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And now, onto the show. From Data Reels, this is FPNA Today.
Welcome to FPNA Today. I'm your host, Glen Hopper.
Today, we have the pleasure of speaking with Christian Waddig, an accomplished FPNA expert with over 14 years of leadership experience in multinational corporations and fast-growing tech startups.
Christian spent 11 years at Procter & Gamble and Unilever, leading FPNA and accounting teams before earning his MBA from NYU Stern and transitioning to the tech sector.
He played a pivotal role in taking Squarespace public as an FPNA leader.
Now, an entrepreneur, Christian shares his extensive FPNA expertise with finance teams through the Wharton School of Business and his own company, FPNA Prep.
He serves as the director of the Wharton FPNA Certificate Program, works as a corporate trainer, offers online training programs, and regularly shares FPNA best practices with his LinkedIn followers and over 30,000 email subscribers through his weekly newsletter, FPNA Tuesday.
Today, we'll be discussing Christian's transition from FPNA leader to teacher, the four phases of FPNA team development, and how modern FPNA tools can help finance teams advance to the next stage.
Christian, welcome to the show.
Thanks for having me, Glen.
So, I got to say, FPNA Tuesday, you're on FPNA today and our good friend, Mr.
Paul Barnhurst, former host of FPNA today.
Now has FPNA tomorrow. There's a lot of FPNA, and it's like there's a timeline we got to keep up with here between today, Tuesday and tomorrow, I guess.
Well, I think there's not enough good FPNA education out there because it's a topic that is really not covered that well by traditional university education.
It's very technical, it's very focused on theories.
So, I think it's great what you're doing with this podcast, and we need more of that.
It's funny. In talking to, in being in this role as a podcast host, and you certainly out there as a thought leader on FPNA and all the others that we talk to all the time, I love that.
I mean, there may be as a friendly competition between how many listeners you have or how many followers there are or whatever, but really, there's the amount of times that we end up interacting and working together.
I think it's, I've really enjoyed that as part of the community.
And I know 100% agree that this really dialing in to the art of, the art in science of FPNA and what we need to do is so important.
And I think you're really out there on the forefront, leading in preaching this and helping to teach kind of the next generation of FPNA people.
So, really appreciate the work that you're doing.
Thank you. Yeah, and I have to say, when I considered quitting my job in corporate finance, and we'll talk about that later, and when I made the decision to be an entrepreneur full-time, I was concerned that it would be a lonely journey, not having colleagues anymore, et cetera.
But I realized that there's this fantastic group of people like yourself and Paul and also Nicolas Hushin and others who also devoted their career to sharing FPNA education and furthering people's development in corporate finance.
And exchanging ideas and going through this journey together and connecting regularly over LinkedIn, over Zoom, or even in person, just makes this whole endeavor feel much more doable and much more fun as well.
So, yeah, I appreciate it.
Yeah, and actually, so thinking about that, I mean, I think for all of us, maybe it's similar and I haven't really talked to a lot of the other folks out there directly about this.
But I'm thinking of you before you decided to make this leap.
You're going along, you're doing your work in FPNA.
I'm wondering like what the early seeds of thought were and when you really started thinking, yeah, I'm enjoying doing this, but I could also teach others.
And I'm just thinking about that shift from being in the weeds doing FPNA to moving more to this professorial role.
Tell me a little bit about that for you.
Yeah, so for that, I think it's helpful if I share a bit about my background and how I came up with the idea to teach because it's all closely connected.
So I started my career at large consumer goods companies, spent four years at Prokden Gamble, working out of Switzerland, and then seven years at UniLiver, working out of New York City, where I'm based now.
And I would recommend people who are early in their career, who are maybe still in school, considering which role to take, to start their career in a large company.
Because their companies have the money to focus on having good tools, focus on having good processes.
And many of these companies have decades of iterating and figuring out what's the best way to do a forecast, what's the best way to synergize financial modeling, et cetera.
And so it's fantastic to learn when you're when you knew in a profession.
And I had fantastic experiences at PNG.
I was faced with a business partnering challenge right out of the gate, where I was working with a sales team who, and they were consistently over promising in their sales projections and under delivering.
It was my job to help them bring more realism into their projections and improve focus accuracy.
It was fantastic. And then I also had the chance to lead a financial modeling process, where PNG was considering building a new factory in South Africa.
And it was a hundred million dollar investment that had all hinged on my financial model to see if the NPV would be positive.
And if we were able to have a payback period that's decent and I learned a lot during that as well.
And then at Unilever, I actually had the chance to lead the internal finance learning and development team.
I was doing that on the side while I was leading the marketing finance and the analytics team at Unilever.
That was a fantastic experience because it was challenging at first.
I felt like, what do I have to teach my fellow colleagues?
But then when I said across from them, and we were talking about pivot tables and how to become more efficient using those.
And I saw people's eyes light up and they got something.
You know, that was super, super rewarding.
And I think that was when I first caught the back about teaching.
