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and now onto the show. From Data Reels, this is FPNA today.
Hello everyone, welcome to FPNA today. I am your host Paul Barnhurst, aka the FPNA guy. FPNA
today is brought to you by Data Reels, the financial planning and analysis platform for Excel users.
Every week we welcome a leader from the world of financial planning and analysis.
Today we are delighted to welcome to the show Cat or Cove. Cat, welcome to the show.
Thanks so much Paul, really excited to be here. I excited to get to chat with you today about
everything. Yeah, I'll be fine. I'm excited as well. So let me give you a little bit of background,
and in a few minutes here we'll give you an opportunity to share a little bit more about yourself.
So she comes to us from Boulder, Colorado. She's currently the CEO at Verredo. She earned
a bachelor's in Masters in Mathematics and started on a PhD. She worked for Facebook in the Data
Science team, she worked for Iron Cloud and Product, and as mentioned earlier she's the CEO at Verredo
today. So I'm going to ask you a question about budgeting and forecasting. We like to start with
this one with everybody. Tell me about the most challenging or worst budget forecast you've ever
been a part of. Yeah, that one, that one's an easy one. So when I worked at Facebook, I started in
Data Science, but then later I was in FPNA and we had all of our different processes, all kinds of
daily, weekly, monthly quarterly processes, like any FPNA team. The hardest thing by far though
was actually not the annual planning cycle. It was the lawn-range planning cycle, which we would do
at a different time of the year. So we never really got a break. And lawn-range planning at Facebook
meant planning for the next five years for a business that was growing very, very quickly at a
very massive scale. And we had to plan everything from how users would grow, but internationally,
like different in the US, whereas maybe things were more mature as compared with India, or things
were growing really quickly. Based on that, we would have to plan out data infrastructures and where
we would physically build massive capital infrastructure. And then also revenue and advertisers and
advertising trends and putting all of that together into a lawn-range plan that then over 100 people
would give inputs into. And many, many, many cycles of approvals, it was a very, very, very massive
undertaking. So definitely the hardest thing I've ever been a part of, we would breathe a big
sigh of relief every year when that process was done. I could imagine how long did that typically
take you? Yeah, well that's kind of the, that was the hard part is we had to put all of this together.
And I'd say like in two to three months get to a very solid outcome because then we did also have
the annual budgeting process and like monthly rolling forecast. So we couldn't afford to have this
take any more time and yet it required so much work from so many people. So it was, it was one of those
like sleeping in the office type of situations. Never fun. So what was maybe the key takeaway or
learning experience from going through that? Well, so one, one big thing that I really saw was
I needed to get inputs from a lot of different people. And this was a very diverse set of people,
you know, some, some folks like the user forecast would come from the Facebook growth team and they
would produce it running all kinds of different machine learning models. They would produce it. They
would give us a query that we could run to pull the latest numbers. And so that was fine. We could
always rerun the query or maybe ask them if we needed a longer forecast or whatever it was. But
then other inputs came from different teams that would give it to us in different forms. It could
be things coming out of Salesforce. It could be people sending us different types of spreadsheets with
various projections that they came up with. It could be people giving us numbers over Facebook
messenger or over email. And, and then every time any one of these numbers changed and I had to
go back to five different people and say, well, this person's numbers changed. So how do your
numbers need to change? So much of my job wasn't even about forecasting per se as it was about data
gathering and, and kind of getting everyone on the same page and doing that over and over and over
as we iterated throughout the planning and forecasting process. So that was like a big aha moment
for me of it's not just myself doing data science forecasting and it's not just about the technicality.
So much of it is about the people in the process. I 100% agree. I mean, there's so much that's about
coordination and collaboration and streamlining process. I mean, the gathering process
can be a nightmare when you have a lot of people. And how do you make sure you do that in a way
that's efficient? Especially when a lot of companies are doing it offline or you're doing it via
email and maybe some spreadsheets and you have it all over the place. It can be a real challenge
to consolidate it all. I can remember some late nights when you try to consolidate all the numbers
don't tie. Just like, okay, are they ever going to tie when you plug there? What are we doing here?
Oh, I have so many stories of that like realizing, you know, realizing at the most critical
juncture when you're in a massive board room that there's one number out of a thousand in your
deck that might not perfectly tie and just having that nightmare of a moment in your head when you
realize that that happened. I'd actually be curious, Paul, like if there's a quick story you want
to share about your nightmare moment or numbers don't tie, I always love hearing those.
Oh, I mean, there's plenty of them. I think my worst nightmare in a moment and not so much
numbers tying, but so we've done this big plan and when I worked, I worked in business travel and
so we had a ton of different countries. We had partner countries and we had to load everything.
And one year I loaded the country of Poland in the complete wrong currency. Yeah, and I was like,
oh, no, I loaded it in US dollars instead of their local currency. The good news was they were
relatively small countries. So it wasn't a big impact globally, but for them and we'd already
closed things. So we couldn't correct it to them. It was a huge deal, right? Their numbers were
completely wrong and it's more than nightmare of that because I didn't I didn't catch it till it was
too late and have to be like, yeah, yeah, poems are wrong. I have to fix it at the next forecast.
