DataFramed
DataFramed

Episode 93 · 7 months ago

#93 How Data Science Drives Value for Finance Teams

ABOUT THIS EPISODE

Building data science functions has become tables takes for many organizations today. However, before data science functions were needed, the finance function acted as the insights layer for many organizations over the past. This means that working in finance has become an effective entry point into data science function for professionals across all spectrums.

Brian Richardi is the Head of Finance Data Science and Analytics at Stryker, a medical equipment manufacturing company based in Michigan, US. Brian brings over 14 years of global experience to the table. At Stryker, Brian leads a team of data scientists that use business data and machine learning to make predictions for optimization and automation.

In this episode, Brian talks about his experience as a data science leader transitioning from Finance, how he utilizes collaboration and effective communication to drive value, how leads the data science finance function at Stryker, and what the future of data science looks like in the finance space, and more.

You're listening to data framed, a podcast by data camp. In this show you'll hear all the latest trends and insights and 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 indepth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello, this is a dull data science educator and evangelists 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 a day's guest. Brian Recordi currently leads the Finance Data Science team at striker. He has over fifteen years of experience as a finance leader supporting multibillion dollar businesses. He has a passion for leveraging the Atalytics to maximize finance, strategic partnership and optimize business processes. He's led several datalytics initiatives and teams over his career and he specializes in various business areas, r. e. from finance, RD manufacturing, marketing and sales. Earlier in his career Bryan start off in pure finance role and earlier in his career Brian started off in pure finance roles and as sended the ranks to financial leadership roles and now to datatalytics. 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 upscaling, finance teams can be a strong talent feeder for the data organization, how Brian Transition from finance to data and more. Now, just a small warning. The audio quality today wasn't as good as we hope it would be, but it's still deftly audible. If you enjoy this episode, make sure to rate, subscribe and comment, but only if you like it. Now let's get started. Brian's wait 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, by, I guess it started by Undergrad. Was An economics I had a minor in music and I started my career pretty much right out of school at comino sugar, and that was still as well, you know, based in New Jersey, and there were card two key things there that happened. I was very fortunate earlier in my career that help shaped by progression in 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 a head of commercial finance for the US after I was concluded my career there after almost twelve years and we were a hundred percent on sap. So that exposed me to a lot of sap modules and tools all abound analytics. I we had a really, really good culture, or bus 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. The lit purport 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 I just had amazing experiences of working with people and kind of people who were kind of took under their waying to help guy my career and always had a foundation and finance, but was fortunate these folks coached me and taught me the importance of an enterprise. Why Perspective? So kind of using things with a finance lens, but seeing in 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 the striker and you know, with my background and 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 and forecasting for finance. That eventually led to the creation of the team on leading now the finance data science team. That's so great. So I'd left to set this stage for Diday is discussion and lay the ground work for the rest of our talk. You mentioned here you experience starting off in finance and growing into it. They had a leader because of the different experiences you've had in the amount of exposure you had in data in the finance function. The prevalence of this volume and richness of the day of data is the standard for a lot of finance, seems today. So I'd love if we can first break down all the ways of finance teams. Say you know ten fifteen years ago as to certain extend the de facto data science team and many organizations, and can you describe the areas of overlap between these two functions? I've as you've seen them evolved. Yeah, it's a good observation. It's true. I mean in finance you interact with data across many domains, right from customer product suppliers. Traditional finance, you know 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 is 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've found ways to bring things together from multiple sources and new where I had to do a little bit of more kind of work behind the scenes, either clean up the data or or normalize it fut pulling things from different sources. But because of that you really know from start to finish how it's created and where it goes to, where ultimate ends up with you. So where there's taps and quality and accessibility,...