And then later I had the chance to be promoted and to have my own direct reports and to lead the teams.
And they're also, I realized that it's what I enjoy most about being a manager is helping my team develop and helping them grow their skills and helping them learn.
And that wasn't yet enough for me to say, okay, I'm going to quit my job and I'll build an education company.
But what was super eye-opening was changing industries.
So after having spent 11 years in consumer goods, I did my MBA.
And then I moved into a smaller tech company because the one thing that I felt a little challenging working at these large multinational companies was the decision making was slow.
You have an idea, you have a recommendation, and then the global team has to approve the regional team has to approve the local team has to agree to everything.
And it takes forever until these decisions are made.
And so I moved to tech because my fellow students during the MBA program told me that decision making is much, much faster there.
And that was true. I was just two levels removed from the CEO and owner of the company.
I was at Squarespace, which is a website builder company.
And also, I think when you work in software, you have just fewer moving pieces.
You don't have to design the product and then produce the product.
Make sure you have the machines to produce it first.
And then ship it, store it, sell it.
The software is much easier.
You put something together and you can immediately release the feature to customers.
And that was very refreshing.
But what shocked me in a way was that the challenges that FPNA teams face are very, very similar compared to the challenges that people in the multinational consumer goods companies face.
Even though the numbers were completely different, now I was going from 10 billion in sales in the US alone to 600 million in sales.
And of course, the metrics completely different.
The SaaS business has completely different metrics compared to a consumer goods business.
But then the challenges like how do we improve our focus accuracy?
How can we become strategic partners to the business and do more business partnering?
Or how do we build a financial model that's optimized for iteration?
They're the same. They're really industry independent, these challenges.
And they also, it doesn't really matter whether you're a smaller or large company.
And that was what really made me think, maybe there's a way I can help people navigate these challenges and improve their FPNA skills.
And so I set out to create my first course and everything else is history from there.
And you've moved now, I mean, the program that you've built for Wharton is pretty amazing.
And I think that that's, I definitely want to dive into that.
But I'm wondering, so my path to being a pedantic weirdo, I guess, it was a little bit different.
But for me, it was, you know, when I started my career, I started as a journalist actually before I went to business school.
And so writing was always a way that I sort of made sense of things.
And so if I were trying to really figure something out, I would take these notes.
And because I was trying to journalism, a lot of times the notes would end up looking like, just, hey, this would be a good blog post.
And I sort of evolved from blog posts to ultimately teaching a lot of the stuff.
But I think that for me, the way to learn something really deeply is to break it down at that level.
But then to sort of commit yourself to teaching it to other people, well, if you're going to be teaching it to others, you can't be in done in crewgroom mode.
You have to really like dive deep and understand it.
So I'm wondering as you built out this, and maybe I'm jumping the gun a little bit because I do want to hear about the structure of the program.
But as you built out the program, maybe, you know, take me through the process of if you're going to encapsulate FPA into a single certificate, you know, that's got to be a pretty big project from just architecting it from your point.
And I'm wondering, even as long as you've been doing this, did you find yourself learning something new or taking, or what did you take away by putting the course together?
Yeah, absolutely. And maybe before we talk about that program, I can, I can touch on how I developed my first course because that really was then what led me to be able to do this much bigger program.
So my first course came out in 2021.
It's a live course called FPA bootcamp.
It's where we have four workshops, four to hour long workshops over the course of two weeks.
And the workshops are super hands-on.
You know, you have lots of break out room discussions and exercises.
And that's something I first had to learn because I had the the FPA know how, but I didn't know how to translate that into an engaging course.
And fortunately, I had a fantastic mentors there from a company called Maven.
It's a platform where they host these courses and they also teach subject matter experts to turn their knowledge into a course.
And they had a fantastic training program there.
Then I created this course and I decided, okay, I want to offer it every other month.
And so I, that was in hindsight, was a fantastic decision because by iterating and by presenting it again and again to different groups of finance professionals, I got a lot of feedback.
I got a lot of notes on what connect weak, what can I improve, how can I make it even more engaging, how can I go deeper, what are the areas that people want to learn more about.
So in total now, I taught this class 18 times.
That was super, super helpful because it really helped me to figure out also where are the gaps because people had the chance to ask me questions.
In addition, I always had two full hours of Q&A sessions where people can just grill me and ask me anything.
And that was also fantastic because I learned what people really worry about and what that what keeps them up at night.
And that then made creating the much more comprehensive program I'm doing with the Wharton School much, much easier.
Yeah, so kind of iterative development to really refine the course.
And so then you reach the program for Wharton.
So tell me what changed in and give me a little bit of a run through of what the Wharton program looks like.
Sure. So the Wharton program for those of you who don't know, it's an FPNA certificate program that was developed jointly by the Wharton School and Wall Street Pratt.
And I'm the program director meaning I'm responsible for the curriculum and most of the content.
So we're also working with a number of other guest instructors and professors for Wharton who also contributing content and or teaching part of it.