They're just going to have to live with those numbers and so you know, their variances were a total mess.
So it's probably the worst as far as just having a major issue with numbers. The other one that
always stuck out to me is I worked for a business one time. We were way ahead of budget. We'd put
in a very conservative budget deliberately because I didn't hit the numbers like five years.
And so of course, the leadership decided we're going to give you more aggressive numbers for the
second half of the year. And so they went through and we get our budget for us and then asked
us to report to them. The numbers made no sense because I had built everything driver base. They
just used some top down approach. I literally a couple times said in the comments, I can't tell you
what the variance is because I have no idea how you came up with the budget number and I'm sure
they love that go to the deck up to court. They never got mad at me, but I'm sure there are a few
times they just shook their head like, come on, you got to give us better than that. So that was
a probably the two that stuck out to me. Love that. Thank you. Yeah, it was fun. Not at the time,
it's fun looking back now. You can kind of laugh about it. I'm sure you had those with Facebook
too. We're now you look back and laugh, but at the time you're just like, okay, can I take another
day of this? Absolutely. I think we've all been there. And that's why it's so great to hear these
stories and realize you're not alone. Everybody that's ever worked in FPNA has seen all of these
things like they understand it. And the exciting part for me is being in a position now where I
every day I'm trying to improve this situation and I'm trying to see how can we automate, how can we
get to a world where these types of errors get minimized and that's been really fulfilling.
I bet yeah, it's rewarding for what I do too of making a different staff PNA, helping people be
better at their jobs. So yeah, I want to move on here to our next question. Just give our audience
an opportunity to get to know a little bit more about your background. So maybe can you just tell us
a little bit about yourself and how you ended up where you're at today? So a little bit of that back
story. Yeah, I have a very, maybe unusual back story. Very, I guess, few people have followed the
exact career path that I have, but lots to learn from there. I'll start with, I always thought I'd
be a math professor. That's why I have three different degrees in math and I was very far along with
my PhD at Yale and I was teaching multivariable calculus to undergrads and really very far along on
that path and thought that that was what I wanted to do. But then I started feeling like something
was missing. It wasn't any burning flag. It wasn't something very clearly going wrong. It just
felt like the academic research felt a little unfulfilling and I wasn't sure what
else to do because this was the only career path I'd ever really considered all my life at that
point. So I did a little bit of exploration. I delved a little bit into the world of hedge funds
and high frequency trading decided, you know, I don't know culturally that that's where I want to go.
A friend of mine was like me and X-Math PhD. He was at Facebook and he said, hey, Kat, like I'm having
such a great time over here. Billions of rose of data. There's this new thing called data science.
And at the time it was new. And this was before all the explosion of data science and boot camps
and everything. Facebook only had 10 people, data science, data engineering, data analytics.
They were all in one and Facebook had only 10 people at the time doing anything at all relating
to their ads, revenue, monetization, data. It was so small. We all fit onto one little floor.
And so he said, I'm having a ton of fun out here. You should come out and do this. I, you know,
I bet they'd hire you. And so I applied, got a job there. I thought it would be for a year.
And then I'd go back to my academic world. But after a year, I was managing a team of data scientists,
Facebook was growing, everything. And I just figured this is going to be, if I go back and finish
my PhD, you know, this is just the biggest opportunity cost to like this is here and now is so much
more fun and so much more fulfilling. So I stayed after about two and a half years. I was tapped by
the now CFO season Lee to come join her team on finance and build out this data science FPNA team.
And I could talk about that a bit later. So that was a very unusual thing. I will say my manager
at the time told me I was crazy for going from data science and product into finance.
Most people want to go the opposite direction. But I thought that our FPNA team at Facebook was
incredibly strong, figured like a lot of them had MBAs, a lot of them had investment banking
backgrounds, consulting backgrounds. I thought I'd be able to learn a lot. And I did. I think about
that as one of the best professional experiences I've had. So I saw it as let me go, let me build this
team, let me have a lot of impact, work with the executives of this big public company. And then I can
always go back to product, which is what I did. I then built out product teams at Facebook was
ultimately ahead of product that a growing startup called Ironclad. That's now a $3 billion
company. Hopefully we'll IPO soon. And then I started burrito. So it's a very kind of unusual
path of building five teams from the ground up at Facebook, building functions from the ground
up at Ironclad and now building a company from the ground up at burrito. But that mix of data science
and finance and product like that's always kind of been there. And now I just get to do all three
of those every single day. Yeah, two kind of fun things with that. Ron, I remember having the
conversation with he was my CFO for a while and then he was the general manager and I was his
support. So I don't remember which role he was in at the time. I think it was the general manager
of the business. But one day I mentioned I want to go over to product and he looked at me as
like there's not a chance you're going to product because it's just a we had a real challenging
FPDA department. He didn't say exactly those words, but that was basically idea and I was like,
man, because I thought it'd been fun to kind of experience the product side of the business.
