The Future of Financial Modeling

Written By Ankita Ghosh

Show Notes

Welcome to the Financial Modeler's Corner (FMC), where we discuss the Art and Science of Financial Modeling with your host Paul Barnhurst.

Financial Modeler's Corner is sponsored by the Financial Modeling Institute (FMI), the most respected accreditation in Financial Modeling globally.

George Mount, Excel MVP and data analytics expert, with an extensive background in finance and data science, shares his insights into the evolution of Excel, the integration of Python, and the importance of data analytics for financial modelers.

Key takeaways from this week's episode include:

  • Excel's flexibility is both its greatest strength and weakness. While it allows for complex modeling, this same flexibility can lead to chaotic and error-prone spreadsheets if not managed properly.

  • Today's Excel is vastly different from the Excel of a decade ago. With features like Dynamic Arrays, Power Query, and the integration of Python, Excel has become a powerful tool for modern data analysis and financial modeling.

  • Python’s capabilities in handling time series analysis, data visualization, and complex computations complement Excel’s functionalities, making it a valuable skill for modelers.

  • Excel excels in prototyping and wireframing due to its immediate data visibility and user-friendly interface. This makes it ideal for quickly iterating models and understanding data before moving on to more complex tools when necessary.

  • George shares advice on how to get started learning Python and other analytical tools to take your analysis and modeling to new heights. 

  • A core principle for George is the importance of sharing what you know about Excel and data analytics. He emphasizes building a culture of knowledge sharing to foster growth and efficiency within teams.

Quotes:

Here are a few relevant quotes from the episode on financial analysis and modeling:

"Excel’ greatest strength and weakness is its flexibility.”-George Mount

“Excel is not the Excel of ten, 15 years ago. I often like to kind of think of it as modern Excel with Power

Query, Power Pivot, now Python, Dynamic Arrays, LAMBDA, LET, and other things.”-George Mount

“The first rule of knowing Microsoft Excel is not to tell anyone that you're good at it.”-George Mount

In this enlightening episode, George Mount provides valuable insights into the evolving world of Excel and data analytics. By integrating new tools like Python and embracing the advanced features of modern Excel, financial modelers can significantly enhance their analytical capabilities.

Sign up for the Advanced Financial Modeler Accreditation Today and receive 15% off by using the special show code ‘Podcast’. Visit www.fminstitute.com/podcast and use code “Podcast” to save 15% when you register. 

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In today’s episode:

[01:06] - Introduction;

[02:14] - Worst spreadsheet model horror story

[04:49] - Importance of achievable modeling in Excel

[05:16] - Guest Backgrounds

[08:57] - Combining data science and data analytics in Excel

[11:01] - Necessity of learning Python and R

[17:19] - Recommended resources for learning Python

[20:27] - Exciting new features in Excel

[22:25] - AI's role in Excel and financial modeling

[25:55] - Favorite function in Excel

[29:00] - Rapid fire;

[37:08] - Get to know the guest and Conclusion

Full Show Transcript

Host: Paul Barnhurst:: Welcome to Financial Modeler's Corner, where we discuss the art and science of financial modeling with your host, Paul Barnhurst. Financial Modeler's Corner is sponsored by the Financial Modeling Institute. Welcome to Financial Modeler's Corner. I am your host, Paul Barnhorst. This podcast is where we talk all about the art and science of financial modeling. With distinguished guests from around the globe. The Financial Modeler's Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling. This week, I'm thrilled to welcome Excel MVP, and all-around good guy George Mount to the show. George, welcome.

 

Guest: George Mount:: Hi, Paul. Hi, everybody. Thanks for having me.

 

Host: Paul Barnhurst:: Yes, no, excited to have you. I know you and I have got to know each other over the last couple of years. Will be fun to have you on the show. So this is the question I ask everybody to start the show. Tell me about the worst spreadsheet model you have ever seen. Tell me that horror story.

 

Guest: George Mount:: Yes, I think this is what set me off into the Excel world. When I was working in healthcare I was responsible for physician incentives and physician compensation. Physicians aren't exactly known as easy people to work with, and I think everybody is sensitive about their paychecks. So it was a worst-of-all-worlds scenario where it was all done in Excel, and I had to learn a lot about Excel to do it well and to get people paid on time and in the right amount.