And it was fantastic also having had the chance to work with you on that program as well.
Glenn, thanks for for contributing some of your insights there as well.
And there are a few things that make this program unique.
So it's it's an eight week program where students go through the lessons in a self-paced way.
So where they go through videos and exercises and can really do it on their own pace, can speed things up to their ready-know, can slow things down.
But in addition, there are weekly life sessions where you have direct interaction with the instructors.
You can ask questions. There are also one-to-one sessions for when you want to maybe talk about a career aspect that you don't want to share in front of a group.
You can do that in a one-to-one.
And we also have in-person networking meetups where I'm traveling to different cities.
And where people can meet in person because I think there's it's just so magical to actually meet face to face in these these days when so much is happening over over zoom and virtually.
And additionally, the program is very comprehensive.
So each week is about 10 hours of workload.
So it's a combination of videos and exercises.
And that was a real challenge for me at the beginning when I was tasked with creating a curriculum for essentially 80-80 hours of content.
And I only had about eight hours of my pre-existing training programs.
So I had to build and build the remainder.
I ended up putting together about 40 hours of content and other instructors put together the other half.
But it forced me to go back to research and really look at, okay, what are other FPNA best practices that I'm maybe haven't really come across yet and haven't considered yet.
So I went and I read a number of books about the topic.
And often often people ask me, you know, what are those books behind me?
And are there any recommendations?
So I can recommend two books specifically that I think will also be relevant for you audience.
There is the CFO lens. So the CFO lens by Ravi Kumar.
He's a former CFO at IBM and in IBM India.
And it's a fantastic book about how to approach finance from a strategic perspective.
And the other book that really stood out to me was called Future Ready by Steve Mollich and Steve Player.
And this book is all about forecasting.
Now about creating forecasts that help make decisions and analyzing the numbers to be able to create those forecasts.
And there are a few other books as well.
But these two really stood out to me.
And when I went into, you know, creating this course, there were a few things that I also didn't come across before even in 14 years of FPNA.
And two things, for example, just maybe for your listeners as well, if they haven't come across this yet, that I recommend is the first one is Business Driver Tree Analysis.
I don't know, have you heard that before?
Walk me through it. Business Driver Tree Analysis.
So how it works is, so imagine the challenges you're doing, variance analysis at the end of the month.
And you say, you realize you missed your sales forecast by 10%, but you don't have a clue why that's the case.
And the goal in FPNA is always to come up with not just what's driving the variance, but also what are the underlying reasons, what's the root cause, and then ideally what's a concrete recommendation.
And Business Driver Tree Analysis can help visualize that and help you find that root cause.
So how it works is you create a diagram.
It looks a bit like an org chart, but at the top it's not the CEO, but it's okay, we missed the sales by 10%.
And then you put in all the levers, all the drivers that could potentially cause it.
So for example, did our promotions not perform as well as before?
Did our conversion rate drop?
Did our number of leads decrease?
Did our customer change their order patterns?
And then for each of those reasons, you have further lines going down in the diagram that define all the reasons that drive that.
And then you go keep going like that until at the bottom of the diagram, you have the true root causes.
And I always find when you can point to a root cause, then it's much easier to say, okay, here's a concrete recommendation about what you can do about about this.
So let's listen to our three analysis.
The other thing, the other analysis technique that they came across in my research was differences in differences analysis.
And that's a way to figure out whether there is a correlation between what you're doing and the financial impact it has.
Or to put differently, it's a way to find out how well your marketing or whatever you're doing to drive the business, how well that's working and how well that's translating into sales.
And essentially, it's an approach that's borrowed from science.
It's where you have a treatment group and a control group and then you measure the impacts differently, you calculate the differences and then the differences from the differences.
And if you google that and you look that up, it's a it's a really neat way on how you can figure out contributions and figure out how well your business drivers are working without needing a degree in statistics and without needing all these sophisticated statistical tools.
Yeah, and now I'm teaching those concepts in as part of the the Wharton program as well.
And it's really one of the aspects I loved about this is that I did had the chance to do a lot of research to read a lot and really get more in depth in terms of the in terms of the FPNA skills that we're developing here.
And that's thinking about who should take this course.
For me, I got a general MBA years and years ago.
And so my first when I first came into finance, I loved the MBA.
It served me well over my career, but going into a hardcore FPNA role, I really wasn't prepared for it.
So I think if you were if you're doing general business being able to really dial in, but then I later went back and got my masters in finance.
And regardless of what your educational background is, I think that there's a program like this or any of the other FPNA specific training we're doing is very important because you see if you whether it's undergrad or master's degree, when you're studying finance, you end up having to take courses in like retirement in a state planning or international finance,
commercial banking, I'm trying to think of all fixed income and derivatives and portfolio design and all that.
That is very hardcore. It's sort of maybe some of it goes more towards CFA stuff, some of it goes maybe towards accounting.
So really lasering in on what we do in FPNA.
