. How do we do it better in the future? How do we improve our processes? When I was at Domino, we were a pretty lean group, but we were growing pretty quickly year over year, went through a lot of acquisitions, and we couldn't afford to add a ton of headcount. So we had to figure out ways to do what we were doing better. And really what helped us from tremendously, in my opinion, was we had really robust data governance. So we were able to bolt on these companies when they came in and integrate them in our environments and just replicate our reporting tools and our processes pretty quickly. So it was nice to make those integrations faster. When you're bringing in other companies with different cultures, it was a little less friction there. So all those things coming together, I feel are really Important to finance and the things that happen. I guess every company is different, but under the surface really isn't apparent for someone from the outside looking in.

Yeah, definitely. And you mentioned here being a partner with the marketing team, with sales, with R and D. And I think one of the few departments that partners with other parts of the organization like finance is the data science team. Because data science is also trying to help different teams achieve their business goals, make them work more efficiently and produce more value. So this holistic perspective and view of the business, I think is what sets finance apart from other teams and highlights similarities to the data team. How do you think that vantage point provides finance teams today to take advantage of the company's data and to provide value for the organization, for the other teams that is akin to the data team's value.

Yeah, I think finance folks are able to connect the dots and I think within data science as well, if you have that underlying domain knowledge, right, people from different experiences, especially if they've been in the business for a while, that really helps to understand how everything's connected. For example, for me, when I was a BI developer, I would build a dashboard or report for someone in the business. I didn't have to hand it over to them and have them check it and say, hey, does this work for you? I knew what they were looking at, I knew what they were trying to answer and I could do it on my own and say level one, like low level analysis, right? Like, hey, I know this isn't acceptable or I know maybe what they're working on today or the challenge they have. I know I've got to be able to provide some insights for that through whatever I'm building for them. I think the same is for data science too, right? If you can take the data you receive from the business and, and you know how it's being used and the systems it's coming from. For example, to go in there and run a report on sales or just, or anything, inventory or any other kind of key metric and be able to go deeper into the data and really understand what it means. It kind of helps you end to end build something that's usable for the business. Right. And that's what it's all about. You don't want to build it and then it sits on a shelf and it dies on the vine. But I think just having that holistic view like you mentioned and just understanding how everything's connected is really important. And that really comes through collaboration. I mean, I think a key strength of finance and a key Ingredient to success is the collaboration and talking to all those various functions and having those kind of bridges built. And what I used to do a lot and still do is bring those functions together all at once. So I'll bring it marketing, R and D, whoever together, talk about something we're working on and talk through it. Because sometimes the data has different touch points in it that someone in marketing, for example, isn't just going to know by themselves.

That's great. And harping on that you mentioned here, bringing people together, how is that collaboration and the ability to bring people together and provide value with data been helpful for you in accelerating your transition from finance leadership to analytics leadership?

Yeah, early in my career I was kind of taught it was part of the group I was in was that the business is our customer, right? So we treated them, of course, like they were our partners and we were, we were part of the same, we were all on the same team, but they were really our customers. So whatever we delivered to them, from a tool for reporting and analysis down to a piece of advice, help them do their jobs better, which in turn helped the company. So I think I've carried that through to data science and really it's, you know, the business is our customer and things start as projects, right? Where typically doing like a proof of concept or, you know, even in my early days it was like, hey, can we do this right? Is this going to work? Ultimately then you're delivering, delivering something to the business that's a product, whether you're not selling it to the market externally, but it's a product for the business to use. So a model, right? It's out there every day and people are relying on it to run, to not fail or error out. They're allowing that data comes out the same way every time. There's a cost to entry and then that it's, it's meaningful and it's representative of kind of what they're expecting, right, for an insight or, or a piece of analytics. So I think that's really important. And then you have to iterate over time, right? As the technology improves or the business changes. You got to be sure you're reflecting that in your modeling and in your tools. So all those things are really important to ensure, you know, you're delivering something to the, to the business that's usable and ultimately you're keeping your customers happy and over time kind of showing them a roadmap of what else you got coming down the road. Because everything kind of builds, right? Everything that's a proof of concept. But my opinion should be if you're part of your roadmap and ultimately leads you to something that ultimately become a product and something that can be scaled across the business. Because even early in my career a lot of things we were doing, we couldn't afford to do things in a nuanced fashion, in a fragmented fashion across the business, everyone had to do things the same way. So we had a common reporting platform, we analyzed sales the same way, we looked at our business the same way from a product standpoint, from a customer standpoint. And that kept everybody on the same page, avoided unnecessary conversation, and you spent more time talking about, hey, how are we going to achieve what we want to achieve versus hey, what are you trying to say? What's the current state? 10 different ways just to get to the point where you're trying to solve a problem.