 

Host: Paul Barnhurst:: Yes. I think you and I could talk for a long time about commission file horror stories. You'll laugh at this. I took one over, and I took it over from a guy who had left the company. The thing was just a complete mess. Hard-coded numbers, copy-paste this, sort it this way, and then do colors for the top ten. I don't know. That's just the way we do it. It's a horrible process and I'm starting to try to fix it all. My boss hadn't looked at the files the whole time this guy owned it. He opens it up and he calls me freaking out. He's like, this is going to fail an audit. I would have failed an audit for the last two years. I'm trying to fix that. You're going to have to give me some time, but I paid some wrong. People had some real pain. So I learned a lot in Excel, just like you said, trying to do commissions.

 

Guest: George Mount:: Absolutely. Mine was the same. Just the lack of rules, a lot of exceptions that weren't documented, and no real such thing as any data integrity, just kind of a kind of free-for-all. I guess that gets to excel, the strength and the weakness is the flexibility. I think that that showed it right to a tee because when you're dealing with something like compensation. I understand it's a very complex matter, To pay people. So sometimes you do it for the sake of the business, want to develop different rules and strategies and things like that. But you need to model it in a way that can be achievable. I think that that's talk about sometimes a disconnect between management and then the person with the spreadsheet. It's easy to come up with some brilliant, complex compensation model. But then like, hey, how are we going to model this in Excel and do it well, Nobody often thinks about that. So it's good that there are resources like yours for the people who have to put rubber on the road and build these models and execute them.

 

Host: Paul Barnhurst:: We've all been there where you're just like, would you please think about the data you're asking for in this compensation plan? You know how much work that is. There are some that try to come up with. Fortunately, I was involved in that side of as well in there a few times. Like, Yes, you're going to need better data before we go down that rabbit hole because I'm not going to be calculating those. So I get it. So why don't you tell our audience a little more about yourself, your background, and what you're doing today?

 

Guest: George Mount:: Yes. So as you mentioned, I am an Excel MVP. I do a lot of training when it comes to Excel and more. So the analytics side of Excel, whether that's getting into Power Query, Power Pivot, or more recently topics like Python and Excel or AI. I'm the author of a couple of books, and of course, I have my props here. The first one is Advancing into Analytics through O'Reilly. This one was my first on learning Python and R, mostly for statistical analysis outside of Excel, with an intro to those topics in Excel. My most recent one is modern data analytics in Excel. This one is your 101 guide to Power Query, Power Pivot, and then the kitchen sink of analytics tools that are now in Excel like dynamic array functions, Python, AI, and all that business.

 

Host: Paul Barnhurst:: I own both books. One you sent me the digital copy I had a chance to review, and the first one I bought, and I will say they're great books. That's one of the reasons I wanted to have you on the show, and we'll get into just what our audience knows is, It'd be great to talk to someone more about Python and data analytics because there's a convergence. Financial modelers are often doing data analytics to a certain extent. They're starting to figure out, okay, how do I use Python to clean the data to be better at what I'm doing to augment the visuals, the model, whatever? So I feel like it's important for them to hear from somebody who's an expert in that area. But before we jump into some of the Python R and Excel, you started your career in finance. You didn't last long. Can you talk about that? What happened? Come on. We want you to be a financial modeler with us.

 

Guest: George Mount:: Well, so I do have a master's degree in finance. I feel like I can run with the crew a bit. I started as an FP&A kind of guy. That's where I was doing some of those compensation models. I think it was the lack of tech-savvy and just overall apathy toward doing things better, more efficiently, and more robustly. Had thought about going back to school for something a little more on the techie side, which is what I ended up doing. That's where I started getting more into things like Python and R. So I think I've been able to take those two paths. And find my third way, right where I do use Excel. I do understand finance and FP&A, but I also have a foot in the door when it comes to data science, AI, Python, R, and all that stuff, too.

 

Host: Paul Barnhurst:: I think it's a great path you've carved out. I think it makes a lot of sense, and I kind of say it tongue in cheek, the whole finance thing and abandoning us. But I get it. I started my career in FP&A and the apathy studies have shown right now, the department that's furthest behind in technology and using AI is generally finance. There are some reasons for that risk-averse. The fact that everything goes public, like if marketing makes a mistake with an AI, okay, a campaign went wrong. If you post something that goes public because you used AI to help generate it and it's wrong and the stock drops 10%, that's a big difference.