I'm wondering if you what kind of backgrounds are you seeing people come in?
Are you seeing people with masters in finance and MBA and mid career or what are you seeing early on who's signing up for this?
Yeah, so it's really a mix.
It's people who are some people who are just starting their career who may be even wrapping up their undergrad, but about 70% of the students are have at least five years of finance experience.
So they could be either in accounting and they've been doing accounting and they want to transition into FPNA or they are in a controller role of the smaller company and their boss is telling them, oh, we need to be more forward looking.
You need to figure out how to develop an FPNA process, go run with it, and they don't know where to start because they're back on this in accounting.
But we even have people who have 15, 20, 25 years of experience in finance or even in FPNA.
And they take the program because they say they don't know what they don't know.
They don't know whether they're really following best practices across the board.
And what differentiates this program also is that we are focusing heavily on interpersonal skills as well.
So you learn the hard skills of financial modeling and forecasting and analysis, but in addition, we cover business partnering, building relationships, developing trust, developing influence, different influencing styles, and also presentation skills.
And I really think that this is so, so critical, especially as the more manual tasks get automated.
And so I think where FPNA is going is really towards FPNA leaders needing to make use of these interpersonal skills more and more because everything else that's happening in Excel becomes, it's still relevant, it's still important, but there's less time spent on that because our tools are getting better, and we've become more efficient.
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What I found early in my career is the background in finance and statistics was helpful, but I didn't realize back then what BI is.
I certainly don't think there's anyone who is called a data scientist back then.
But I'm wondering in the how much do you talk either in this course or just in general how you see the overlap of BI and financial analysts and ultimately data science.
Now I know in an eight-week program they're not going to learn, you know, it's not going to be the equivalent of a master's in statistics, but there is a big component of having that statistical knowledge and how you apply it in FPNA and all that.
So how much do you talk about this sort of beyond traditional FPNA or maybe now traditional FPNA is being left behind and it really is a lot more about the BI and the data science.
What do you see in there?
So I've been thinking about this and I'm thinking about it slightly differently.
I don't think that FPNA is left behind and BI and data science is in the foreground.
And I also frankly don't believe that to succeed in FPNA even in five, ten years down the road, I believe you don't have to become a data scientist.
And the reason for that is twofold.
One, companies at a beyond a certain scale, you know, have the budget to actually hire quote-unquote real data scientists.
So those are people with a PhD in statistics and they can do a completely different work than your regular FPNA professional who tries to build their statistical skills on top can do.
So I think companies are better served at keeping the skills at different, separate and separate teams because it's just such a different skill set.
If you are data scientists and you're building machine learning models from the ground or you're even just combining different different statistical techniques, it's a different skill set because you need depth but you don't need the breadth that an FPNA leader needs to have.
You know, an FPNA leader needs to be able to use what the data scientists give them and maybe even ask them to do things a certain way or to investigate certain things.
So they need to have that fundamental level of knowledge.
But then FPNA leaders also need to know how to take that data, translate it into insights, get the story behind the data, connect it with operational metrics and then put everything together in a model and eventually also essentially sell the recommendation to the other leaders so that it eventually hopefully gets implemented.
So I think it's a different skill set and it will remain separate.
That said, what's important for FPNA leaders is that they have an idea of what data scientists are capable of doing essentially.
What I teach in my programs is helping people at least understand some of the core concepts of what machine learning is.
For example, what it does, how it can help, how it can supplement other forecasting approaches and also what the downsides are and the watchouts and negatives.
And some of the terminology, some of the definitions and the language that data scientists use, so that you can partner with them and you can engage them and ask them to go build a statistical model that helps you achieve your goals.
That makes sense. And I think what you mentioned is when a company gets to a scale, they have someone who's a PhD and is really, they understand what's happening at a statistical level and how to build these models way more than someone who, by the way, their day job is being a finance professional.
So you're not just going to have a hobby of having that skill.
So a lot of times, I think in companies that don't have that luxury, you do find FPNA people trying to extend and reach out and use some of that stuff.
But you can get yourself in trouble if you're using the wrong model or if you're using like drag and drop machine learning and you don't know the difference between, you know, clustering algorithm and a regression algorithm or whatever.
So you know, there are downsides to people trying to get out over their heads.
But I think that is the perfect world where, as the FPNA pro, you understand what it is and the value of it.
But you don't have to then also go and build these complex models and Python or whatever they're building them in.
So yeah, great points there.
And I know, and you know, we talked about data science and how important data is to what we do in FPNA.
But there's something you and I have talked about before in the development of an FPNA team through four phases.
And I love the way that you put this.
And I think you've got with data focus story, then strategy and then to being a true value creator.
Can you walk me through this and maybe elaborate a bit on each of the phases?
Sure. So I had this chance to work in many FPNA teams because when, even when I was at Unilever and PNG every two years, I moved into a different team.
And I found that there are four distinct phases of FPNA development.
What I mean with that is building out a comprehensive FPNA process.
