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How Data Science Drives Value for Finance Teams

Adel Nehme,
June 27, 2022 min read
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Brian Richardi talks about his experience as a data science leader transitioning from Finance. He provides insights into utilizing collaboration and effective communication to drive value while leading the data science finance function at Stryker.

Adel Nehme, the host of DataFramed, the DataCamp podcast, recently interviewed Brian Richardi the Head of Finance Data Science and Analytics at Stryker.

Introducing Brian Richardi

Adel N: Brian it’s great to have you on the show.

Brian Richardi: Yeah, thanks for having me. I am excited to be here!

Adel N: 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 some background about yourself and what got you to where you are today?

Brian Richardi: Yeah, sure. So, I guess it started with my undergrad, which was in economics. I had a minor in music, and I started my career pretty much right out of school at Domino Sugar. And that was still, when I was based in New Jersey. Two key things happened, and I was very fortunate, early in my career that shaped my progression and 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 a hundred percent on SAP.

So that exposed me to many SAP modules and tools- all about analytics. And we had a really good and 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 in there, a lot of late 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 working with people who took me under their wing to help guide my career. And I always had a foundation in finance, but I was fortunate that 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 an entire company perspective- data analytics was 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, starting 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.

Evolution of Data Science in Finance

Adel N: 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, you experienced 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 the Finance function.

The prevalence of this volume and richness of data is the standard for many finance teams today. So I'd love it if we could 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?

Brian Richardi: Yeah, it's a good observation. It's true. I mean, finance, you interact with data across many domains, right? From customer to product to supplier, 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, what's really 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 they are. And usually I can speak from experience in my career. I've 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, 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 ends up with you. So where there's gaps 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 backend I'm seeing, you know, that maybe the margin or where something isn't matching up to something else I had.

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 consolidate a lot in Excel back. 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 and you can have a great experience and skill with SQL, with Python and any kind of coding or data science practices, but what's really powerful and really critical is that the domain expertise and that was something, you know, I think in finance, you pick up just organically through, through your day-to-day. 

So understanding what the data means, but also how it's used and then getting an understanding of 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. 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 and 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, is it 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. I find it being integrated more and more now, as people are trying to, maybe improve their data or improve processes around running analytics. There's a lot of projects that get spun up, the kind of lead to those results or those kinds of new tools.

Cases where Data Science was run by Finance

Adel N: 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.

Brian Richardi: Yeah, I think, the analytics side of finance is less understood and it's becoming more and more prevalent. I think. When I came out of school and started working in finance, there was still a heavy focus on traditional accounting practices, right? And, I was fortunate to get- I had great leaders and coaches who were extremely analytical.

The person I worked for was the first person who started or was part of the Sales Analytics team, by Domino Sugar. And that was 10 years before I even got there. So I feel like they were really setting the trend and ahead of the curve. There's always something like 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, 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 the entire business.

So I had to go to every division leader. Ask them how they look 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 can look at all of our customers through different slices and all the products. So had a ton of different ways we can combine and look at data. So it really through that, 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 cruel, you know, I'm out of school couple, really couple of years. And a lot of folks that I was working with had been working in the sugar industry longer than I've been alive. And they're asking, Hey, what do you think about how should I communicate this to the CEO or to the board? What, 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, 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 the other team as well. And we, worked together with our strengths and, with that, we just led to projects, a lot of project management - “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. We're through a lot of acquisitions and we couldn't afford add a ton of head count. So we had to figure out ways to do what we were doing with. And really what helped us 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 meet 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. Like I said, every company is different, but under the surface really isn't apparent for someone from the outside, looking in.

Adel N: 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.

How do Finance teams add value through Data?

Adel N: So this holistic perspective and view of the business, I think, is what sets finance apart from other teams, and highlights similarities that the data team. How do you think that vantage point helps finance teams today take advantage of the company's data and provide value for the organization? For the other teams that is, akin to the data teams.

Brian Richardi: Yeah, I think finance folks are able to connect the dots. And I think with the 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 awhile, 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 you know how it's being used and the system that's coming from. For example, to go in there and want to 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 add to add, build something that's usable for the business. Right. And that's what it's all about.

We don't want to build it. And then it sits on a shelf and it dies on the vine. So 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 kinds of bridges built. What I used to do a lot…I still do is bring those functions together all at once. So I'll bring it marketing R&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, is just going to know by themselves.

The Role of Collaboration in Data

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

Brian Richardi: Yeah. Early in my career. So on, that was part of the group I was in, was that the businesses, our customer, right. So we treated them of course, like they're our partners. And we were part of the stake and we were all on the same team, but they were really our customers. So whatever we deliver to them from, a tool for reporting and analysis down to a piece of advice, helped them do their jobs better, which in turn, help the company.

