FP&A professionals are the original business intelligence (BI) experts, says Len Mcfall, senior consultant, finance at Windstream Holdings, a $1billion US telecoms company.
“FP&A itself is synonymous with business intelligence,” he says. “And honestly, weren’t, weren’t we the first business intelligence? We’ve always been working with data. We’ve always been trying to simplify and present data in a meaningful way to people who might not,otherwise be literate.”
In this frank and honest interview McFall, whose background is heavily influenced by BI, gives insights from a 20 year career including HCT Investments, Sitel, Crossroads Treatment Center and a minefield of data at Windstream.
“I’m not a bullet point guy. I’m a paragraph guy” says Len – warning he’s not one for short, snappy answers, but a storyteller who loves to dive deep, as he reveals:
- Why 40% of my job is touching base with people to curate all the data we use across the organization
- Putting sales on blast: how often do you catch a forecast from a sales organization that’s even 50%, right?
- Coming into a company to save them after they were delivering finance reporting 30 days after close (from 30 days to day 8)
- the role of data analysis and bi in finance and in the decision making process.
- How business runs on “hunches” and why it is FP&A to prove that hunch
- Why if you cannot communicate to the business you are useless
- The vast amount of data we have access to in telecoms and how we separate the signal from the noise
- How finance rubs against IT and why it’s not pretty
- Why AI in finance is like a “very smart but green intern” and prompt engineers are ridiculous
- Killing for food in winters during his childhood
Connect with Len on Linkedin: https://www.linkedin.com/in/len-mcfall-6274857b/
Full blog post and transcript
Glenn Hopper:
Welcome to FP&A Today, I’m your host, Glenn Hopper. Our guest today is Leonard McFall, a seasoned finance professional who has been navigating the world of FP&A and business intelligence for over 20 years. Lenny’s career has taken him on a journey through diverse industries, from telecommunications to healthcare, customer service, and retail. He is a principal of finance at Windstream, a billion dollar telecommunications and managed services company where he is responsible for board and senior leadership reporting of strategic metrics and KPIs. I’m excited to have Lenny on the show because like many of us, his background in finance is heavily influenced by bi. He’s worked in areas including strategic planning, organizational leadership, financial reporting and analysis, cash and asset management, and p and l ownership. He’s more than just a numbers guy. He’s also a tech savvy professional with a strong background in data and technology. His toolkit includes Power bi, Tableau, adaptive Insights, Azure, sql, and of course, Excel, using them to transform raw data into actionable insights. Lenny, welcome to the show,
Len McFall:
Glen. Thanks for having me. I’m very happy to be here.
Glenn Hopper:
I guess I, I should probably go ahead and get this out at the beginning that you and I have worked together at, at a couple of different companies. We’ve known each other for what? It’s over 15 years. Years. Yeah. It’s
Len McFall:
Been a long, long time. Long. Yeah.
Glenn Hopper:
So, so it’s like, it’s like old home week on fp and a. Today is I’m, I’m bringing in people that I’ve had the pleasure of working with and, and getting some insights, but easy
Len McFall:
Mode, but
Glenn Hopper:
That’s right. That’s right. So and I guess the other caveat I’ll give at the beginning is Lenny and I both tend to speak in paragraphs rather than bullet points. We should have like a, a, a safe word here where, or a word that if one of us is rambling, the other one can maybe a hand gesture for the people who are getting the audio only of this <laugh>.
Len McFall:
Like, just, just like,
Glenn Hopper:
Just, but plaintiffs, you
Len McFall:
Tell me the word and I’ll, I’ll heat it. Because you’re, you’re right. I’m not a bullet point guy. I’m a paragraph
Glenn Hopper:
Guy. <Laugh>, let’s go. Parrot. You wanna go parrot par? Yeah. Okay. Parrot
Len McFall:
Par one. Parrot for wrap it up. Parrot. Parrot. Parrot means, you know, stop right now. Kind of like a pan pan pan <laugh> in the aviation. Yeah. I’m with you. I’m with, we’re gonna be succinct and yeah,
Glenn Hopper:
We’ve, we’ve already gone long and we haven’t even gotten to the first question. So
Len McFall:
Let’s rock and roll.
Glenn Hopper:
So, Lenny, can you take us through your career journey? I want to hear about some of the key milestones and really the, the maybe where you had those points where it, it kind of shaped the rest of your career. And I’m really thinking about, you know, starting out in fp and a and moving into more and more of a business strategy role as your career progressed.
Len McFall:
Probably the first thing I would say is that I think we can all agree, all of us that are in this business that’s you know, fp and a itself is moving into, is becoming synonymous with business intelligence. And honestly, weren’t we the first business intelligence? We’ve always been working with data. We’ve always been trying to simplify and present data in a, in a meaningful way. It’s to people who might not otherwise be literate. That’s changing, right? The top half of every organization now is financially literate. We’re bringing a lot more to the table than just month over month variances and revenue modeling. My career has been a little bit, you know, is all over the place. Is that a technical term that you can use for a career track? You know, I’ve been in telecom for the, the bulk of my career.