You can't do that from one day to the next.
You know, it takes time and there are some foundational criteria that have to be there until you can get to proactive value creation, which is at the top of the pyramid.
And people told me that the framework is helpful because it helps finance teams determine at which stage of the development they currently are, which then helps them figure out what should be the next steps to get me to the next phase to the next stage in FPNA development.
And so it's four stages.
At the bottom of the pyramid is data focus.
So that's where the focus of the team, the majority of the time is spent on making sure they have accurate and timely data available.
So I'll go through the stories, the stages quickly.
And then I'll talk a bit more about how you know when which stage you are.
So the first stage is the data focus.
The second stage is story focus.
That's where you take it step beyond the data and you are able to make connections between the data points and uncover two insights and the stories behind the data.
But you're not yet focusing on operational metrics.
You're mostly focused on the financial metrics.
The next phase is strategy focus.
That's where you get out of the finance silo.
You look at the operational metrics that are available across across the different departments and you understand their strategies and you understand how the strategies of the other departments translate into tactics to translate into action plans and important measurement.
And when you understand that, then you can say, okay, if this operational metrics changes by 10%, probably the impact on sales will be 5%, will be X.
And that's powerful because that helps you to become more forward looking.
And then the last stage is proactive value creation.
That's where the finance team can add value by making concrete recommendations about where to take the business and concrete recommendations both about accelerating growth and improving profitability.
So to drill into that a bit, how do you know you still in the first stage data focus?
Well, you probably spend most of your time on creating your accruals when preparing your actuals and you're spending more time on that and getting the data ready than you actually spend on analysis and forecasting.
Another indicator is that your month, the management reporting deck, follows always the same structure, always the same slides just with different numbers.
It means that you don't really have the capacity to figure out what the story is and what should be highlighted.
And your forecasting is mostly top down and completed by the finance team without cross-functional ownership.
That's stage one. Stage two, story focus, that's where you start to highlight the metrics that actually matter.
You don't just share 20 slides of reporting every month.
You actually, you may still have those 20 slides, but in addition, you have three or four slides at the beginning that tell the story and that tell leaders what to really pay attention to this month and what moves the needle.
But you're not yet considering operational metrics in your analysis and reporting.
That comes next at stage three.
And that's where you actually have a closer relationship with the cross-functional partners.
You understand what their pain points are, what the challenges are, and how they want to, what the goals are, and how they plan to achieve their goals.
And then you can help them measure that.
You can help them link that to the, to what's happening in the financials because that's really the cool thing about FPNA is we have a unique perspective that no one else has.
We see what's going on on your provident last statement, your balance, your cash flow.
And at the same time, we have visibility of what's going on in operations, factory, our, our, our, our website traffic going, how are the people moving through the marketing funnel or through the sales funnel.
And that unique perspective, then if we develop that business understanding, right, that's super, super crucial here, that understanding helps us connect the dots and then really help drive strategy.
And then the last stage, proactive value creation, you know you're there when you can, you do your balance analysis and you don't just find reasons for why you're missing or you're, you're exceeding expectations.
You're actually identifying real risks and opportunities and you're making concrete recommendations about how can companies capture the opportunities or mitigate their risks.
And another indication that you're made it to that point is that leaders from other departments actually come to you, the finance leader and ask you for advice about big decisions that they are considering.
If, when that happens on a regular basis, you know, you probably hit that level of proactive value creation.
So that's the framework in a nutshell.
And what's important to understand is that you can just jump to proactive value creation right away.
You need to have your house in order first, you need to make sure that your data's accurate, timely.
And you need to make sure that you're building a step by step until until you can get there.
I love that evolution because to me, it kind of goes along with two other sort of evolutions.
One is a company's data maturity level.
So at your data focus, it's sort of that lowest level of data maturity of a company where I picture everything seems like an ad hoc report.
You're going, you're just constantly grabbing data and putting it into, into Excel and you're maybe manually running your depreciation, whatever it, I mean, it's just there's not a good data governance policy.
So I think about the levels of data maturity.
And then I think about also the types of analytics that are out there and how you're able to leverage the data.
So when you can get out of that sea of just fighting with your data and trying to do so much work to create reports, then you are able to start kind of seeing the forest for the trees and see the, be able to see the big picture and able to move on to where you're really creating value.
And I do think that as you are able to move up and get a better handle on the data and have everybody sort of plan from the same sheet of music when it comes to what the metrics are and what the sources of truth are in data and everything that that's how you really get to a level where you're starting to provide this value to the company.
Not just, you're not just the rear looking, you know, reporting.
It's like I'm identifying things based on data here.
And I think that having that data foundation is important, but then being able to use analytics to drive that decision making and to drive that strategy and add value to you to your mind as you move through those, how do you leverage data and analytics to move up on the pyramid?
Yeah, so having data, having accurate data in places is super important and not just accurate, but you mentioned a critical concept, which is single source of truth data.