...how it's being used. Going back to things like I would go back to the points at times when in order was created and asked how this is done, because on the back end I'm seeing you know that maybe the margin or or something isn't matching up to something else I have. So you kind of have a full, full range of view. Of all those things and we rely on data from a ton of sources, right, and we consolidated a lot and excel back then. Today that's becoming more of a job for sequel. I'm finding I see more finance professionals coming out of school or just having that skill they picked up on their own. But that really goes back to you. You can have a great experience and skill with sequel, with Python, any any kind of coding or data science practices, but what's really powerful or really critical is that the domain expertise, and that was something you know. 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 and alitics function with really large data sets, some of them even coming from external sources. So when I enjoying 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 didn't have all the answers. I I heavily on those functional experts and and he's and time 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 thought, a lot of like process optimization. So that's that kind of gets integrated with Fridays. I find it being integrated more and more now is people are trying to maybe improve their data or improve processes around analytics. There's a lot of projects that get spun up a 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, acument that a day to role wouldn't necessarily give you, which is very advantageous for a day to leader. Given what you laid out, what are common use cases finance seems worked on in the past that you think with 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 the traditional counting practices right and I was fortunate again I had great leaders and coaches who are they are extremely analytical. The person I work for was the first person who started or was part of the analytics sales analytics team domino sugar, and that was ten years before. I haven't got there, but I feel like they were really exciting the trend and ahead of the curve. There's always something to the financials that goes outside of finance, like marketing, manufacturing and every every finance function at every company's 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 did go to every division leader, asked them how they look at their business and then replicate that in our bi tools and our master data environment, and I love it so much that came back and said you do the same thing for customer by build a customer segmentation hierarchy or infrastructure that we didn't link through sap and that was really powerful because we can 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 take the input from the business and what they're looking to solve, what questions they have. They answer and for me I was fortunate like I didn't just come back and give an answer. I was asked to work sometimes direct your indirect as an advisor. So there were times which was pretty cool. You know, I'm at a school a couple or a couple of years and a lot of folks that was working with have been working in the sugar industry, love and I've been alive, and they're asking, Hey, what do you think about this? Har Should I communicate this to the CEO or to the board? What would you say? What 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 feel like you were integrated in the business. Finance is great and I love but 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. Where the RD team, and I was fortunate they really embrace to me too as part of the other team as well, and we work together with our strengths and and with that. It always led the 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 with a lot of acquisitions, and we couldn't afford at a ton of headcount. So we had to figure out ways to do what we're doing better. And really what helped us from tremendously, my opinion, was we had really robust data governance, so we were able to bolt on these companies when they came in, integrate them in our environments and just replicate all reporting tools on our processes pretty quickly. So it was nice to make those integrations faster. When you're bringing another companies with different cultures, it was a little less, littless friction there. So all those things coming together, I feel a really important to finance and the things that happen like I said, every company's different, but under the surface really isn't apparent from someone from the outside looking in. Yeah, definitely, and you mentioned here being a partner with the marketing team, with sales, with R 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 the holistic perspective and view of the business, I think, is what sets finance apart from other teams and highlights similarities of the data team. How do you think that vantage point provides finance teams to day...

...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 will 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 of the business. I didn't have to hand it over to them and had them check it and say, Hey, is this work for you? I know 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. To write. If you can take the data you receive from the business and and you know how it's being used in the systems that'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, they be able to go deeper into the data and really understand what it means. It kind of helps you add 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 when it guys on the vine. But I think just having that holistic view. Like you mentioned, just understanding how everything's connected is really important. I never really comes to collaboration. I mean I think a key strength of finance and a key ingredient new success is the collaboration and and talking to all those various functions and having those kind of bridges built. And what I used to do a lot still do is bring those functions together all at once. Well, bring it marketing, Rd whoever together, talk about something we're working on and talk through it, because sometimes the data has different, different touch points in it that someone in marking, for example, isn't just going to know by themselves. That's great and prping 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 and accelerating your transition from finance leadership to analytics leadership? Yeah, earlier my career I was kind of taught them as part of the group I was in was that the business is our customer, right, so we treated them, of course, like there are partners, and we were, we were part of the state and we were all on the same team, but they were really our customers. So wherever we delivered to that and from a tool for reporting and analysis down to a piece of advice help them do their jobs better, which in turn help the company. So I think I've carried that through to data science. And really it's the business is our customer and things start as projects, right, where typically doing the a proof of concept or even to my early days, it was like, Hey, can we do this? Right, it was just going to work. Also be 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 you do. So a model, right, it's...

...out there every day and people are relying on it to run, to not fail or error out, the relying that that data comes out the same way every time. But those are costs to entry. And then that it's meaningful and it's representative of kind of what they're expecting, right, for an insight or where a piece of analytics. So I think that's really important. And then you have to iterate over time, right, it's 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, if you're delivering something to the business that's usable and ultimate, you're keeping your customers happy and over time, kind of showing them a road map. But what else you got coming down the road? because everything kind of builds right, everything has to prove a concept of my opinion, should be if you are your road map and ultimately leads you to something that ultimately become a product and something that can be skilled across the business. Because even early my career, a lot of things we were doing we couldn't afford to do things in a NU ones fashion in the fragments of fashion. Across the business, everyone to do things the same way. So we had to condom reporting platform. We analyze sales the same way, we looked at our business the same way from a product standpointing from a customer standpoint, and that get everybody on the same page, avoided unnecessary conversation and you spend more time talking about hey, how are we going to achieve what we want to achieve, versus hey, what do you try to say? What's the current state? Ten 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 data science leader. What do you think you're able to do more with data that you couldn't necessarily do before? Earlier my career, we did not have cloud at all, right, and I remember they're building these Queeris as a BI analyst and my goal was to get people out of like the source, the ARP, right and to stop running these like very clunky, not very user friendly reporters. Right, because we have this bi sweet with like dragon drop and it was very slick. Everybody loved it, and you run it to a certain point and you get to a level of detail that wasn't there and then you have to write down all your parameters and more people doing this and prying 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, of going down to invoice level detail. So I say great, I'll build queries where you can get all that. But then they were just bomb out because it just 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 right, let me, let me go check and run some of these queries I used to I build and they ran in seconds and that was just the game changer. And and people knew about them and they were collecting dust because 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 at one place to get everything they ever needed. And if you were going to go into the transactional ARP, you're going in there to do a transaction, you were creating an order,...