 

Guest: George Mount:: Yes, Yes, that's true.

 

Host: Paul Barnhurst:: I get some of the reasons, but nonetheless, I think people need to be more open. We need to advance so that finance doesn't always feel like it's a little behind on the tools. So what I wanted to ask you is I know you, as you said, focus a lot on data analytics. Would you consider that kind of your area of expertise at this point, or where would you say, if you had to describe yourself? You mentioned, hey, I got a little bit of Python, you got the finance background, how do you kind of define yourself to people? And they're like, okay, what? what do you do? What's your area of expertise?

 

Guest: George Mount:: I think what I had been doing in the past was explaining that you think about data science as this exciting, glamorous new profession, and then you have your data analytics that might be not as glamorous, but meeting in the middle of those two. And finding the best of data science and what's possible with Excel for data analytics and helping people build things like an ETL process in Excel, which sounds complicated, but that's just Power Query. Or doing a relational data model, which also sounds complicated, but that's just PowerPivot or Dax, which I guess is complicated. But so that was what I was focused on now. But I think I'm expanding that out with what we're seeing in Excel, which is there's this new cool field of data coming out now called data engineering. Which well, let's take a step back and think about our data quality, how we get it, how we build our pipelines, and things like that. We can do that in Excel now too. There's more opportunity to do that with things like power automation, office scripts, all that, all that stuff. So I think where I'm going forward professionally, but I think Excel is a toolset. It too is, um how can you be a data scientist, a data engineer, and a data analyst in Excel, and looking forward to exploring how those fields kind of interconnect right inside of a spreadsheet.

 

Host: Paul Barnhurst:: It's exciting times. when I did my undergrad, I did a master of science information management, and I wish they now have. Instead of that, the primary focus is the data analyst degree kind of data science data analyst combination. I'm like, I wish they had that when I did my MBA. It became big kind of early 2010. I graduated around oh eight. So most schools didn't have a dedicated program at that point. So I hear you, it's very exciting to watch. Speaking of that, I know you've learned Python and R, if I can speak, and you're also a big Excel advocate. So why did you feel it was necessary to learn Python and R if you were already good in Excel and knew Power Query and we're using that a lot?

 

Guest: George Mount:: Yes, I think in the beginning it was just like pure trial and error. I mean, there was a time that I was trying to learn Ruby and JavaScript and like all these languages because I had no idea what was going to help my spreadsheets. I just knew I needed help I kind of mucked around a little bit and accessed, databases as well. So it was just purely explorational. Then once I started thinking, well, maybe I want to go into grad school, I was in like a very quantitatively heavy kind of a program where learning things like Python and R just for that next level of statistical analysis that I don't think a lot of people in the business world need to know. But If you are going to build a regression model and you want to check your assumptions and stress test it and things like that. Maybe some of your listeners do things like that. But Yes, so it was just a combination of trial and error and also just necessity based on the new field I was getting into. Got it.

 

Host: Paul Barnhurst:: It's a little bit of necessity, a little bit of trial and error. I do think for financial modelers it's beneficial to know some statistics being able to build a regression sensitivity analysis, being able to do something like Monte Carlo simulation, and being able to show that end user. The numbers we came up with were 6 million. But here's kind of the range. Here's our real how we think about it. The chances of that happening. Because so often, as having been in FP&A and financial models, somebody sees that estimate and they expect that to be the number. You're like, no, it's never going to be that number. There are all kinds of risks and assumptions around that. I think being able to bring some statistical modeling to financial modeling can help better bracket what that range is, and what that risk looks like. I know an example of that is the CFA. Chartered Financial Analysts, redid the program about a year, year and a half ago now. Yes. One of the sections they added was Python as an optional learning path. Right? I think it's like ten hours of learning Python. So it just shows that recognition, because you're not using at least most people, aren't building their three-statement model in Python. You're not writing all that. You're generally doing that in Excel because we'll talk about it later. It's the better interface. That prototyping wireframe where you can kind of see it all versus writing all the code to look at it right there, just a different, different purpose. So I think there's a lot of opportunity. Speaking of that, what do you think? Do you think most financial modelers should learn kind of basic Python and R? Like what would be the benefit to them to learn some of that?