So when I think about single source of truth, there are two ways to of looking at it.
One, you want to make sure that your data is in a place where you can easily access it, a deal in one place, and everyone knows that this system, this repository is the truth, you know, it's the correct number.
And there's no debating about whether the number is right or wrong.
This is the number. And the reason that this can sometimes be challenging is that companies are adding different systems on top of each other, you know, that it starts with your accounting system, but then there may be another earpiece system connected to that and another sales system, another system, and sometimes the numbers change a bit when they move through
those systems. And so you need to be very clear about what is the single source of truth where everybody goes to get the numbers.
The other aspect of that is single source of truth of metric definitions.
So I, a company I worked before, there was a big challenge there because there were some metrics that the product team was measuring and the finance team was measuring, but they didn't align.
And so when we had discussions with leaders about how to interpret them and what they tell us and how we need to take action, it was a mess because finance, we would say, oh, this metric is going down, we really need to do something about that.
And the product leaders said, what are you talking about?
It's fantastic. Look here, I can show you all these charts and graphs of that build.
Look, it's perfectly stable.
There's no concern. And then you're just just losing so much time and energy about discussing which number is right and which definition of that metric is right.
And so that's why, you know, when, before you even think about going deeper and investing into your analytics, you need to make sure that your, your houses in the order, single source of truth is there.
And then once that is clear, it really comes down to what I like, what, what some people call XPNA, right?
So I agree with that. I agree that financial planning and analysis is a misnomer.
It really should be not just about the financial data, it should be about the entirety of the data set in a company because the real value from analytics in my point of view comes from connecting them.
So for example, there are most companies have a consumer hotline, right?
And it's not traditionally something that finance pays a lot of attention to, but they're really short because when they see the, the call volume suddenly increase in your consumer hotline, it probably can tell you something about something going on in the product that is not, that this could be a concern, right?
It could be a quality concern.
And you don't typically notice that right away in your sales, but you will a little bit later, maybe in the next quarter.
And the finance can be close enough to the business to first have access to these data points and then to understand how they translate to financial performance that can make a huge difference in being able to see spot changes to trends earlier.
And then ideally early enough that you can actually do something about it.
I love to hear you say that because I think throughout my career, I have believed from my first financial leadership role.
And I think it's because I came up, I kind of came a security, a circuitous route into finance through I was the finance guy for the COO and had to adversarial relationship with the finance team that rolled up with the CFO initially.
But I understood because I rolled up to the COO, I had all these ops metrics.
So when I was doing my forecast, and this was in Telcom, and so I could see network outages.
And if there's a network outage, what is the impact?
Or if there's this client has or a customer has this many trouble calls in a month, they're now there are red flag for churn, but having access, this is back in the days of where everyone had data silos because you, everyone was kind of reinventing their metrics early on.
And so if you're, if you're, you had a number that was looked down this quarter, well, let me just redefine, we're not going to report on that anymore.
We have a new metric we're going to track or we're going to report a different way.
Whereas if, if you move it into finance, if finance is the arbiter and the truth teller about the financial metrics, good, batter, and different, then you should also be reporting on the company's other metrics.
So if sales pipeline looks a little light, they're not going to reinvent what a, you know, what a lead or a prospect is or whatever to make it.
And regardless of who owns them, I guess it is that define, you know, what the metrics are and lock them in and don't have them changing because then you have Apple and Orange comparisons over time and that causes its own problems.
But thinking about how important it is to define those metrics and then have them reported consistently and how finance can use them in other ways.
So whenever I would come into a company, new CFO, I'm going to take your reporting and your reporting people and bring them under me and here's why.
And it's, and you know, I think it becomes the sum is greater than the individual parts because when you have, you know, first off, you're all singing from the same sheet of music.
But secondly, now you've torn down those data silos and you can use them and you're in your modeling and measurements and everything.
So great, great points on that.
And you know what's interesting about that?
What I found is that it's not only a technology challenge, you know, sometimes the bigger challenge with getting there is the mindset change that needs to happen with people because in the traditional world, you know, where things were siloed and marketing was owned there, marketing metrics sales, own sales metric product, own product metrics that also made
it so that people got used to that and leaders could use to that.
And there are now some of them are concerned about removing those silos and giving finance access to the data because then in some sense, they're also giving up a bit of power because they give up the power of interpretation and of being able to point to an issue, but then not necessarily sharing that issue with the with the leadership team of the company, but you know,
figuring it out and trying to figure it out on your own.
But it's not the most effective way to run a company.
You know, ideally, everybody works together, puts the head together when they're issues and people who are in this close to the business like you're when you're an FPNA or in finance, oftentimes we can have a unique perspective on things and can actually help you address those issues.
But in order to get there, the leaders in the different departments have to trust you that you're not using the numbers against them, you know, that you're not using them to cancel the favorite project or to take money away from their from their experiment that they're running.
And so the relationship aspect is super, super important and basically showing people that you can do more than just cut budgets and update forecasts, you know, that you can actually be a strategic partner and help them reach their goals.