So I can relate to that one and start up environment is always an interesting environment.
It's very fast moving. Sounds like you have a lot of great experience there doing that. So that's
kind of my product experience. But I'm curious going back, you mentioned you always knew you wanted
to do math. So when did you know? Like did you always just love numbers or what told you that
mathematics and teaching was what you thought you always wanted to do? I think this is where we
sometimes believe we have more more choice or we've made choices ourselves that are actually so
impacted by the environment around us. My husband's a lawyer and his dad was a lawyer and he never
considered going to academia. And in my case, my parents were both kind of the you know, masters,
PhD like math, sciencey types like white lab coat, like literal little scientist types. And I mean
no one forced me to go into the career that I did to go into academia. But in retrospect, I think
that's just what I was seeing around me. And that's just what I thought, well, this, you know,
this seems good. This seems like seems like a good profession. But while I love, I do love math.
And I spent many years doing it. What I realized I love even more is, and part of that you get
in math, part of it you don't is just problem solving that actually then has an impact on the world.
Because the thing is when you fall in love with mathematics, like calculus, multi variable calculus,
it's centuries old, then you get into number theory and topology and algebra in grad school. And
you know, that's still like centuries or at least decades old. And by the time you're doing any
kind of real mathematical research, it's so theoretical and it's so specific that very rarely
can it have real world impact. And I think that's what I was missing. And as soon as I came to Facebook,
every week I could go work with some engineers to say, hey, can we log ABC things? Because then I'm
going to start building all kinds of analysis and dashboards and whatever on top of it. And then
based on that, I would have recommendations of like we should build this feature or we should work
with this partner, but not that partner or maybe we need to change our pricing or and I would see
this immediate impact not only on the business, but on the people around me. I did a thing and they
were happy and they were like, great, like we're going to make our money now or our team is doing well.
And I really wasn't seeing that in academia. So it was like that was that problem solving, but
there has to ultimately be an impact that you're able to see. Like that was the thing that I needed.
Makes sense. You wanted to be a see that direct impact and be able to experience that. And you
just it's different in academic. I'm sure you could see it's some with students sometimes and
things, but it's not the same, especially on the problem solving side, right? Like you said,
you mentioned, I'm sure the research that's going on today is very theoretical because math has
been around for thousands of years. How many things are you going to really be studying that aren't
theoretical at this point that are unique? You know, one of the other things I know you do is I know
you're a general partner for dark mode ventures and you invest in startups in addition to having your
own startup. So what do you look for in a company? And when do you recommend these companies start
thinking about finance and FPNA and you know, those type of things for their companies?
Oh, yeah, I love this question. So I should mention that I started investing as a way to prepare for
being a founder. I spent 10 years actually preparing to be a founder and that was part of why I joined
finance, part of why I did all the well, did all the many things I did in my career to get more
exposure. But I also started angel investing and working with startups, even while I was still a meta
at a big company, as a way to learn more about them and to learn about fundraising and how they operate
and how they hire and how you really get to product market fit. And I just like I saw this is a big
learning opportunity. And so now when I look at founders after years of having done angel investing,
having then been a Sequoia Scout, having one fund, now having a second fund, obviously what I
look for has matured quite a bit. I think the thing that's always been the same throughout is
a big emphasis on the people. And everyone will say this for early stage investing, you know,
that the founders matter a lot, ideas important, but who the people are matters. But I guess the way
that we look at founders and founder founder competencies and what does someone actually need,
not just in terms of can they do it, but motivation, what is going to make them want to do it for years,
like get up every day and like work hard and because it can be really hard, these are kind of things
that we think a lot about now, almost like an interview process for founders. In terms of finance
and FPNA for companies, this is maybe a bit of a controversial view. I know there's a lot of
people out there kind of saying, how do you build and all in one so that even startups that
they're earliest days just have like a system that can do finance for them and maybe help them plan
at earlier stages of the company. I actually think there's something to be said for having people
do something manually or in a very hands-on way, especially when things are changing quickly,
to really understand what are the drivers of the business, what really matters and what doesn't.
And so for any startup that's less than let's say 50 people or less than series B,
doesn't really have a not just a growth stage kind of scalable motion, but really a very
repeatable motion where it makes sense to have bottoms of planning and things like that. I personally
say like use spreadsheets, but focus more on what matters for the business and like nail that.