A little bit of nepotism. Got me my first telecom job as a as a buyer of leased facilities. Did that job for a couple years until, honestly, I met you and you were in crisis for somebody with how, how should we say it? Somebody with a forward looking mindset toward capital planning. Maybe you can, you can enlighten me here, but there was a, there was a process involving a printer and reams and reams and reams of paper on a daily, or at least every other day kind of basis. And that was somehow how way, how we managed our capital assets. Is that all you
Glenn Hopper:
Pretty much. So my, my procurement guy actually had and kept a paper ledger Yeah. And printed out every single invoice. And, you know, a lot of these invoices look very similar. <Laugh> and <laugh> have similar numbers on them, <laugh>. And yeah, that one ended up I thought that might be the last thing I ever did in fp and a on, on in budget management back at that point was when, when we had a an error that represented about 12% of our total annual capital budget, <laugh> yeah. That we were you know, heading in, heading into the end of the year, and getting ready to present to Goldman Sachs was involved. It was to the investors. And we were under a lot of pressure to hit our numbers. And oh, by the way, <laugh>, we thought we were spot on. Now that we’ve reached the end of the year right before Thanksgiving, it was if
Len McFall:
That was time, I’d experienced that <laugh>. Yeah. I, I love it that you say 12% and only those of us here would gasp at that. You know, there’s, there’s poor 10 only for the, for your core audience. I think when you say 12% variance, <laugh>, that’s a big number. But yeah. You know, so that, that was, you know, so taking that job with you was my first sort of for foray into planning and, and finance. And you know, big surprise, I never planned to be in finance. When I was a little kid, I didn’t have a career track. I went and got a nebulous business degree, right. Followed that up later with a attached master’s degree. There’s a, a matter of coin toss odds that you’ll end up in in planning after you went off to do a different thing.
I had the exact same crisis that you had a, you know, a an unexpected, you know, a multimillion dollar capital line item that I failed to plan for. And yeah, almost cost me, <laugh> almost cost me that, that particular position. After you have that experience, I think, I think everybody should have to, you know, travel through that gauntlet because it puts you in a position to recognize, first of all don’t manage, don’t manage to the number, manage the business. Had we done, you know, had any of it at any point, you know, you’re, you’re never gonna be wrong managing the business, the bus, if the business doesn’t live up to your plan, then, you know, just go back to the drawing board and do it again. Don’t, don’t try to force the issue, I think is the point I’m making here.
And then yeah, beyond that, every other experience I’ve had in financial planning analysis throughout the 20 years that I’ve been doing this now is be right. Be consistent. Understand what your source of truth is, and make sure that you’re able to communicate that truth across the company. If you, if you have other people that are sharing the data that you’re using, or who are making business decisions based on your source of truth, make sure that you’re communicating that data to them. Make sure that they have access to that data. And more, most importantly, make sure, you know, a lot of things are reliant on logic for us these days, right? So make sure that those people have logic that you’re using in order to get to the answer that you’re delivering. I think the greatest source of stress in my career, ha, has always been ad hoc questions where a a a an executive had a pencil in a, in a meeting and was not able to foot something some random, some group of numbers that they, you know, that was being presented. There’s no, no worse feeling than thinking you’re wrong. So designing your models, designing your reporting to be centered against fixed, you know, trusted databases and tables is critical so that you can say, well, whatever math you’re getting, let’s figure out how you are wrong, <laugh>, as opposed to, let me describe to you how I, I blew it from a, from a core whatever, foundational data standpoint.
Glenn Hopper:
Yeah. And that’s, everybody loves to talk about ai, ai, ai, bi, bi, bi, and, you know analytics. But it starts with, you have to have that foundation of well, so you’re at enterprise level companies. I’ve spent most of my career in the SMB space where, you know, the first thing I I do when I come into a new client, or if I’m taking over as a CFO somewhere, is let’s fix the chart of accounts. So you would assume an enterprise level, they’ve got their chart of accounts pretty square <laugh> squared away in most cases. But even then when you, the, the bigger the company, the more data sources there’s going gonna be, the more you know, sales and marketing may have their own set of numbers they like to use, and finance and accounting uses this, operations uses this. So the first step you have to do is kind of come up with that data lexicon and get everybody nodding their heads that this is our source of truth for this information. So that when the executive is sitting in a meeting and they’ve got some numbers, and they’re told one number from sales and marketing and other from finance, maybe another on the same metric from ops, well, you know, everybody could argue about, well, this is why we calculate it this way. But if you’re gonna label a metric, you’ve gotta have everybody using the same variable.
Len McFall:
Absolutely. I would say 40% of my job’s just touching base with the people who either use or own or curate all, all of the data that we use across our organization. ’cause The fact is that we’re in a, you know, we’re, we’re in a position now where we have a, like you said, a myriad data sources that we never had before. And we’re thinking of ways to use that. You know, let’s talk about big data, right? For a second. Right. You know, I’m in the, you know, I’m in the, in a business where GAS data comes into, into, into play weather, data comes into play. You can start using very small, very granular transactional data to start making statements about your customers, right? Marketing is, is starting to use a lot more intel about not just who our customers are or what interest industry our customers are in, but where are they, right?