 

Guest: George Mount:: Yes, that's a good question. Individual results may vary. So I'll start out with however with Excel in particular there are opportunities to use Python specifically in the program. That's probably the best place to start for most people in financial modeling. I would say the two biggest areas that if you are thinking, is there a better way for this, then your answer might be yes, try out Python. This would be your time series analysis, which is important for finance. If you are struggling working with dates or changing the aggregation, you've got weekly returns and you want to turn them into daily or monthly or maybe build a simulation that forecasts things over time. Any of those things work well in Python. Also, visualizations if there are certain plot types that you've maybe heard about, you've seen online and you just got a little intimidated by all of the hacking and pointing and clicking that you need to do to build it in Excel, or you don't want to buy the add-in that would let you do it.

 

Guest: George Mount:: There's probably a way to do it in Python with like a line or two of code. So I'm hoping with your listeners that one of the big takeaways is that, like, I want to disentangle Python from being necessarily super complex and hard. Like a lot of you have probably built functions in Excel that are harder than any Python code I've ever written because it's just another tool. And a lot of these packages that are in Python that are totally free, we're developed for people like you and me. These are not for software developers. This is just for everyday people trying to build a model, clean their data, and do with a line or two of code here. We're not going to build an app or you're not it's we're not talking thousands of lines of code. We're talking like three at the most. So it's pretty achievable, I think, for most Excel users who have probably written nested if that could fit on like an eight-and-a-half by 11 pieces of paper.

 

Host: Paul Barnhurst:: Yes. We've all seen the nasty nested. If we've all written it.

 

Guest: George Mount:: Yes.

 

Host: Paul Barnhurst:: It will we'll go down infamy, I guess, or whatever you want to call it. Glory. I don't know, but I like how you shared that. It's not that difficult. I've started down the Python path and never committed to learning more. It's still something I want to put a little more time into because I know it would be beneficial. But now that I do so much more talking and content creation and training, I don't have as much time as I used to fix all that crappy data, so to speak. I think that you make a great point of one look. If you can write complex formulas in Excel, you can learn Python. It's not that much. It's not like it's that difficult. I think that's a great point. Then you mentioned the use cases. For the time series cleaning data graphs, one that comes to mind is seeing K-Means. I know that's pretty easy. I've seen some examples of doing that in Python. Do you know when Python goes for general releases? Excel announced that, still in beta, right?

 

Guest: George Mount:: Yes, I think there was an announcement recently that it is moving out of beta, but I don't know the timeline as far as longer-term plans, that's not been announced too much whether it will be paid and to what extent and that sort of thing.

 

Host: Paul Barnhurst:: Yes, I know it will be exciting, and I think it's something that modelers should at least learn at a basic level, learn how to do a few things that can enhance their work. So I think, like your book is a great resource. There's a lot out there. Any other resources or thoughts you'd give them on learning it? I know you have your book, but any other ways you'd recommend if they want to get started?

 

Guest: George Mount:: Yes, I would check out my blog. I've been writing a lot on different. I just posted something yesterday about calculating investment returns. I calculated and did something the other day on creating a moving, average interactive kind of plot. So those might be some good resources, specifically in finance for how to work with Python and Excel. There's also a great book by a guy, Felix Zumstein called Python for Excel. That is a large, comprehensive, and good overview of a lot of stuff that isn't quite there yet with Python and Excel. So if you're thinking about, well, how can I do stuff like automate my workbooks and almost build the applications and things like that with Python? That's going to be a good book for that. You're going to learn pretty quickly that Python in Excel is just the tip of the iceberg for what you can do with Python or as an Excel user.

 

Host: Paul Barnhurst:: In today's business world, financial modeling skills are more important than ever. With the Financial Modeling Institute's Advanced Financial Modeler accreditation program, you can become recognized as an expert in the field by validating your financial modeling skills. Join the Financial Modeling Institute's community of top financial modelers, gain access to extensive learning resources, and attain the prestigious Advanced Financial Modeler accreditation. Visit www.fminstitute.com/podcast and use Code podcast to save 15% when you register. I have heard that It's not every library. It's not like you can do everything in Excel that you can do with Python. But what you can do can enhance what you're doing in Excel. So speaking of that, do you have a favorite use case for Python in Excel?