Don't get too bogged down and the BBAs where you're operating on such a little small revenue,
that it doesn't really matter. Focus on the bigger picture of like what's going to drive faster
revenue growth. There's a lot of truth to that and yeah, I have a lot of people talk to me and it's
like, hey, well when do I get a tool? And I'm like, when the complexity warrants it, you'll know
as usually what I'll say, right? We've all experienced that. And that can be at different points for
everybody. But there is some value, like you said, focusing on the strategic and the big picture
versus thinking, I need a tool from day one. Are there some places where it makes sense? Sure,
there are some great tools out there that you can use. Just in our sponsor focus on the small
and medium market. And I encourage people when you're ready to go, but it's hard to be to
spreadsheet, especially those very early days from a cost standpoint and other things. So you just
have to think about when does it make sense? Data Rails is actually a great example of people are
still on spreadsheets, but they've got the benefit, like some incremental kind of benefits on top of
that with maybe pulling data in and out. And I think that could be really great for small
medium businesses. So I don't want to imply that there's no value at all in any kind of software
if you're a small company. But we're just like, you still have to ultimately own, I think,
the strategic plan in yourself. You can't outsource that to a tool early on.
And that makes a lot of sense. There's the strategic part of the process. And I agree with you.
Data Rails has a great, you know, great platform and there's definitely opportunities out there.
But like you said, it's the strategic part and the learning that takes place in those early days.
So that makes a lot of sense. So I'm curious, you mentioned one other thing. I think you said you
are on your second fund. Is that what you said for your dark mode ventures? Yeah, correct. We recently
announced fun too. Exciting. So what's your favorite part of investing in startups? What's
kept you doing that even as a CEO? Like, what do you love about that? Yeah, that's, it's interesting
because I definitely thought that when I became a startup founder, I would stop investing. The
whole point of investing was to learn and to prepare. And now that I was a founder, I should just
focus on that. Right. So I intended to stop. But by that point, I had very good deal flow. I had
worked with enough startups that they recommended me to other newer startups. And so even without me
doing any outbound work, inbound, great deals were kind of coming to me and people just saying,
hey, I'm starting a startup. Do you want to invest? Do you want to help me? And so I thought,
okay, well, let me just do that. Only the inbound side of it, like, let me really limit my time.
And then kind of around the same time, Sequoia and others, funds that I'd been sending some companies
to and helping facilitate a lead for a seed round, let's say, we're just saying, you know, you're
sending me great companies. Do you want to work more formally with us? Do you want to be a scout
and invest our money? And then, you know, like, kind of have a more formal relationship. So
I decided to do that. And I was a Sequoia scout for a couple of years. And again, really tried to,
they do a very good job trying to offer you optionality, but without the commitment of having to
make investments. So that was a good way for me to feel things out. Ultimately, though, I ended up
doing the fund and sticking with it because I realized it did make me a better founder. And while
there is a bit of a tradeoff of time, it's the same reason why so many, let's finance leaders and
others take board roles in other companies. It makes you better as a CFO or as a VP finance.
It makes you better at your job. If you have to look at other companies from a 360 view,
where you don't have the same type of day to day, you know, nitty-gritty, and you're forced to
understand how product and go to market and how all the things, all the dots connect together.
I've just seen on the same way the investing perspective gives me that and actually has enabled
me to do a better job with Beretta. And frankly, I just enjoy it. So I love working with founders.
It's been a real privilege. I appreciate you sharing that and it does sound like it's obvious
that you love it. I can tell the passion there. You know what it is like? 13 different spreadsheets
emailed out to 23 different budget holders. Multiple iterations, version control, errors,
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I'm going to switch gears a little bit and ask a question. We're getting back to FPNA. So you've
worked in product a good part of your career. You've been head of product at a company. How do you see
product and FPNA working together and any advice you can offer on how FPNA can better support
product from your experiences? Yes, I especially saw this at Iron Cloud where I had the PM function
and also the beginnings of our analytics function for the entire company. And we also had our
first ever finance lead join around the same time. That person didn't report to me. I think
reported to the CEO directly maybe at that point. But it was just so early where you only had a
few PMs and analysts and a single finance person. And that was when we put together the first ever
infrastructure for our company of how do the dots connect? And for example, we realized with some
very basic analysis that sales was selling all use cases equally. And meanwhile customer success
was spending 10x the time on serving the procurement use case, the serving a sales use case.
And that doesn't really make sense. Maybe you need to then charge more for the procurement use case
or figure something else out. But it was just this like misconnection or a lost opportunity. Let's
call it between what sales was doing and what customer success was doing. And we only found this
out when we actually did the analysis. The reason I'm talking about this is analysis is because I
think often the finance team is on one hand, they're sort of seen as like, well, you're part of
GNA. It was like finance and HR and legal and ops and you all sit over there. And then there's all
these other teams that actually build the product and like sell the product and make the business go.
But actually, especially when I was an FPNA myself, what I really saw was finance thinks about
the entire business. They have to understand so well what is going on with product and with go to
market and how all of those different segmentations are looking and connecting the dots as a whole.
And so in that sense, like the analytics team that we formed at Iron Cloud was, it wasn't a finance
team per se. But we were doing much of what a finance function might otherwise do. Just in that
sense of like, let's look at our business at different levels. Let's understand where there are
gaps or opportunities. Let's surface those recommendations up to the executive team and let's,
you know, let's let's take action on it. So I just really saw that that kind of interplay between
finance, product, go to market, really just connecting those dots was was absolutely critical there.