What has been their experience moment to moment with our company? Because we, that data exists, for example, I could tell you how many outages any customer has ever experienced, right? In our, say, network, for example. And that, that information alone is moderately useful, being able to tie that information. Y but what if you don’t have that data tied to billing? You could be talking about a batch of customers. You could be talking about a set of data that’s recognizing a customer, or, you know, whatever you wanna call it a location that no longer exists, or that’s no longer billing right outta the gate. You’re wrong if you don’t have all of your sources of data tied together. All of your logic described and laid out and clear for everyone to, to, to use the most important thing that I do, probably, like I said, 40% of the time, I spend making sure that we all agree on logic, making sure that you know, that we are, that all of our sources of data are, or that the ones that I use at least are true and trustworthy.
Glenn Hopper:
I’m thinking about the amount of data you have that’s just available to you and kind of the data maturity levels of these companies in different industries. I mean, working with in all these sectors, how does the financial reporting differ? I, I mean, maybe, you know, because I know you were in a PE-backed healthcare company, right? Yeah, I, I touched too.
Len McFall:
Yes. High touch private equity, and yeah. You know that’s you know, think about 120 doctor’s offices. Think about how doctors have managed data up until, what, a decade ago. This is a big hand cranking file rooms. It’s not, it’s a new, this is, you know, and, you know, not to even talk about, you know, privacy and that kind of stuff. It’s very difficult, you know, just the ETL process for, for, you know the work I did at Crossroads, for example, you know, involved when, when I got there, I spent a Tuesday and a Wednesday, once a month manually getting downloading, you know, the, the monthly data from individual point of service terminals, <laugh>. And then I would, then I would transform that data using a power, you know, using a you know, power pivots. And then I could commence to building, you know, my hundred tab you know, p and l. So, but go ahead.
Glenn Hopper:
But you ended up there, and maybe this is, maybe we come to this in a minute, but you ended up actually building a, a decent team there did. And you actually had data scientists. Yes.
Len McFall:
Yeah. Well, that, you know, showed up and there was a cave, and we were doing, you know, look, fundamental fp and a can be done with sticks and rocks, right? We all kind of agree that, that we <laugh> that. It’s not a, a complicated thing, but it is labor intensive. And so, you know, I got there, it was sticks and rocks, and it was very, very rudimentary. And, you know, we, it’s inter, I, I don’t know. It’s very interesting. I don’t know if you, you’ve continued to have this experience through your career, but I feel like every time I show up someplace, it’s a target rich environment. And that’s exactly what kind of happened with Crossroads. I showed up there and they were like, please, for the, you know, please help us <laugh> so that we can, you know, so that we can just get, they were, they were delivering finance reporting 30 days after close 30 days.
And we, you know, we chopped that down. We were delivering on day eight, you know, within, within a few months. You know, I got there and it was a matter of, and look, I’m a Luddite. I’ll be the first one to tell you that. I’m not super interested in ETL. I’m not super interested in how, where the data lives, what the, you know who, who, who, who, who owns it. I just need to, you know, my, my my my goal is always to have access to it. And I know, you know, the nice thing is, is that you know, that it’s a thing that you only have to suffer once. And so that’s what we did. We suffered once we got you know, we, we hired, the first thing we did was hired sort of a contractor who worked for us part-time to develop an ETL process so that we were, so that we had access to all the data that was sitting in all of our point of, you know, point of service machines.
And then we hired a a double master like not, you know, she was a, an MBA and a, is it, what do we do? An md MDA? She was a ma master’s in data science something, data analytics with two good, she had two good degrees, and she was you know, she was a power BI person, and it was a matter of <laugh>, you know, we start, we started building automations on day one, standing at a, at a whiteboard. And we were peeling time out of the process very quickly, and of course, making it essentially error proof. We weren’t doing, you know, any of this sort of crazy, you know, we won’t get into, you know, managing the balance sheet. But, you know, for, for <laugh>, for, for p and l and for, for, you know, planning and analysis and for, for modeling, it put us in a position where we could model, which we, we, you know, we really, we really could not do that before.
We could answer ad hocs in a timely manner. And I’ll say, you know, I, I, I think, again, sure, I’m, I’m in, I’m in paragraph land, but you know, the, the power to me of, of access to data is the power to answer ad hocs quickly. You know, like I said, financial planning, we, you know, you can, you can build a model out of anything stick, you know, a very basic stuff for modeling. The point being, you can model with Excel and a stack of papers if you have to. But when it comes to real time decision support or ad hoc analytics, you have to have access to good data where you’re gonna, you’re not useful.