So I think I've carried that through to data science and. You know, the is our customer and things start as projects, right? We're typically doing a proof of concept. Or even in my early days, it was like, Hey, can we do this right? Is this going to work ultimately, then you're delivering it, 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. To run to not fail or air out. There are a lot of that, that data comes out in the same way every time, those are costs of entry.

And then that it's meaningful and is 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? As the technology improves or the business changes, you gotta be sure you're reflecting. And your modeling and then your tools. So all those things are really important to ensure that, you know, you're delivering something to the business that's usable. Hopefully, 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, in my opinion should be part of your roadmap and ultimately lead you to something that ultimately become a product, that can be scaled across the business. 

Because even early in my career, a lot of the things we were doing, we couldn't afford to do things, in a nuanced fashion, in a fragmented fashion across the business. So 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 we'll product standpoint from a customer standpoint. And that kept everybody on the same page. Avoid unnecessary conversations. And you spend 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 to get to the point where you're trying to solve a problem.

Improvements in Analysing Data

Adel N: 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 do before?

Brian Richardi: Early in my career, we did not have the cloud. 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? Cause we had this BI suite with like drag and drop, it was very slick.

Everybody loved it and you'd run into 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 and where people are doing this and printing things out and then replicating that in the source system logging in. 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 pull queries where you can get all that. But then they would bomb out because 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.

So that was just the game changer. 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 our self-service reporting because people 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 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, a certain line items in an order, you were doing all your analytics to one spot and it was great. Cause it took stress off of this. Yeah, that was huge.

Transitioning from a Finance Leader to Data Leader

Adel N: That's awesome. And segwaying 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 organized.

Brian Richardi: Yeah, you touched on it. The executive of support is really important and I've been fortunate to have that throughout my career. And even early on, I know I learned how to navigate our BI tools. And where all the data was from our CEO had a finance background, and I remember him showing me, Hey, Brian, here's how you analyze sales. Here's what I do. And here's all the tricks I have. So that, that was really important. And, um, you know, I'm 24, 25. I figured everybody knew how to do that. I grew up 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. 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-- talents hard to find, especially data scientists. The last thing you want them doing 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 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 quickly and in finance, you have a view of everything going on.

Like I talked about, people are coming to you with lots of challenges and you have visibility to things that you find on your own, because it is a heavy collaborative function, and we're embedded in finance. So, from everything from sales forecasting to looking at all items in the P and L to the balance sheet, cashflow, we're there.

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 going to be, or it's pretty common for what I'm seeing in finance. It's all, it all, just a matter of, having that data ready to go and having the people and the talent that can do it.

How to upskill finance teams? 

Adel N: 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 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 upscaling for finance teams and the value that you can have from a finance department, once you equip it with tools that go beyond Excel

Brian Richardi: 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 an Excel. And then it got up, I think, to like over 40,000. And that was really detailed information. And 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 counter.

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 SQL is the future, right? And it's becoming more integrated in finance where you have a limited capacity, right.

To store data and the speed in which you can retrieve it. I think it's, it's really going to replace Excel. And I see it already happening. I'm like Excel's a requirement and every JD for a finance role, I think you're going to see SQL there more and more. and I think the data visualization tools as well, like when I learned to do BI, I had to go to like the SAP school for in total.

I think I went for almost two months altogether and it was really hard to access the data, the cubes, those DSOA data databases. Pull it in, like you needed a really, there's a large learning curve. And I was really the only one that 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 and get one of those tools. I think you can get them even for free where there's a ton out there. Start doing that 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 in a large company, there's always somebody, especially the likelihood that's doing this for fun and learning it. I always find somebody who's the, who knows SQL or who knows Python. And I just kind of added 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.

Adel N: 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

Brian Richardi: Yeah, I think, collaboration's huge because you only, you only know. what you know, and 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 and add value.

Like I have the things that I think are cool and fun, but again, it's gotta be useful for the business and translate to business results. It's gotta 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 there'll be an expertise.

I know I keep saying it, but it's really not something to take for granted. It doesn't happen overnight. I I've been on teams with it and without it, and I see how quickly you could move to it. 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 at which you could 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 call me you to help them build a solution.

If you do that in house, and you could 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 it. It 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 with a project that you can make it low risk, real effort to get something that's really valuable. And then it'll help you build strong momentum.

Adel N: I couldn't agree more, especially when it comes to subject matter expertise. It's relatively easy to upskill 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 datasets. This is where supplementing that subject matter expertise with technical skills really drives the potential for a finance team.