Now that you made a transition from being a finance leader to a data science leader, what do you think you're able to do more with data that you couldn't necessarily necessarily do before?

Early in my career we did not have cloud at all, right. And I remember building these queries as a BI analyst and my goal was to get people out of like the source erp, right, and to stop running these like very clunky, not very user friendly reports, right? Because we had this BI suite with like drag and drop and it was very slick, everybody loved it. And you run it to a certain point and you'd get to a level of detail that wasn't there and then you have to write down all your parameters. I remember people doing this and printing things out and then replicating that in the source system, logging in and then they would maybe get to what they needed, where they needed to be. So for example, going down to invoice level detail. So I said, great, I'll build queries where you can get all that. But then they would just bomb out because it just, it couldn't handle all that data. So we moved to late, very late when I was there to a cloud platform. And I would say, all right, let me, let me go check and run some of these queries. I used to, I built and they ran in seconds. And that was just a game changer. And, and people knew about them and they were collecting dust because they, they knew they weren't going to run. And that was really a key point for us in changing kind of our self service reporting because people then felt they could stay in one place to get everything they ever needed. And if you were going to go into the transactional erp. You were going in there to do a transaction, you were creating an order, you were booking a journal entry. You might be just going in there to check a specific order, certain line items in an order. You were doing all your analytics in one spot and it was great because it took stress off of the system. You know that, that was huge.

That's awesome. And segueing here. I'd love to be able to understand as well, beyond what you've been able to do with new technologies per se, what are some of the use cases that you've been able to activate as part of this transition from finance leadership to data SC science leadership, especially given what the technology is allowed, but also having more executive support to be able to activate and launch high value use cases for the entire organization.

Yeah, you touched on it. The executive support is, is really important and I've been fortunate to have that throughout my career and even early on, you know, I learned how to navigate our BI tools and where all the data was from. Our CEO, you had a finance background and I remember him showing me, hey, Brian, here's how you analyze sales. Here's what I do, here's all the tricks I have. So that, that was really important. And I'm, you know, I'm 24, 25. I figured everybody knew how to do that. I moved to other companies and other roles and people don't. But I think that the cloud storage was huge. The cloud computing also just gave us so much freedom to do things. Where you go in Excel and you're sitting there waiting for things to open and load, and that was the world I lived in. That's a waste of time, right? You have people and finance professionals. Talent's hard to find, especially data scientists. The last thing you want them doing is sitting waiting for things to churn. Right. Time is most valuable. If you can cut that down to the point where you're ingesting data, running the model and getting an output to start really analyzing it and bringing it to the business that's most powerful. So that's the freedom that I find is really valuable and my team finds really valuable too, is that we can do things really quickly. And really in finance, you have a view of everything going on like I talked about and people are coming to you with lots of challenges and you have visibility to things that you find on your own because it really is a heavy collaborative function. And we're embedded in finance, so we're there from everything from sales forecasting to looking at all items in the P and L to the balance sheet cash flow. And there's really a great interest in it. I would say across finance it's becoming more integrated. Who doesn't want someone to come help them get better analytics or take time away from having to compile stuff in Excel? So there's really a strong pull so we don't really have to go out and sell too much. And I think that's, that's going to be or it is pretty common for what I'm seeing in finance. It's, it'll it all just a matter of having that data ready to go and having the people and the talent that can do it.