Glenn Hopper:
So you, I mean, you basically just described, you know, the if you’ve seen the data maturity scale, where it starts out where, you know, we’re doing ad hoc reports in Excel, there’s no notion of self-serve anything. Mm-Hmm. <affirmative>, everything is reinvent the wheel every time there’s a, a new report request. And it’s, that is a tough place to do fp and a, but I, I kind of carved a career out of being the guy that goes from, you know, takes the company from low data maturity to reducing that close cycle time. Yeah. From from 30 day. And that’s, that’s pretty standard in the SMB space. It just, it’s kind of an accepted thing, 30 days. But think about how, how much less valuable your financial data is if you’re not even looking at it until 30 days after the fact. I mean, that’s, it’s, you can’t,
Len McFall:
Yeah. It’s not useful. You know, you’re, you’re you’re, you’re, you’re attacking pain points from let you know, talk, think about cost for a moment. You’re, you’re attacking cost pain points way past the time that it’s useful. Right? Or it certainly a di you know, has a diminished usefulness. Yeah. If you don’t have, if you can’t get out of the gate, you know, you’ve gotta, you’ve gotta have, well, and whatever, it’s fundamental stuff, you know, the world closes in three days now. That’s just the way it works. And then, then you can start to talk about, you can start analyzing the month on day three. That’s when I plan to do it. And, you know, any place that’s, I don’t wanna put anybody out, you know, put anybody on blast here. But if you’re not closing by the middle or the end of the third day you’ve got an opportunity, I think, regardless of the size of your company,
Glenn Hopper:
Obviously you want that close time to be as short as possible. But now there’s this concept of, you know, the real time close, which I know we can’t, you know, tick and tie everything with, with the books and everything, but sort of, you know, there’s software out there that helps with it because people say, you know, I don’t wanna wait till the end of the month. So what data points can you provide? You know, and, and this is more of like a rev ops kind of reporting requirement, but it’s you know, there is that expectation. And if the comp, if companies have some level of visibility throughout the month in between the close, that’s just more, more valuable and, and supports decisions.
Len McFall:
That’s not something that, it’s in the conversation right. In my life now, but it’s, it’s interesting to me because I, I say this all the time when I’m talking to people about building <laugh>, about modeling, about anything, about about, you know, the realtime close thing. I think if in a world where human beings don’t exist, it makes a lot of sense to start jumping the gun on certain metrics. But you know, I I, I work with sales a lot, and those guys won’t close. You know, you’re never gonna <laugh>. There’s no way. And I’ve tried it. I’ll tell you, I’ve tried it in a couple different, with a couple different firms. I’ve tried to model forecast on run rate, which you cannot, you cannot do from a timing standpoint. ’cause The, I don’t know, they’re human beings, man. I can, for, we can forecast in a lot of other ways, but just not on run rate, not on, not on, not early, I guess is my point.
I have never gotten a pipeline report from sales that I believed, oh, I don’t, <laugh>, I don’t, I’m glad you brought that up. It’s fi I’m, so, I’m so happy to talk about that, because it’s something that flashed through my mind as I was going on my last rambling, which is, let’s, let’s set this aside, modeling co complexity. Maybe there’ll be some time for us to talk about that in a minute. But you know. Yeah. how, how often do you catch a forecast from a sales organization that’s even 50%, right? Never. Never. I never have. And I’m not, you know, if there’s some folks that are listening that are in sales organization, I think they own it. I think they, they, they know it, they are overly optimistic or overly cynical, depending on, I don’t know which way the wind is blowing, frankly.
It’s very difficult for them to forecast. In the meantime, I have, and I do it all the time. I have the most rudimentary model, which is, Hey, <laugh>, let’s look back at the pipeline. Let’s look at close rates based on, based on stage for my CRM. Let me put those in five buckets, right? Of like, of, of weights, right? And let me build a weighted pipeline, and from that weighted pipeline, I’m gonna build a forecast for this quarter. And, and let me say something, month to month, I’m within a dime. I’m within 10%. And never, never have I been beat, ever. Not even close by the sales forecast, which is a forecast presumably from the people who are directly involved in the pipeline moment to moment that we’re literally going to them and saying, tell me what you’re gonna sell this month. And they’re wrong every time.
And so this is sort of the power and importance of very, you know, this is a very rudimentary, very simple model. Imagine how close you could get if you had a lot more data to throw at it. And that’s another question. Maybe you couldn’t get any closer at all. Because again, we’re talking about people and companies who are out there doing business and dealing with the same stuff that everybody else is dealing with. Maybe 10% is as good as it gets, and maybe it doesn’t make sense to, I don’t know apply you know, a weather based regression model to your electric bill to see, you know, <laugh>. But maybe it does. You just don’t know until you do it for a couple months in a row, right?
Glenn Hopper:
Yeah. And it’s you know, thinking about that, it, the, I feel like the sales pipeline report that comes in, it’s based on a lot of just emotion. It’s based on, they really liked me. I’m pretty sure they’re gonna close this. It is. And
Len McFall:
Absolutely qualitative person to person, and yeah, they, they can’t know. But you can take that data, and this is small data. This is not, you know, this is not a huge, you know, you’re barely sort of statistically <laugh> significant. You can still prove in a hunch, or you can still at least call you know, call shenanigans on, on a forecast. You know, I don’t know how many times I’ve seen a forecast come in for, you know, number X and I’m going I don’t wanna burst anybody’s bubble, but that’s not mathematically possible.
Glenn Hopper:
<Laugh>. Well, and the mathematically possible thing. So, I mean, I keep, you know, as we’re having this conversation, I think about like the, the qualitative part that where, you know, people guess wrong. They read, read things wrong, and, and that jacks up the pipeline.