Thank you. And I appreciate that. There's something you said there that I've always really liked
is finance in particular FPNA is one of the only functions outside of the CEO, the office of the
CEO, the CFO has a 360 view of the business, right? Especially in a smart business where you're working
with everybody. And so there's opportunities to bring things up that others don't notice because
sales is focused on the sales engine and marketing is focused on the marketing engine and product on
the product. Whereas finance can bring that analysis and those opportunities across all of it.
I think we're seeing more and more of that today, especially since COVID where people are realizing
that FPNA and finance can play a much more strategic role than historically. Like you think back
office, right? They're just you go over there, you don't make money, you just kind of do your job,
you're a cost center. And I think that we're definitely seeing that change. I like how you said that.
So I'm curious, you spend a few years at Facebook, you mentioned a little bit about that story,
but can you go into details? Why did they create it? You know, a finance team that had data science
skills. What led to that? What was kind of the goal of your team and how did that come about?
Yeah, I mean, huge credit there too, Susan Lee, who's now the CFO and is just absolutely
outstanding. I've had many great managers over the years. I've been very, very fortunate to learn
from so many incredible advisors, but Susan is just she is special. She's in a category all in her
own, just someone that I really look up to and aspire to. And she had this insight like she made
this happen. She saw that on FPNA at meta. And this is like, you know, like meta was already just
gone public, let's say was growing a successful business, making a lot of money. But despite all
the wonderful people they were hiring, very smart, very hardworking people, meta could get the best
of the best, but they were still FPNA type of people. They weren't data scientists and they didn't
have any relationships with like the product teams that we were mentioning earlier. So they didn't
really know what the state of the users and like what product launches might cause users to engage
more and spend more time, which meant we could show them more ads. They just didn't have any of those
internal relationships. And so Susan saw that the FPNA team would do their best using the data
that they had to create revenue forecasts and to do various things. But then ultimately something
would happen. And kind of like again, coming back to a youth subpoil, the drivers weren't there.
And so there would be actual versus the forecast would be quite a bit off. And the entire executive
team would be worried. And for a while, this was happening on a weekly basis. There was like a
revenue war room. And it wasn't clear whether or is it that users are switching to mobile? Is it
that advertisers, you know, the brands using that Facebook or maybe doing something different?
Is it that we're just changing our own algorithms for ads or for products that are like what are
the drivers of this change? Should we be worried? Should we be taking an action? Those were
questions that were being asked on a daily basis and war rooms were happening every week. And
it was just really hard. So I think Susan saw the need to try to bridge that gap and to get someone
that did have more of that product and data science, not only a technical ability, but I had
the relationships because that's for two and a half years I've been working in the ads, data
science and product world. And so Susan kind of figured like, okay, if I bring Kat over here into
finance and we build out a team here that's focused on almost bridging the gap, maybe it's not
going to work, but at least it's something new that we should try because the thing that we've
been trying so far, it's just it hasn't been working. So she did that. She kind of came over,
asked me about this idea again. Like I mentioned, my manager thought it was a crazy idea first,
but I was really intrigued. We really talked about the role, like we talked about it being a two-year
thing where I would come in, I'd build a team, I'd do a, we ended up automating a lot of big processes,
we ended up taking forecasting from monthly to daily, making it accurate, like having it be more
driver's base, I had hired my replacement who's actually still the leader of that team today.
So that was kind of how that transition came to be. And fortunately, I think Susan, she really
saw the future, right? It worked out very well for us and we had a number of years of very good
forecasting. That's great. And whenever you see an inspirational leader that can look beyond and
kind of see where things are going, can make a huge difference. I mean, there's so much value in
that having that ability to kind of be forward thinking. But I'm curious, you know, obviously,
you guys are posting forecasting from a data science perspective different than FPNA. So was
there kind of challenges as you try to bring in more data science or how did that go about of
kind of blending the processes and improving the forecasting? Because I'm sure there are very
different methods that you were using compared to what most of those traditional FPNA people were
used to. Correct. And that's actually one of the trickiest parts is when you have folks in
data science and then you have folks in FPNA, they're living in different tools. Like as a data
scientist, my tools were Python and R and Terminal and FPNA lives in spreadsheets. And so even if
you could do a certain thing better, if now the rest of the FPNA team can't access the thing that
you did because it's a Python script, it's helpful, but there's a limit to how helpful it could be.
So establishing a good bridge where the whole workflow can operate together, but without forcing
everyone enough, PNA to learn Python and kind of vice versa, that was a big part of what we had to do.