So we talked about the past when we discussed how the finance team in a lot of ways was the custodian of the organization's data and how it was activating a lot of analytics use cases that you would normally associate with the data science team. I'd love to switch over to the present and discuss how to empower the modern finance team with new tools and technologies that data analytics teams are using. I'd love it if you can break down how you view the importance of upskilling for finance teams and the value that you can have have from a finance department once you equip it with tools that go beyond Excel.

Yeah, I remember doing budgets as an analyst and I would just every year I would count how many rows of data we were churning through in Excel and it got up, I think to like over 40,000. And that was really detailed information. I remember saying like at some point we're going to reach capacity and what we can get from this and it's going to be counterproductive. We're just going to band Aid things and we're going to spending more money and resources going through this. And I think what's been interesting for me to see is that evolution to where I think SQL is the future. Right. And it's becoming more integrated in finance where you have a limited capacity to store data and the speed in which you can retrieve it. I think it's really going to replace Excel and I see it already happening like Excel's a requirement and every JD for a finance role. I think you're going to see SQL there more and more. I think the data visualization tools as well. When I learned to do bi, I had to go to SAP school for. In total, I think I went for almost two months altogether. It was really hard to get access to the data, access to those cubes, those DSOs, those data layers, pull it in like you needed a really. There's a large learning curve and I was really the only one who knew how to do it. Now you can get a tableau or Power Bi and be up and running in no time. So I would even say if you're starting from nowhere or you're starting from, from step one, go get one of those tools. I think you can get them even for free, or there's a ton out there, start doing that and, and start learning SQL. I mean, you could start with online courses, then you can kind of work your way up to boot camps and you can go from there. And if you're at a large company, there's always somebody, especially the likelihood, that's doing this for fun and learning it. I always find somebody who knows SQL or who knows Python and I just kind of add them to my list as someone I need to keep in contact with. It's really incredible how it's just the pockets of excellence are building on their.

Own and following up here. What do you think are the main tools and techniques finance teams need to learn to become more effective at their roles?

Yeah, I think collaboration is huge because you only, you only know what you know. You don't know what you don't know. So I think having that connection to the business to understand what's going on, what are the challenges, that helps me a lot, has always helped me in my career identify what are the right things I should be working on to add value. Like, I have the things that I think are cool and fun, but again, it's got to be useful for the business and translate to business results. It's got to help somehow. Again, like the tools and techniques, it all comes back, I think, to SQL, it comes back to that data visualization and light coding, bringing that all together with that collaborative nature that is within finance, that domain expertise. I know I keep saying it, but it's really not something to take for granted. It doesn't happen overnight. I've been on teams with it and without it, and I see how quickly you can move too. There's always that element of, am I doing something that's right for the business that's going to be useful, but the speed in which you can move through it is really night and day. If you have that domain expertise in house on your team, so you don't have to say, wait, I don't know how to calculate this financial metric. I got to go ask somebody. People are busy, right, and they come to you to help them build a solution. If you know that in House and you can say, yeah, I know how to calculate this or I know how to do this metric or I know what this means. I've seen this when I was in the business as a finance leader or a financial analyst. Let's work on helps everybody and at the end it helps build your reputation as a data science team that you're going to deliver value and it's not going to be another thing for the, for the business to have to worry about or take on to come to you in a project. You can make it low risk, low effort to get something that's really valuable and it'll help you build strong momentum.

I couldn't agree more. Especially when it comes to subject matter expertise. It's relatively easy to upscale on Python, SQL, Power, bi because ultimately it's skill agnostic tools. You're just learning a tool. But as soon as you have subject matter expertise in a data set and you're working with complex financial data sets, this is where supplementing that subject matter expertise with technical skills will really drive the potential for a finance team.