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This is something you and I have both preached for, is the role of data analysis and bi in, in finance, and in the decision making process. And when you are doing fp and a, then the value that you give to the organization is not, I mean, obviously being able to model and all that is, is, is key and great, but it’s the communication of what you created and being able to, you know, and more and more is getting automated from the bottom. You know, you have all this, this wild amount of data, and then, you know, you turn that data in information, you log it, you capture it, and you know, catalog it, put it in the data warehouse or whatever. But then what, what do you do? You have to turn that information into knowledge, you know? So now I’ve consolidated, I’ve made a dashboard, here’s your quick look. But then where we start now, because there’s, there’s been so much automation at the lower level is okay, we take that knowledge, turn it into wisdom, in that now I’m adding my value as a fp and a expert to tell you to make, so you can
Len McFall:
Make strategic decisions based on all that, what, what we’ve gotten to hear. So yeah, what, what good is there? You know, you can have a hundred Tableau graphics, and there are no good if nobody can understand them. I feel like we’re in the infancy of what is a future of real communication up to the business, up to the business owners. ’cause You know, like I said, it’s like you, the point I think you’re making is information is useless if you can’t communicate it to the people who need to use it to improve their decision making. You know, and, and look, business is a hunch, right? Business, we, I, I think we, you and I were talking about this the other day. We were talking about you know, how, you know, ai, it commoditizes experience, right? So businesses run on hunches. So I, I have a hunch that a particular initiative is going to work.
And our job as fp and a folks, and more importantly, business intelligence folks or decision support people, whatever you wanna call it, is to buttress that hunch is to, is to prove that hunch. So, yeah communication of all of this stuff is absolutely the key, the crux, the, the future. ’cause Honestly, I’ll, I’ll tell you, I experience a lot of decks. A I see a ton of slides and of readouts and reports, whatever you wanna call it. So many pie charts. Look, man, I, I don’t even, you know, <laugh> a lot of pie charts. Yeah. Too many pie charts. But, you know, hey, man, you know, if I will say this we, we all have an aversion to pie charts for, for whatever reason. I’m being a little tongue in cheek here, but a a line, a line over stacked bar can be just as worthless as a pie chart if it’s not telling you anything, or if you can’t communicate what the, you know, what it’s showing.
I don’t know how many times I’ve seen a stacked bar or three bars side by side with two trend, you know, two trend lines and a long rhythmic curve. And it’s like, this is a useless graphic. If you have to spend 12 minutes telling me what in the hell, pardon my French is on this graphic, you’ve wasted my time. And you’re, you have not brought, you have not brought me any, any value, frankly. And that’s something that we all as business intelligence people to decision support people, financial planning and and analysis, people, we have to guard against, which is bringing nonsense to the business because it’s easy to get down in the weeds and to not, you know, you can have the great, you can, you know, recognize the greatest you can have the greatest epiphany, but if you can’t communicate it clearly to the business and to the owners of the business or the, the, the, you know, the folks who are responsible for any given business unit or initiative, it’s not, it’s not useful intelligence. It’s not useful bi, it’s a waste of time.
Glenn Hopper:
I think where one of the areas I wanted to cover is, you mentioned this at the top of the show. I think you called yourself a Luddite, where you’re, you don’t want to be a tech person, but the nature of the business, the way it’s changed since you and I have been in it, is you have to be a tech person. So you have to go, you know, first you have to learn how to write a SQL query so that you can get access to information. And then there’s another tool and another tool and things you have to learn. But to add value to your job, you have to become, you know, you, you have to grow your bi chops because it’s Mm-Hmm. <Affirmative> you know, the value that you provide is gonna come out of that. So I think it’s, it’s tough when you go to school for a thing for finance, and then, oh, by the way, it’s just expected that you can code a little bit, and certainly you can write a little python, right? Right. And certainly you can write a, a SQL query or, you know
Len McFall:
And the answer to that question is yeah, <laugh>.
Glenn Hopper:
Yes. Because I don’t know if you can’t do that now. I mean, unless you’ve got the coolest drag and drop interface in the, in the world
Len McFall:
Is there anybody in our business that can’t at least dip an Oracle database used in sql? Right? We, we, it, to me it’s kind of like it’s kind of like typing. I, I, I expect, you know, I don’t know. I think my kids missed it generationally, but I, I, I feel like there’s ninth graders out there. You know, you and I took keyboarding classes in ninth grade. Right. You know, we, we learned how to No look type. And our kids are learning how to code. I th I think
Glenn Hopper:
Looking back to when you first started your career, I, I understand we have to learn all these different environments Yeah. And, and different coding ways and everything, but the nature of what you even had access to in the tools that you had early career. Walk me through sort of the evolution of what you’re using. And, and you don’t have to specifically, you know, name brand. I mean it’s fine, but you know, what technology you have now compared to what you had when you first started, and maybe some ways that that’s facilitated the the work that you do, or I wanna say take not facilitated the work because the work is still challenging, but maybe it eliminates some of that, the grind of, of just, you know, being lost in Excel and <laugh> Yeah. Hammering through and first
Len McFall:
And foremost sort of, you know, ETL, you know, getting your hands on any data from any system, you know, is much easier now than it was in the past. I think, you know, when you and I were doing this back at the turn at, you know, at the Millennium <laugh> we were ODC two to you know, to our, you know, whatever our financial was at that base, at the, no, I don’t even think you can, I dunno what it was, but, you know, <crosstalk> Great Plains. Yeah. Great Plains. That was it. Into an access database. And we’re sort of learning gui, right? We were building queries in a GUI in Microsoft GUI interface, and we made some powerful tools out of that stuff, and changed the way that, that our company was able to manage. Its you know, to say the least cap, you know, complicated capital planning situation.