So I did ingest higher people to write Python scripts while we did do quite a bit of that and we
built some very sophisticated machine learning models and everything. I also owned this giant
50 tab kind of revenue forecasting model that took as outputs some of the things our Python scripts
did as well as various other inputs we get from the accounting team and others. And ultimately,
that was something that the rest of FPNA could interact with because it was more in a format
that they could understand. And that was honestly to one of the gaps that I saw where I was very
fortunate. And Facebook is a special company that's printing cash and can afford to hire a data
science team within finance because let's face it, most companies that's not an option. So Facebook
was lucky to do that. But even there, everything we built internally in a lot of our internal processes,
it was never kind of completely to the level of what I thought could be done with a purpose built
tool. And in particular, that collaboration layer, like that still largely remained spreadsheets
and emails and PowerPoint decks. And that's kind of the part that I always thought, you know,
it's great that we did all this automation and forecasting stuff. But if I could also build a
collaboration layer to solve that additional set of problems with the inputs gathering and
and the approvals, like that would really that would be ideal. And that's kind of one of the
main things I've been thinking about ever since. And so I'm curious, sticking back, you mentioned
you did a lot of algorithms. I know Facebook has released an open source profit algorithm that
used for some of forecasting. Is that one of the things you were working on there? Is that part of
kind of the forecasting you were doing? I'm just curious. All of them that. Yeah, actually that team
and that model, they were working very closely with my team as well. So the way Facebook is structured
was there was the finance team, but we also had a kind of core data science team for the entire
company. And several people on that team worked really closely with us on some of the more
technical aspects and models. And yes, they ended up later taking some of that as inspiration and
building kind of external open source versions. I think one of them might have even built a startup
or around some of this. So it's a lot of really, really exciting stuff was done there. And it's just
important to separate out though. There's like the pure forecasting of imagine I have all of the data.
How do I do this technical aspects? We did a lot with seasonality, for example, and other overlays
that was very interesting, made our forecast a lot more accurate. But then there's sort of the,
well, but I just need even macro inputs. Like there's data about the future that nothing about
my historical data could possibly predict that a lot of that data lives in people's heads. And it's
maybe it's even strategic decisions about where you want to take the company. And so that how does
that data ultimately get those drivers ultimately get into your model and kind of mix in with the
technology with like the more advanced forecasting techniques. Like that was one of the very interesting
things that our team tackled kind of speaking of that lead. So question I think is along those lines.
How do you know, FPNA and data science professionals work better together? Any advice you'd offer
from your learning experience? You know, if companies are thinking, hey, I want to have, you know,
a data science person in FPNA, we're definitely seeing more of that. How do they make sure they're
kind of maximizing the benefit of their skill sets and working together to improve that forecasting
process? Any advice you'd offer there? Yeah, absolutely. I mean, in a way that's sort of what we did
where my team was kind of a mix of, I'd say, more technical and more traditional FPNA, but then we
worked with this core data science team that was very technical like passing in a set of engineering
interviews and everything. And we would also work with a BI team of more like data engineers kind
of helping build certain data pipelines for us. And working with these other kind of partner teams,
one of the things we found was it's so critical to lay out the right context because it's very
easy to just assume, especially when some of these folks are more or less full-time assigned to work
on the FPNA set of problems that they know what FPNA is all about and they understand what the
executive team cares about or, but they don't. And so sometimes like things like whenever we'd
introduce a new ad type, we'd have to make sure that that the logging for it and ultimately it
would get rolled up in a certain correct way into our actuals without context as to what is or
is an important for the type of reporting that we do at a business level. It wouldn't be obvious
to our BI team necessarily to do that and to know what to look out for or like at what which
product features or launches matter and we need to do some work and which ones don't. So the more
business context we could give the data science and BI teams, the more they could use their judgment
because otherwise where you end up is in a situation where the finance team has to know about
every single technical thing that could happen and while maybe they're not writing the code,
they have to really understand a lot of it enough to give very explicit directions which I think
kind of defeats the whole purpose. So the more business context you can give the better.
Yeah, makes a lot of sense. I would totally agree the better you have that business context so
you can kind of work together. So I appreciate that. I think that's really good advice.
One other question I have is I occasionally get people approaching me and for a while I get
out of people think I need to go into data science. Right? Like I need a data science degree even if
I'm working in FPNA, everything's going toward the data science. What's your thoughts on that?
You know, I think it's for anyone that's really ambitious and especially if you want to career
honestly in almost anything but that's going to be an a more strategic role, you don't need to
know detailed level coding but I think you need to at least understand what coding can do or what
data science generally is or what it can do enough so to potentially be able to hire people to do
that or to know what kind of context to give them. So it's what I would say is very tactically I would
recommend taking one or two intro courses to data science, you know, just just like get comfortable
with the basics of manipulating data, doing some basic even like linear regression, some kind of
basic forecasting. It doesn't have to be anything crazy. I don't think you need to go in and learn
all the depths of machine learning or generative AI but enough just that you understand the concepts
and again what matters and what doesn't. And then where where I kind of see things going especially
with just more and more things being automated and more kind of more applications of AI being used
is even in software engineering we're sort of seeing this that software engineers right now might
need to write all kinds of code to produce let's say a dark mode button like if you want your website
have dark mode light mode, so all kinds of like code that an engineer would have written historically.