Yeah, I mean, there was historically the whole view of finance was, you know, the CF, no, like CF, I was just going to shoot everything down.
Oh, we're not budgeted for that.
And I think the idea of business partnering and it is, it's an evolution.
And I think we're all saying it, and especially people who've been around the business for a long time, just the difference in the whole concept of business partnering didn't exist before.
But now the idea that you have embedded FPNA people and in some companies, you have embedded data scientists in, you know, each operational group and working together all towards, I think that's the evolution.
And I'm wondering, so we could talk all day about the technologies and tools that people use.
But I'm wondering, and you mentioned the soft skills and I think these soft skills are going to stay important as we offload more and more to AI and, you know, whatever type of automation and whatever type of AI that is.
But what do you recommend to people?
And this is the, we've had this question for years, it was, you know, it's digital transformation.
We've been talking about for 30 years and it turns out that it's, it never ends.
It's sort of just the evolution of the business.
But from a, just soft skills, from a change management perspective, and and especially if you're trying to lead from within finance, how do you advise people to move from this to elevate themselves, to move from data focus to story, to strategy, and then to, to help bring others along to where they really are creating value for the company.
Yeah, so you mentioned tools and tools to play a big role there because a lot of finance managers, FPN managers, I talked to, told me that, yeah, 100% agree with you.
We need to be more strategy focused.
We need to learn more about the broader business.
We need to talk to our business partners more.
Have you just don't have the time?
You know, we spend so much time in creating our month end reporting, our management, our events, analysis, focus updates, that there is maybe, there may be a half a week, a few days left in the month until we have to start the process all over again.
And that's where I'm a big, I'm a big proponent of FPNA tools, you know, and two, for companies to look at what is out there.
And a lot of tools came on the market in the last five years for every segment of the market you're in.
And I've got highly encouraged people, even if they feel like they are too busy to implement a new tool, especially then, you know, to prioritize that.
Look at what's out there because what these tools can do is they can automate your reporting in one click.
They can update collecting input.
They can automate collecting inputs for forecasts or make that very easy because people can directly enter the data into into the cloud rather than you're having to consolidate lots of Excel spreadsheets.
And they also make it easy to keep version control and to manage all that.
And that then frees up the time for you to go and do more business partnering.
Because then once you have the time, the next step is to build that understanding of the business because you can't make those connections without the understanding.
And one of the best ways to do that is just to talk, to spend more time with the business partners, to join some of the operational metrics that they're already having.
And to ask them a lot of questions around what keeps them up at night, what are the goals, and how can you possibly help them?
You know, it's funny, as we're having this conversation, I feel like this is a kind of conversation that would happen in the teacher's lounge at a university in between classes and everything.
And I think we could go down this road indefinitely.
But I do because you're out there so much and you are teaching about this and you're interacting with with businesses and with the students and everything, I really want to get your take before we let you go on kind of what you're seeing now and where we're headed in the near term.
And there's a couple of ways to look at this.
One is just key insights or trends you've seen in finance in general this year and recently.
But also because everybody's talking about it and as you know, I'm very focused on this.
Are you seeing yet any practical use cases for generative AI and or something that has been out there maybe for the larger companies and some people have been using it?
It's just traditional AI as we referenced earlier machine learning.
I'm wondering what you're seeing in interactions that you're having and what your sort of expectation is around the technology in general generative AI and if maybe generative AI and because we're talking about it so much if it's not opening up more finance teams to use machine learning traditional AI at this point.
In terms of we have been going, I think that it's transitioning from FPNA being seen as a publisher of reports and forecasts to being seen as a strategic partner to the business who can do more than that who could who can actually help leaders make better decisions.
So a lot of CEOs, a lot of department heads and CEOs are seeing that change and are expecting that FPNA is doing more, you know, is doing more than just the basic reports.
And as a result of that, people have to figure out how to devote more time to deeper analysis and business partnering and that's where modern tools come in because that's where they that's what frees them up the time as I've mentioned.
And specifically there are three areas in terms of tools that where I see the trend going in FPNA, one is about automation so automating your reporting, your forecasting or your forecast input collection.
The other one is machine learning has been around for a long time and it's a way for FPNA teams to not necessarily automate the forecast process but have an additional way to do the forecasting that doesn't rely on human input as much that isn't prone to human biases as much.
It's prone to biases that are in the data that you're feeding the model with, but combining a machine learning model with a more subjective approach like driver-based forecasting can be can significantly increase your your forecast accuracy.
And so I'm seeing a trend there because these tools become easier and easier to use.
You don't need to be a data scientist anymore to implement a machine learning forecast also partially because you don't really have to know how it works in the inside.
Even some data scientists don't know why some of these algorithms, how some of these algorithms come up with their outputs.
So that's the second one.
And the third one is genitive AI.
Although I really do think that that's at the beginning of the journey and when I talk to finance professionals about how they're using genitive AI, most tell me that they use it to help them with Excel.
