How Data Science Drives Value for Finance Teams

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.