But nowadays you can actually ask chat GPT to write this. It may not be performance optimized. I'm
not suggesting we all go out and do this but the point is often if you can say nowadays I want to
write a script that does X it's becoming increasingly easier to do that. Same thing in data science,
same thing in software engineering, anything you can imagine. So what matters is almost the
the architecture of knowing I need a script that does this, I need a script that does that.
And then how do I put those two scripts together? Because in the end you can't quite ask chat GPT
to write you like the whole web application. It's not there yet. It's more on a micro level.
You know, funny enough, so I when I did grad school, you know, data science really wasn't a thing yet.
Yeah, I mean, yeah, at early stages of it. And so I didn't MBA in a master of science and
information management. And you know, I remember looking back, it was five, ten years after I had
graduated, seeing they had an analytics program, you know, data analytics. I'm like, man, I wish I
could have done that because I would have been more than what I wanted, but it just wasn't there yet
in the university. It could really cropped up about a couple years after I left. They really started
to become bigger. And so I can appreciate wanting to be able to at least understand it. You don't
got to go deep. I just said, you don't need to be a coder and spend all your time in Python
by having an appreciation can really help is kind of what I was hearing there. Exactly.
And you mentioned a little bit about AI. And so how do you see AI changing the role of FPNA moving
forward? And what should FPNA professionals do today to be prepared to capitalize on AI? Because
it's not going away, right? We all know it's here to stay. It's just a question of what does it
look like in the future? So I know you presented at AFP on this subject and we just love, you know,
your thoughts for a minute here on that. Yeah, a lot of people are asking that and not just in
finance, but across every function and in marketing and sales. What is going to be automated? Whether
that's AI specific or just kind of more general automation? And what are people still going to be
doing in five years, 10 years? And to me, it really comes down to again, that kind of like the
the business strategy, the business logic, figuring out what to do is always going to be the job
of humans. They're the ones that make decisions. And a lot of the decisions we make aren't
pure optimization. Like you look at companies and there are many successful types of businesses
out there. There are some that say we're limiting ourselves to 50 employees. We will never grow above
that, but we want to see how much revenue we can get, just 50 employees. There are others that make
a different decision to grow as quickly as possible, even though, you know, revenue per head count is
not going to be as good. Like it's not black or white. Humans make choices. Humans drive the
visions for their businesses. And I think in a very similar way with FPNA, humans are the ones that
work with business stakeholders to make these types of decisions and to say, well, here are our sales
targets. Marketing, do we feel comfortable or not with you doing this big event this year,
given this budget? But where I do think AI and automation really steps in is in a lot more of
those more granular pieces. Kind of the all the like little scenarios, all the little, well,
if marketing does or doesn't do this conference, what impact does that have on expenses, what impact will
I have on sales, given reduced downstream leads if we don't do the conference? Just all those things
where FPNA would previously have said, well, let me get back to you. And then three days after a
meeting would have come back with a spreadsheet and analysis. All of those things will become in the
moment like, let's just run a scenario. Let's just get the answers we need and let's make decisions
faster. But at the end, human beings are always going to be the ones to make decisions. I'm,
I don't believe companies are going to be AI driven like cars are going to be self driven.
It doesn't work that way. Yeah, and that makes sense to me. I can see that and I tend to agree with
you. I think the humans will still make the decisions, but will be much more productive,
have the data much more readily available and being able to be better equipped to make those
decisions better to analyze the data. I think that's where Chatchee PT can be great in some of those
areas is helping you get something ready. You still have to decide what to do with it,
but it can make a big difference in that preparation process. Just wanted to add that I,
someone else had said it so well at the AFP conference. This idea that many FPNA folks today who are,
who can be very strategic are spending 80% of their time in a, you know, kind of like in a
back room by themselves running various analyses because there's just so many scenarios that we need
to be running at all times. But as all of that gets automated, the role of those folks and in
general, the role of most FPNA, FPNA professionals will be more to have conversations to be in a way
in the spotlight, to have discussions, to talk about the trade-offs and the decisions. And so I see
this kind of shift from not to say you don't need your Excel skills anymore. I think you will always
need some level of technical data skills, but you will spend less time practicing those skills and
more time practicing a lot of the soft skills communication negotiation kind of alignment with
stakeholders. And so I'm seeing a lot of folks actually focus more on developing those skills as
well, not just the technical. I agree with you. I said, a lot of that data prep, those type of things,
we'll get to the point where a lot of that will be automated. And as you mentioned, the soft skills,
the human skills, the people skills, those things will become more important. So we're coming up
on the end of our time. So we have this section called the Get to Know You section. How this works is
we have four questions we're going to ask. You get no more than 30 seconds to answer each question.
So I just kind of relatively quick. And it's just to have a little bit of fun and get to know you
on a little more personal level. So the first one is, what is something interesting about you that
maybe we wouldn't find online or that not many people know that you can share with us?
Well, I grew up in New York City with skyscrapers. Did not see a mountain until I was 18 years old
when I did a road trip across the United States. And then it was love at first sight.