...you were booking your journal entry, you might be just going in there to check a specific order, certain limes and in order. You were doing all your analytics to 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 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 the executive support. Is is really important and I had been fortunate to have that throughout my career and even early on. I Know I learned how to navigate our bi tools were all the data was from our CEO and you finance background, and I remember him showing me, Hey, Brian, here's how you analyze sales, here's what I do, there's all the tricks I have. So that that was really important. And I'm two thousand four hundred and twenty five. I figure everybody knew how to do that. I go to other companies with other roles and people don't. But I think the cloud storage was huge. The cloud computing also just gave us so much freedom to do things. Where you go and 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 had people in finance, professionals. Talent's hard to find, especially data scientists. The last thing you want them to do and is sitting waiting for things to turn 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 the start really analyzing it and bring it to the business. That's most powerful. So that's the freedom that I find is really valuable and 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, two 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 PNL to the balance sheet cash flow, and there's really a great interest in it, I would say across finance is becoming more integrated. Who doesn't want someone to come help them get better analytics or take time away from having to compile stuff and excel? So there's really a strong pull. We will really have to go out and sell too much and I think that's going to be is pretty common what I'm seeing your finance. It's it all. It all just a matter of having that data ready to go and having two 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'd 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 datalytics 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 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 out account how many rows of data we were turning through an excelent it. It got up, I think, to like over fortyzero and that is really detailed information. I remember say I like, at some point we're going to reach capacity and what we can get from this. It's going to be counterproductive. We're just going to band aid things and we're going to have 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 sequels the future right and it's becoming more integrated in finance, where you have a limited capacity right to store data and a speed in which you can retrieve it. I think it's really going to replace excel and I see it already happening, like excels or requirement and every jd for a finance role. I think you're going to see sequel there more and more. I think the data visile with visualization tools as well. Like when I learned to do be I I had to go, like I say, Pea School for Intro. I think I learned for almost two months all together. It was really hard to get access to the data, access to those cubes, those Disos, those day layers. Pull it in like you needed a really there's a large learning curve and I was really only want to know how to do it. Now you can get a tablower power by 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, and get it even for free, or there's a ton out there. Start doing that and and start learning sequel. I mean you can 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 and learning it. I always find somebody who does sequel 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 good the pockets of excellence are building on their own and following up here. What do you think are, in the main, tools and techniques finance teams need to learn to become more effective at the roles? Yeah, I think collaborations 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, where are the challenges? That helps me a lot, as always helped me in my career identify what are the right things I should be working on that add value, the guide the things that I think cool and fun. But again it's got to be useful for the business and translate to business results. It's got it somehow again, like the tools and techniques.