Brian Richardi: Yeah, it's a great point. I 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 one. And you have a knack for things like you said, like Python and SQL coding, you just took this a matter of just getting into great in your business and vice versa.

If you're a very well-versed in your business or a function start learning Python, start writing SQL. It's there. There's so many resources out there that you probably don't know more than me, that can help you upscale. Right. And you can do it in your own time. For example, 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 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 up-skilling 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 asked them, it's like, Hey, how can I get started? The sooner you can start practicing and applying it to.

Setting up upskilling programs for Data and Subject Matter Experts

Adel N: 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.

Brian Richardi: Yeah, I think early on, we would just do my career's 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 practice. Right. It didn't need me there, or my team there with them all the way through to side-by-side so they could do it on their own.

we were recorded videos for people, things does it. 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 and would be there to help them. And then they would trade each other as do people enjoy in which, which was great. I think for me, what's been helpful currently is just, they bringing the community together. Your data science community, or even beyond that in your data lakes community through events, especially with COVID doing things in person has been great and people sharing what they're working on.

Adel N: Especially for finance teams, looking to learn technical data tools.

Brian Richardi: Yeah, absolutely. I think inherently you had that analytical muscle you're exercising every day And you're getting a perspective on every area of the business. you're balancing, a very, I want to crawl 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've been through a rotation where you worked with someone in marketing as a, as their finance partner, you've worked with someone in RMT, or you've worked with someone maybe on the manufacturing side, and he's 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 where the evolution of the data science function is really powerful.

And if you pair that with people with straight. Who caught from a different background where they were more in the code and coding 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. I'm the person who has all 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.

You know what you have today? No. What you have normally you want to go and what skills you need. And the difference is what you go out and hire, 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, I adjust 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 might, well, this is what I presented like a month ago, but things have changed. This is their 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 ops experience or someone with more data visualization experience. Because what the business is asking us to do is more of that. I don't have the capacity. I don't have someone who has that skillset. And then we need to get it quick or if we want to wait to learn it ourselves, we can do that. But usually you don't want to wait. when, when the business really needs it, you gotta find a way to make it.

Adel N: 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 cops within your team.

Brian Richardi: Yeah, I think it's really important to have a roadmap and show what you want to do. I think what, 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 to me. 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 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 going to want to be  work?

How are you adding maybe increasing revenue or we're driving a cost avoidance or a cost savings, or is there something very focused on cash cashflow 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 got to look at the benefit as well. Right. And if you can, if you look at it less as a, like a cost of 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 I found is really, really helpful is be upfront on kind of your process, how you've been, 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 throughout my career where 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 in front of your team. being up front with it, bringing your data governance team, your process governance team, and just call out the risk. Right. Try to score it if you can, to try to measure it.

And then everyone will be thankful, right? It's like, oh, I didn't know. This was here. And analytics was great to solve problems, but it also helps you find problems to. 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 will 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. so those are the 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.

Things to look forward to in the future

Adel N: 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, where you rethink, what do you think are some of the most exciting trends you see in data science that were really affect the finance team's workflow?

Brian Richardi: Yeah, I think it's going to be integrated. It's going to be one of the same. Yeah. It's going to be A data science capability embedded in finance and 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 in those. 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, the roadmap and the projects to that. I think, you're going to have that, that integration is hopefully becomes more part of the curriculums going further back.

And in schools, when I went to my MBA, we didn't have like a business analyst. concentration. I wish we did because I just, I went for finance because that's what I do, or 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, it really just emphasizes and a testament to how it is getting more integrated into finance and other functions.

So 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, not how to do the science, role within finance, because I think it's all been a blend 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 generate insights really quickly. And traditional tools that I, I grew up with through my career are not going to cut it. So you could probably get some way there, but if you wouldn't want to have a competitive advantage and B really scale your operations, no matter what function you're in, you're going to need data science in there.

Call to Action

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

Brian Richardi: Yeah. I would say, make it a point to speak to your finance partners out there. And more about what they're doing and, and, and build that strategic partnership. I've found everybody loves having a finance person on their side, or at least in their corner, helping them out, crunching the numbers, or we're kinda another, another piece of it's like, 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 day-to-day and it helps, you learn more from them and they'll learn from you as well. They'll, there'll be better at their jobs too. I think that's, that's definitely a call to action. And if you were data scientists, scientists, especially, hanging out with your finance people more, go off 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 with the data science community. I love doing these things and hope to do more of them.

Adel N:That's great. Thank you so much, Brian, for coming on the podcast.