So that's why I moved to Boulder Colorado later with no family and no official relationship
there whatsoever. But I live in Boulder. I love my mountains. It feels very, very natural for me
being here in the center of the US. Great. Now I love mountains as well. I grew up in Salt Lake,
but I've always been a big outdoors person. A lot of mountains camping, hiking. So I can totally
appreciate that one. So next question, if you can meet one person in the world that are alive,
who would you meet and why? Gosh, there are so many people. I think if I had to pick one
Mozart would be fascinating. The kind of music that he wrote especially at such an early age
is astounding. I don't know how many people even today or since then have been able to replicate
that. So just understanding how such a young boy is able to do something so incredible that
has touched humanity for centuries afterward. That would be fascinating. Yeah, I can see that
when it would be fascinating, especially as a kid to watch him work and just see that
this thought process because yeah, it stood the test of time and he did it at such a young age.
It really is amazing. All right. So this next one we're going to ask you is what is the last thing
and I'm going to ask specifically, what's the last thing you use, Genitive AI that you asked it about,
something related to finance, FPNA, Excel. What's the last thing you use with Genitive AI for in that
realm? So I do a lot of research and testing or I try to using chat GPT. Some things that's
very, very good for and other things not so much. I was trying to get it to help me brainstorm
topics around actually finance and AI and it was not able to give me a whole lot. If you go and
you search for machine learning and finance or AI and finance and you try to figure it out,
not a whole lot, which I think just goes to the internet in general not having a lot on that topic.
But the level of interest compared to the level of knowledge or information available,
it's very big discrepancy. So we're working to fill that. Interesting. Yeah, I had an interesting
search and I would agree with you. We definitely don't have as much information as I think could
help all of us. So the last one here, obviously, you know, data, our sponsors to show great
Excel tool for smaller and medium-sized businesses. And so one of the questions we ask everybody
is what is your favorite Excel function or feature, favorite thing about Excel?
So I will have to say the keyboard shortcuts. The fact that there is like a world Excel
championships and there's so many lovers of Excel, Excel is not just a tool. It can become
something that you can be good at. Something that almost like a gamer or like a chess master,
you can master Excel. And the fact that you have all these keyboard shortcuts that not only make
you insanely efficient, not having to click through like a whole 10 different clicks into some
modal somewhere, but it's almost like a secret language that some people know and some people
are very, very, very good at. It makes Excel almost feel like a super hero's tool if you know how
to use it. So I absolutely love that. And I think like to all FPNA folks out there, if you're not
using Excel, demand that the tool you do use cares about keyboard shortcuts, right? Because it's such
a super power. I have to laugh because that's what I've been doing all day today because I'm prepping
for the FMI, which is a modeling test and it's all about speed with that one. Because you get four
hours to build an entire three statement model. So yeah, you got it. You have to use shortcuts or
you're not going to pass if you're doing the mouse, you're going to be there forever. So yeah,
there definitely is a whole world of shortcuts. And it's amazing to watch some of these people how
quick they can do something. It is, it is pretty amazing. So you have just two questions left here for
you. So first one is what advice would you offer to someone starting a career in FPNA today? Any advice
you give them? Probably similar advice to anyone in general, but especially FPNA given like we said,
even an analyst tends to have more of a 360 view of the business. It's a very rare position to be in.
Most important would be focusing on learning. I think historically FPNA has often hired people
for processes. And even an analyst will typically get hired in into one business line. And you tend to
do a lot of that back off of spreadsheet stuff. And you don't, you're not running the budgeting
process. Usually the manager of the director is. But the more that you can, I guess, try to get your
processes done as quickly as possible and try to learn more about those interactions between finance
and the business. Try to take advantage of being in finance to get the context of the business as
a whole. If you work in a public company, listen to the earnings calls. We used to do that as an FPNA
team altogether. It was really, it was really special. Just like take advantage of the fact that you
were in a very rare position to have access to so much information. And even as an analyst to
the leaders of the company in a way that most people, like just a data analyst, junior PM,
a junior engineer, wouldn't have that level of access. So just really, really trying to learn
as much as you can early on. And like we mentioned, look to develop not only your core finance skills,
but also some technical knowledge that not beyond a certain amount is needed, but at least some
is good and then soft skills as well. Got it. You know, great advice there. And I agree with
about learning the business and being curious so much value in that, especially in an FPNA role.
So last question here, if someone wants to get a hold of you, what's the best way for them to do that?
Email or LinkedIn. I'm just catkat at reddo.com. So super simple. And I'm on LinkedIn as well.
So find me there, connect with me. I'd love to hear from you. I love chatting about this stuff.
So all questions, comments are open. Great. Now, I know we've had a number of conversations on a lot
of this stuff. So thanks for joining me. I really enjoyed learning more about your story and experience
working in data science and FPNA and bridging that gap. And then I was starting your own company.
And so thank you so much for joining us today. I really enjoyed the conversation cat. Likewise,
thanks so much for having me, Paul.