’cause When you’ve got little pieces of capital all over the country, it’s difficult. And we, I, you know, we, I think we did great works with, you know, very little that said, that was a time, you know, at that, you know, so the difference between then and now is that then e even that rudimentary access to financial data was still sort of, you know, good access to financial data. And the difference between then and now, the real primary difference in my mind is that it’s not just financial data that we have access to. Now we have access to s sales data, marketing data, big, you know, we’ve got access to bi you know, to, to the, to the larger world of big data. We’ve got access to you know, in telecom, maybe it’s switch data, maybe it’s net traffic data.
Maybe it’s, you know, we have access to correlating data that enriches our, the finance data that we’ve always had access to. And so that is a game changer because now we can start making much more powerful guesses about, you know, what’s gonna, what’s happening with our customers, what’s happening inside our business you know, on a cost basis, on a, on a sales basis all of these things. So, to me, the big difference between 20 years ago and now is that we are able to, to join to data that we didn’t have access to before. I suppose it was probably there, it just wasn’t as easy, you know, I suppose we had, we could have, if we really wanted to, figured out a way to get ahold of switch data engineering data, the good news is, is that it’s easier to get connected to disparate many multiple disparate sources of data.
The other news <laugh>, is that it moves us closer to it, which traditionally, right? It has sort of weirdly fallen under the, you know, the, the provenance of the CFO, right? You know, back before it was its own thing, back before CTOs existed, or CIOs or whatever you wanna call them. I think an opportunity that, that we all have going forward is, you know, trying to find the correct interaction between it and business intelligence and finance, because it is, it is kind of a brave, a brave new world, right? It sees the kinds of things that you and I are, are doing in, in business intelligence. And they’re going, that looks an awful lot like it. That looks an awful lot like information technology. Why don’t we own this? And
Glenn Hopper:
That, and there’s a whole, I mean, there’s a whole debate around that Yeah. About where should data science live. And then, you know, of course I’m a homer for, for finance. Yeah. And I think, well, to your earlier point, we are the original business analysts, and that’s why I’m so mad. How did sales and marketing just like a rocket ship takeoff in using machine learning? And <laugh> finance and accounting has been left in the, in the dust on that. And I think we’re getting there more now. And I think now though, you’re seeing more machine learning just being built into other tools, other software that we use. It’s just different ways to do the, the forecasting. But I kind of was miffed for a long time that I felt like, you know, the best machine learning resources were being put to sales and, and marketing. And but I also argue all the time that you
Len McFall:
Want, you want the real answer?
Glenn Hopper:
Yeah. Well, actually, I think I know, I know what what you’re gonna say, but yeah, let’s, let’s
Len McFall:
Hear it. Marketing professionals are like weathermen, who, who, who was using big data first? Meteorologists. Why? Because they don’t know which way, literally the wind is gonna play <laugh>. And when you are in the business of being close and you don’t have, you know, again, sort of in more, you know, in a more serious note, <laugh>, you know if, if, if your be, if the benefits of your activity are a little softish, then you’re gonna start, you’re gonna employ tools that add a little bit of hardness to the benefits that you are delivering to the business, or, you know, suggesting that you’re bringing to the
Glenn Hopper:
Business. And since we’re dogging on people sales and marketing, you know, I, I’m, I’ve, I don’t trust their pipeline. I’ve never, like, I’ve, I’ve been frustrated, but never actually like angry, ready to punch someone in sales and marketing it. On the other hand, I just come, I just come to a meeting with it, just ready to fight. Yeah. I’m just <laugh>, let’s go. <Laugh>.
Len McFall:
It’s the nature of the relationship. I’m not sure why it’s that way. I think it’s that way because well, I, I, I don’t wanna blame, look, the way that it gets done, dev cycles and sprints and all of the things associated with, you know scrumming and agile approaches and all these, if, you know, it spends so much time trying to manage throughput for limited resources and, you know, managing intake and that kind of stuff. That’s you know, frankly, finance professionals, we don’t, we don’t have, we don’t have time to participate in your intake process because we’re beholden to the closed timing, right? We, we live period to period. If you can’t help me this week, then you can’t help me, man. And often that sets up an immediate problem, an expectation versus reality problem when it comes to getting to getting work out of it. Not to mention they don’t, you know, they often don’t see, well, I think that it sometimes sees finance centered business intelligence folks the way that finance centered, business intelligence folks see market analysts.