Yeah, it's a great point. And look, I think it all comes down to your desire to learn, right? And how fast you learn. If you're fortunate to be a quick learner and you have a knack for things like you said, like Python and SQL and coding, you just, it's a matter of just getting integrated in your business and vice versa. If you're very well versed in your business or a function, start learning Python, start learning SQL. There's so many resources out there that, and you probably don't know more than me that can help you upskill, right? And you can do it in your own time. Like for example, I had to go drive two hours and stay at a hotel for a week to learn data extraction and I had this big binder and I had to flip through it and everything. And like now it's just there's modules you learn, then you practice right away. I think that's really important as you're upskilling tools out there where you can learn and then practice it right away through a module. Super powerful. I always recommend that the people who ask me is like, how can I get started? The sooner you can start practicing and applying it, the better.

So I'm sure you've worked with a lot of data scientists who needed to be upskilled on subject matter expertise and finance folks who needed to be upskilled on data tools and technologies. Can you walk me through an example of how you would set up Such a learning program.

Yeah, I think early on we would just do in my career just overview of basic things like customer master product hierarchy. We would build really good training documentation for people, like I said, so they could do it on their own in practice. Right. They didn't need me there or my team there with them all the way through to side by side. They could do it on their own. We will record videos for people of things that they can use like a lot of Salesforce. In my prior company we would give them a whole toolkit to be able to do things on their own. It would be there to help them and then they would train each other as new people joined, which, which was great. I think for me what's been helpful currently is just, I think bringing the community together, your data science community, or even beyond that, your data analytics community, through events, especially with COVID doing things in person has been great and people sharing what they're working on.

Especially for finance teams looking to learn technical data tools.

Yeah, absolutely. I think inherently you have that analytical muscle. You're exercising it every day and, and you're, you're getting a perspective on every area of the business. You're balancing a very, I don't want to call it like heads down, but you're, you're deep into the data at the same time you're partnering with the business. And depending where you are in your career, you might have been through a rotation where you worked with someone in marketing as a, as their finance partner, you've worked with someone in R D or you've worked with someone maybe on the manufacturing side and you just got a breadth of knowledge that's really valuable to the business and to take that and tap into it and use it to fuel the starting or the evolution of a data science function is really powerful. And if you pair that with people with strengths who come from a different background, where they were more in the code encoding and they find SQL and Python and those data science practices like machine learning, very, very, very common and it's very natural to them. You bring those two together, it's really powerful. They learn from each other. You're never going to find or if you do, you probably can't afford them. The person who has all of it right, you can't afford a whole team or find everyone that has every single aspect. It's like any team, you've got to find the strengths and you've got to start with you know what you have today, know what you have, know where you want to go and what skills you need and the difference is when you go out and hire internally or externally and that that changes over time. I have a spectrum I look at that says, okay, here are all the skills I need for my data science team. Here's what I'm trying to do and I, I adjust that and review it all the time. I've changed it a lot and I, I feel bad changing because I presented a lot to the business. So then I'm like, well, this is what I presented like a month ago. But things have changed. This is a priority now. Now we need someone with these skills and experience. We need, for example, maybe someone with more machine learning experience or more cloud apps experience or someone with more data visualization experience. Because what the business is asking us to do is more of that and I don't have the capacity rather have someone who has that skill set and then we need to get it quickly or if we want to wait to learn it ourselves, we can do that. But usually you don't want to wait when the business really needs it. You got to find a way to make it happen.

That's so great. And do you think in some sense the finance team can act as a feeder organization for the data science function? And how do you manage the expectations of the business that's really shifting over time as a data leader to account for how you want to fill the gaps within your team?