It all comes back, I think, the sequel. It comes back to that data visualization and light coding, bringing that all together with that collaborative nature that is within finance, that they'll be an expertise. I know I keep saying it, but it's really not something to take for great and it doesn't happen overnight. I've been on teams with it in without it, and I see how quickly you can move to is. There's always an 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 and hops 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 call me, 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 don't how to do this metric, for I know what this means. I've seen this when I was in the business of the finance leader or a financial analyst. Let's work on it. It helps everybody and, at the end of 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 with a project that you can make a low risk, low effort to get something that's really valuable and then to help you build strongmamentum. I couldn't agree more, especially when it comes to subject matter expertise. It's relatively easy to upskill on bythone sequel power and be I because ultimately it's skill agnostic tools. You're just learning a tool. But as soon as you have such a 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 were really drive the potential for a finance team. Yeah, it's a great point. It look I think it all comes down to your desire to learn right and how fast you learn. If you're a fortunate to be a quick learner and you have a knack for for things like you said, like Python and sequel encoding, you just do a matter of just getting into greating your business and vice versa. If you're a very well versed in your business or a function, start learning python and start warning sequel. It's there's so many resources out there that you probably don't know more than me that can help you up skill right and you can do it in your own time right for is that I had to go drive two hours instead of hotel for a week to learn data extraction and I had this big binder and I had to put 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 at the people who ask me is like a how can I get started? The suitor 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 up skilled on subject matter expertise and finance folks who needed to be up skilled 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 my career just overview of, you know, basic things like customer master, product Hier Key. We would build really good training documentation for people, like I said, so they could do it on their own 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 would record videos for people of things that they can use, like a lot of sales force my prior company. We would give them a whole tool kit to be able to do things on their own, movie there to help them and then they would trade each other, as do people enjoy, and which was great. I think for me what's been helpful currently is just they bringing the community together, your data science community, even beyond that, your data and Les Community, through events, especially would 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 had that analytical muscle. You're exercising it every day and you're getting a perspective on every area of the business. You're balancing the very I want to call like heads down, but you're deep into the data. At the same time you're partnering with the Business and pending where you are in your career, you might have been through a rotation where you worked with someone in marketing as their finance partner, you've worked with someone and R and D, or you've worked with someone maybe on the manufacturing side, and you just got a breath of knowledge that's really valuable to the business and the take that and tap into it and use it. The fuel the starting or or the evolution of the 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 and coding and they find sequel and Pythont in 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, a 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 got to start with you know what you have today. Know what you have, nor you want to go and what skills you need, and the difference is when you go out and higher internally or externally, 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 had just that review it all the time. I've changed it a lot and I feel bad changing because I presented a lot to the business so that I'm like, well, this is what I presented like a month ago, but things have changed. Is are priority now. Now we need someone with these skills and experience. We need, for example, maybe someone with more machine learning experience or more clasps experience or someone with more data visualization experience, because with the business is asking us to do is more of that. I don't have a capacity...

...and I don't 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 a happ 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 caps within your team. Yeah, I think it's really important to a roadmap and show what you want to do. I think what happens early on, what I observed is that everyone gets really excited about data science or just in general data analytics and use a lot of people. It's great as a lot of demand, but it really gets caught up in a lot of buzz words and you're doing a lot of cool stuff and it's hard to get out a Beta, prove a 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, is really important to show what's to return on your investment right, like how are you want abating work? How are you adding maybe increasing revenue, or we're driving a cost avoidance or a cost savings or do something very focused on cash web. Sometimes, and I'm guilty this too as a finance professional, you get really call up on the cost of something, because you're trying to make things work, make a budget work. You got all the at the benefit as well, right, and you can if you look at it less as a 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 other questions really important, and what I found is really really helpful is be up front on cutting your process, how you can take a request of 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 about my career we got to that later in the game realize 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 Up Front with it, bringing your data governance team, your process governance team and just call out the risk right. Try to score if you can, to try to measure it, and then it 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 own map and what you have going on. People will always be happy that they might be second or third in line, but it's worse when their second or third in line and they're not here from you and you're not being transparent and what else is going on with it within reason, right if but sensitive, obviously you can't share it. But those are key, key elements I try to use. I don't always do it perfectly, but I try to stick to it as best I can and 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 you think, what do you think are some of the most exciting trends you see in data science that we're really effect the finance team's workflow? Yeah, I think it's going to be integrated. It's going to be one in the same. You know, it's going to be the data science capability inbedded in finance and and every function. So there is like you'll see like hubben spoke models or a cooe of data science. I think those that value when 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, but the domain expertise, the current challenges, the strategy, the business objectives and tying the the road map and the projects to that. I think you're going to have that. That integration is hopefully becomes more part of the curriculum's going further. Back in schools, when I went to my MBA, we didn't have like a business analytics concentration. I wish we did, because I just I went for finance because that's what I knew or that was the best option at the time. Now I see like business analytics or data science concentrations everywhere. I think that's great because, if fully it really just emphasizes and a testament to IOU is getting more integrated into finance and other function so if you're a data scientist, I'll think about all how I ever going to get to finance, or if you're in finance, I'm ever going to get into data science? I mean I've I've pivoted back and forth from finance to it, back to finance, not going to data science roll within finance. It's I think it's open a blood again or 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 generating sites really quickly, and traditional tools that I grew up with through my career are not going to cut it. So if you could probably get some way there, but if you want to really have a competitive advantage and be really scalier operations, matter what function you're in, you're going to need data signs. That's awesome. Finally, Brian, before we up up, do you have any final words before we end today's episode? Yeah, I would say make it a point to the speak to your finance partners out there. You know we're wor 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. Go to coaching the numbers or we're kind of another another piece that it's like there's always that question when you bring a proposal or an investment, is like hey to finance, look at that. It just it helps you. It helps you in your day to day to helps you learn more from them and they'll learn from you as well. 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 of science is spect scientists especially. I know what your finance people more go off a lunch if you're working virtually stuff, a quick connection with them.

And finally, for me, I mean get involved 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 really enjoyed just just getting to know you and just getting more integrated 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,.

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