Glenn Hopper:
If somebody wants to be at the forefront of fp and a starting out their career, what are you telling them that they need to study and focus on right now to be successful?
Len McFall:
Yeah, I think, well, I, I do think you, you focus on analytics, you focus on you, you, you find the bleeding edge in terms of tools and processes that are being used right now. And you know, to the extent that you can be, be interested in that, be curious about that and stay in front of that. ’cause The fact is, is that, you know, you’ll get, you know, financial literacy obviously is critical, but for, from a business intelligence standpoint you know, if you’re talk, if you’re getting your education, the business intelligence part of the analytical part of your, your work is gonna be more and more as time goes by, more and more centered on your knowledge of the industry, of your particular business. And you’re not gonna get that knowledge until you’re in the, the business. So go study tools, go study the technology and show up to whatever business you choose or where, you know whatever business you get hired on onto with a toolkit that you can apply to that business and that business’s industry and customers right out of, right out of the gate.
I think, you know, a very, you know, the, the days of, I don’t know, you know, like, you know, like the days of MFA and that kind of stuff. I don’t, I don’t think that’s a useful educational track now, a a as much, especially if, like I said, if you wanna be in the, the business of business, you need to yeah, embrace the tools first and foremost, and I know that everybody’s doing that. You know, e every undergrad is coming out of, you know, coming out of their business undergrad program with a, you know, knowledge of the, you know, they’ve got Power bi, they’ve got Tableau. They understand the basics of data and, and database structures and that kind of stuff. That to me is the, the bare minimum. And if you wanna make yourself, you know, if you wanna elevate yourself, get curious about that stuff and get out on the, like I said, out on the bleeding edge of that stuff. ’cause That’s where, that’s what everybody’s looking for.
Glenn Hopper:
I look at it like this, the, the role of the CFO, and then therefore, all the departments under the CFO have changed significantly from what, just let’s say 20 years ago, maybe a little longer, but 20 years ago is probably accurate. If you’re a CFO, you probably are A CPA. You came up through audit and you went through a controllership. And the most important thing for the CFO to do was be able, and this is still important, but I’m, I’m gonna get to that in a second, was to be able to close the books and speak to the numbers. And you were, it was very backward looking. It was, we’re gonna close where everything’s gonna be ticked and tied, and that’s it. So what’s happened in the last 20 years, what used to be the CFO is now what the controller does. So the controller has elevated as well.
But then on the CFO side, you had to lean in a lot more to the fp and a, to the forecasting, to the strategy. And it, you could, and I used to say this because I bounced around a lot of industries as well. I’d say, I don’t care what the widget is, just drop me into finance and I’ll, you know, drop me into finance and accounting. I’ll figure it out numbers and numbers. But, so now I think with the evolution of the role, you have to have, you have to have the basic accounting and understanding of just the chart of accounts and how everything works and how the three financial statements flow together. And you’ve gotta have the domain expertise, well, you know, the training to speak the finance lingo and understand all the components there. You also, you come in, you have to understand statistics analytics, BI and the tools, because are you gonna, are you gonna do this all in a calculator?
Are you gonna do it in r you know, you have to be able to use, operate in the environment that the company’s using. So now you come outta the gates, you’ve got, you say, okay, I have a master’s degree in, in finance to understand all this. Now I have a master’s or, or, you know, at least coursework in data science and statistics. And now I, I know enough and now I’ve gotta go out and apply that in the world. So it’s really, it’s a huge ask right now. But one of the things that I think is gonna make that easier is the whole world of data science used to be blocked off by SQL at a minimum, hopefully also, you write Python or c plus, you know, whatever your whatever your programming language is. But now what I, what I’m seeing early days of generative AI is natural language becomes the new programming language.
So as long as you can get, and it’s, and you know, we’re just a matter of a couple years away from this, but all of your databases, all of your big tools where you’re handling this data are gonna have an interface that is, let me talk to my data and let me pull it this way. Mm-Hmm, <affirmative>. So, but now I’m rambling. But, but you have to just, like, in, in an example I always use is if you are going to be talking about the income statement, you need to know the difference between net income you know net income, ebitda, you know, all the different points there. Otherwise you don’t know what you’re talking about. Now you’re gonna have to understand the fundamentals of of statistics to understand what you’re talking about. But as far as the coding, that’s not gonna be a barrier.
It’s if, you know, and you can interact with data through natural language, and you can, you know, you know how to test and, and see if you seasonal auto regressive, integrated moving average forecast is I can’t believe I spit that out without stumbling. But to be able to understand how it works and, and what you’ve asked the AI to do I mean, I think maybe that’s gonna level the playing field a little bit, but I think it asks for the same things that you said that what you should be studying right now. Maybe you don’t have to know the coding, but you’re gonna have to know the rest of it.