Yeah, I think it's really important to have a roadmap and show what you want to do. I think what happens early on, what I've observed is that everyone gets really excited about data science or just in general data analytics. And you just a lot of people. It's great. There's a lot of demand, but it really gets caught up in a lot of buzzwords and you're doing a lot of cool stuff and it's hard to get out of that beta proof of concept phase. You got to show how it's tied to a product, right? How it's going to be delivered to the business to, to add value. So if you showcase what you're delivering and being in a finance data science team, it's really important to show what's the return on your investment, right? Like how are you automating work, how are you adding maybe increasing revenue or we're driving a cost avoidance or a cost savings or just something very focused on cash flow sometimes, and I'm guilty of this too, as a finance professional, you get really caught up in the cost of something because you're trying to make things work. Make a budget work. You gotta look at the benefit as well. Right. And you can. If you look at it less as like a cost and more of an investment, I think that's really important. And then showcasing what you're delivering. Right. And managing expectations to your. Your other question is really important and what. What I found is really, really helpful is be upfront on kind of your process, how you intake a request from the business. What's your project life cycle look like? Get in with the data and understand the challenges with the data right away. I've had projects throughout my career, we got to that later in the game, realized we had to backtrack and fix a lot of stuff. That hurts morale. It hurts the focus and the emphasis from the business and from your team. Be upfront with it. Bring in your data governance team, your process governance team, and just call out the risk. Right. Try to score it if you can too, try to measure it and then everyone will be thankful. Right? It's like, oh, I didn't know this was here. Analytics is great to solve problems, but it also helps you find problems too in your data. I think if you do that, it's really helpful and just be very, very transparent on your roadmap and what you have going on. People won't always be happy that they might be second or third in line, but it's worse when they're second and third in line and they're not hearing from you and you're not being transparent on what else is going on with it. Within reason. Right. If it's sensitive, obviously you can't share, but those are key elements I try to use. I don't always do it perfectly, but I try to stick to it as best I can.

We can always do our best. So, Brian, before we wrap up, I'd love to gaze into the future and understand where you think the future is headed. What do you think, what do you think are some of the most exciting trends you see in data science that will really affect the finance team's workflow?

Yeah, I think it's going to be integrated. It's going to be one of the same. You know, it's going to be a data science capability embedded in finance in every function. So there is like, you'll see like hub and spoke models or a COE of data science. I think those add value and those are important, but really you need to have them close to the business, those data scientists close to the business to understand what's happening, the domain expertise, the current challenges, the strategy, the business objectives and Tying the roadmap and the projects to that, I think you're going to have that integration hopefully becomes more part of the curriculums going further back in schools. When I went to my mba, we didn't have a business analytics concentration. I wish we did because I just, I went for finance because that's what I knew and that was the best option at the time. Now I see like business analytics or data science concentrations everywhere and I think that's great because it fully, it really just emphasizes and a testament to how it is getting more integrated into finance and other functions. So if, if you're a data scientist, don't think about oh, how am I ever going to get to finance? Or if you're in finance, how am I ever going to get into data science? I mean I've, I've pivoted back and forth from finance to IT, back to finance. Now I'm in a data science role within finance. It's, I think it's all going to blend together every, every company out there. No matter who you are, you need data to be successful somehow, some way. And you need someone who can handle large data sets and, and generate insights really quickly. And traditional tools that I grew up with through my career are not going to cut it. So you could probably get some way there. But if you want to have a competitive advantage and really scale your operations, no matter what function you're in, you're going to need data science.

That's awesome. Finally, Brian, before we wrap up, do you have any final words before we end today's episode?

Yeah, I would say make it a point to speak to your finance partners out there. You know, learn more about what they're doing and, and build that strategic partnership. I found everybody loves having a finance person on their side or at least in their corner helping them out, crunch, crunching the numbers or we're kind of another, another piece of insight. There's always that question when you bring a proposal or an investment is like, hey, did finance look at that? It just, it helps you, it helps you in your day to day and it helps you learn more from them and, and they'll learn from you as well. They'll, they'll be better in their jobs too. So I think that's, that's definitely a call to action. And if you're a data science scientist, especially hang out with your finance people more, go out for lunch if you're working virtually, set up a quick connection with them. And finally for me, I mean, get involved in the data science community. I love doing things like this and can't thank you enough for inviting me to chat with you and I've really enjoyed just getting to know you and just getting more integrated in the data science community. I love doing these things and hope to do more of them.

That's great. Thank you so much, Brian for coming on the podcast. You've been listening to Data Framed, a podcast by Data Camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment, and share episodes you love that helps us keep delivering insights into all things data. Thanks for listening. Until next time.