You know, if they have a challenge, they have data that's messy and they then go to chatchipity or a Gemini or others and explain the problem they have in Excel and say, hey, can you help me with a formula or with a way I can speed up this process in Excel?
And then the tool walks them through step by step what to do.
And for that, it's fantastic.
I do think that these tools will become more and more important.
But for that, I think it's really where we should look at is these manufacturers of FPNA tools because if you just take the vanilla genitive AI tool, it's not really built to be able to give you a forecast or to help you with a deep analysis.
You know, it can make things up.
It can hallucinate. And so there are God rest and have to be put in place and specific tailoring that has to be done.
And I know that a lot of companies are working on that and I'm really, really curious to see what the next five to seven years hold in that space.
Yeah. And I always, you know, for, because I, I deal a lot with SMB space, you know, say under under 50 million, maybe under 100 million.
And as you get to the higher end of that, there are some, you know, maybe there's additional resources.
But if you're under 50 million or even smaller than that in revenue, you just, you're not going to have an in-house team.
You're not going to have the PhDs.
You're not going to have data scientists.
You're not going to have machine learning engineers.
You're, you're just not going to be able to develop generative AI in any meaningful way that's significant to your company.
So to your point, I do think that the, the SaaS providers, the software that we're using now, we're going to start saying them integrated in and they're going to put all the guardrails around it and they're going to help make it easier for everyone to use.
But to think that companies themselves, unless you're at the, you know, at enterprise level, we're not going to see them developing their own, their own AI tools.
It's just, it's cost prohibitive and the expertise in there to it.
But I do, you know, it's obviously, it's going to change a lot of industries and we'll see, you know, there's varying degrees of optimism and pessimism around it.
But I guess in this environment, and as you are speaking with so many students, whether they're early career, mid-career, just looking for a refresher, whatever, if they're coming to you during office hours and saying, okay, I got a CPA or I got this level of education and this is where my focus is now, how can I future-proof my career?
What do I need to focus on for success so that I'm ready for what happens in the industry over the next five to 10 years?
How are you advising them?
Yeah, yeah. I can start maybe by what I would advise people not to do.
So, you know, sometimes people ask me, oh, you know, which programming language should I learn to succeed in FPNAs, should I learn Python, should I learn SQL and I tell them, don't waste your time on that because these AI tools and even what we have today make it so that for basic FPNA tasks, you don't need to learn SQL.
You can ask the bot to help you write the query and then do it yourself.
You know, you basically, you can transition from just in case learning to just in time learning.
You know, that's where these chatbots really, really excel.
But more broadly, what people should focus on in FPNA really is, I mean, FPNA is very broad, right?
There are so many different areas you need to understand.
And I'm pulling it down to six fundamental skills that people need to focus on.
It's planning, which is, you know, annual budgeting, annual planning, it has to do with managing a process and a project.
Then there's forecasting, specifically look into what are different forecast techniques.
So there are, there's surveys forecasting, driver-based forecasting, time series analysis, other statistical approaches.
And then the third one is analysis.
And analysis and my, how I define analysis is taking raw data and turning it into insights, right?
So doing the business drivetry analysis, I mentioned earlier, other ways to really get to what the root causes and looking behind the numbers and what the stories and numbers can tell you.
And there's financial modeling.
So even though we have all these great tools, you still often, even if you have the best tools, you sometimes there's nowhere around opening a spreadsheet and starting to do modeling and building that from scratch.
So there you need to be able to not just understand, you know, all the Excel formulas you may need, but also building a model that's optimized for fast iteration.
Because the best financial model is useless if someone asks you a question in a meeting and it takes you two hours to update it and run the sensitivity.
That's a technical aspect.
But then there's business partnering, you know, which we talked about today, the whole interpersonal aspect of building relationships, influencing people, holding them accountable, challenging them, and lastly there's storytelling and reporting.
So how do we take all the insights from analysis, what it learns from our business partners and put that together in a comprehensive way that is deep but still easy to understand by the decision makers who need to take what you're telling them and putting it into action.
So that's how I would look at skill development in FP&A.
Perfect, perfect, love it.
I have like 10 other questions I wanted to ask you, but I think the producers are going to beat me up if I keep going.
So we'll try to wrap it up here.
And obviously, we'd love to have you back any time.
And I think this is great, great insights.
And I guess before we let you go, for our listeners, if they want to connect with you and learn more about your work with the Wharton School and FP&A, how can people get in touch with you?
Yes, so for my work with the Wharton School, simply go to Google and Google Wharton FP&A certificate and then download the brochure.
That's where you can really learn about what the program includes.
And other than that, I share a weekly newsletter.
It's completely free. It's where I share some of my best tips.
And you can sign up to that at fpaprap.com.
So fpaprap.com slash newsletter.
Great. Christian, as always, great to catch up with you and thank you again for being on the show.
Thanks so much, Glad. I really appreciate it.