Len McFall:
Yeah. Yeah. I think that, yeah, that’s the thing. You’re, everybody’s, you gotta chase, like what’s the minimum education that you need? What’s the minimum tool toolkit that you need? And that, like you said, that now is, you know, sql, python, you have to, you know, understand the, the tech, right. You, you have to understand it. And then, yeah, the, the next thing is, I, I don’t like to use this because so many people use it the, these days, but, you know, sort of the next step of programming is, you know, prompt engineering, which I, I, I really don’t love that phrase because what it, what it suggests to me is that, and maybe this is the case, but what it suggests is that there will be, you know, that generative AI and interaction with generative AI will become codified just like any other any other language.
Glenn Hopper:
So I’ll tell you, I <laugh> as a, and this is funny, this is someone who just for a group wrote a guide. I mean, just within the last 24 hours, wrote a guide to prompt engineering. However, I get angry when I hear someone talk about you know, I need a prompt engineer, or I’m gonna be a prompt engineer, or I think in what’s gonna happen is it’s actually gonna move in the other direction, and within the next 12 to 24 months saying I’m a good prompt engineer is gonna be like, bragging that you’re really good at Googling. Yes. You know, it’s like, it’s, because that’s the, the nature of these large language models is that they interact like humans. So it’s more of a, an approach to how you interact with them than it is specific prompts or whatever. It’s, yeah. I think of when I work with generative ai, my default is, this is a very bright but very green intern. Mm-Hmm. <affirmative>. It can do anything, but I gotta put guardrails around it and I gotta say, listen to me, <laugh>, this is what I want you to do. Alright, Lenny, we’ve reached the time of the show where I’m gonna learn a little bit more about you. I bet this is, we’ve known each other long enough that I, I probably will, will know what you’re talking about when you answer this, but the, the standard question we ask is, what is something that most people don’t know about you? Right? we could turn this into a real confessional, we could say, what is something I don’t know about you, but maybe we stick to <laugh>. I’m gonna
Len McFall:
Off the book man book. Here’s something that might surprise you. If you’re, if you can see the video of this podcast I don’t play the guitar very well. <Laugh>,
Glenn Hopper:
He’s Lenny’s sitting with three guitars behind him hanging on, on the wall here in his office.
Len McFall:
I think you know this about me, but a, a thing that a lot of people don’t know about me is that you know, I my background is you know, my childhood was a countryside farm-based, north, northern end of the Appalachian mountain range kind of childhood. So I, I, I, a lot of people don’t know that I grew up in the woods, like the very deep dark forest <laugh>. No, nobody, nobody, nobody’s, I don’t strike people as the kind of guy who, who killed for food every winter, you know, as a child. You know what I mean? You
Glenn Hopper:
Didn’t, you didn’t carry that with
Len McFall:
You, huh?
Glenn Hopper:
Killed animals. Yeah. Animals. And that, that the, that didn’t carry into your adult life. You’re not a, not
Len McFall:
A big I’m not, no, I’m not between Hunter. No, it didn’t. I, I left when I left the woods. I, I pro, I, I essentially left all that behind <laugh>.
Glenn Hopper:
Alright, Lenny, next question. Mm-Hmm, <affirmative>, what is your favorite Excel function and why?
Len McFall:
Can I, can, can, can we talk about, it would be more on theme for us to talk about the shortcomings of Excel at this point, don’t you think? <Laugh>, can I, okay. Can I complain to you? Would you, is, are you a sympathetic ear when I say why in the world can I not get an easy quarter to date function in Excel? <Laugh>,
Glenn Hopper:
I’ve, I’ve given up on any sort of like getting any in, in Excel, getting anything to today’s date or trying to get, you know, count days or whatever.
Len McFall:
I have gymnast offsets and matches any, I’m gonna, I’m gonna, I’m gonna jump off bridge. It’s the, the most annoying thing that I do. And it’s funny because I don’t know if you’ve experienced this in my career, I don’t do it anymore. I kind of got past the trying to not do the hard stuff at some point pretty early on in my career. But, you know, I can remember times when it was like, you know what, I’m just not gonna put quarter to date on this report and see if anybody cares about it. And of course they care about it. The first thing that somebody sees when they see financial document with wi with, with a stack of periods on it is, can I get quarter to date? How about quarter over quarter? How about year to date? How about same period last year? How about against against end of year last year? And it’s like, because there’s no good function every time that happens. It’s like, what? You know, again, you know, my mantra is if you only have to do it once, it’s not really a, you know, it’s not a problem. Otherwise I just use some if and many, many various lookups and nested if statements. You know, you and I broke the nested if statement back in the day, went back when it maxed at five. Nested if statements
Glenn Hopper:
I’m just getting started at five. Alright. All right, Lenny. Well thank you for, for coming on the show. I guess last question how could our listeners get in touch with you? How, how can they reach out to
Len McFall:
You? Well it’s, it’s nice because I’m old enough to have one of the earliest invitations to Gmail, so you can just send it to Lenny McFall at Gmail. My actual name, L-E-N-N-Y, YMC A at gmail is the best way to reach me. I it’s, that’s the only mailbox that I monitor. So,
Glenn Hopper:
<Laugh>
Len McFall:
And yeah, this, thank you so much for having me on. This was a a lot of fun, obviously. It’s always fun to hang out with you and, and talk.
Glenn Hopper:
Thank you Lenny and thank you listeners for tuning in.