 

Guest: George Mount:: Yes, I think where I've been finding cool uses is combining. If you think about Excel as this user-driven tool right where you can have the user select what time range they want, or what interest rate they want, or what asset they want, or something like that. We're all used to building the data validation and the dropdowns and having those inputs. Then from there, you can feed those inputs into Python, have it do whatever you're looking to do, whether that is doing a K-Means or a simulation, or how many periods of end you want to smooth for, and you're moving average and building those outputs in Python. So you are getting the best of both worlds. Python is just very good at raw computation, not the easiest tool to build outputs right that users might want to look at or care about. Right? So whether that's even having your data formatted right having it Interactive. So your inputs can be entered in the workbook like that. So I'm finding a cool combination of of Excel inputs. Then Python models and things on the output.

 

Host: Paul Barnhurst:: Other than Python Excel has made a ton of changes in the last few years. I know you talked about it on LinkedIn. I've talked about how today's Excel is not the Excel of 10, or 15 years ago. I often like to kind of think of it as modern Excel with Power Query, Power Pivot, now Python, dynamic arrays, Lambda, Lab, and other things. Adding the Java scripting office script in there and all that. Is there something you want to see next or where? Is there something you're excited about that you're looking forward to, that you think may be coming to Excel, or that they've announced in beta, or any thoughts on where we're kind of going with all this?

 

Guest: George Mount:: Yes. As I mentioned, I think the new frontier of, I guess, citizen data engineering in Excel is exciting. Being able to do more stuff with office scripts and Power Automate, lets you kind of bake Excel into the wider set of tools that you might be working with. Often it's hard to kind of flow data in and out of Excel. Once data is in there, it kind of stays in there. Once you have your model built on Excel, it kind of stays in Excel. So I think it's exciting to think about how could we do something more with Excel to integrate it with other tools we're working with. That's exciting. So letting people do all those things and obviously with copilot and I, I think the way I'm thinking about that is just as we have more and more things that you mentioned. Like, nobody's going to become an expert in Lambda, Lab, or Power Query. There are so many things. But with the help of AI ideally, that's going to give you that assistance that you need to refresh your memory to help you question, make sure you know that you're doing the right thing, and give you that kind of sanity check for what you're trying to build. I think we've all experienced that AI, itself, can use some sanity checks now and then as can we.

 

Host: Paul Barnhurst:: Yes. Every so often I go off the rails, so to speak, and one of my other podcasts called Future Finance, we have a section where you know your job is safe for now. If AI said what and then we share different things that it's done that kind of make you go, all right, it has room for improvement. So next question I want to ask you is about a comment I've heard you make a few times. You've talked about how Excel is great for prototyping and wireframing. Can you elaborate on that? What did you mean by that?

 

Guest: George Mount:: It's difficult in tools like Python or R to see the data. The whole point is that you're storing data away in memory, and the only way you can see it is if you have to print it. You have to go out and look for it. In Excel. It's kind of the opposite. You see the data first and foremost. I think that's when most people think of right with Excel that all the numbers are there. You can drill in, you can see the formulas, but it's kind of the outputs come first and the inputs are layered underneath that. So what makes that useful is, is you get to understand your data, you get to see it. It's a fast tool. You talk about like that envelope math, If you just need to, to bake something up quickly and move with it and not get too worried about that analysis paralysis that can set it. If you think too long about trying to build the perfect model. It's often better to just try to get something on the page and iterate. So I think Excel is good for that in ways that traditional programming language isn't necessarily best suited for.

 

Host: Paul Barnhurst:: Yes, I like the example you shared about how it's great for prototyping and wireframing, and how you can drill down and see everything. I think for me, often when I work in Power Query, when I'm trying to convert somebody's process saying, no, you shouldn't be doing this in Excel, but I can go through and understand every formula, dig into it, break it apart. Think about how I do it in Power Query. I don't load the data to Power Query and try to make it look like an Excel file. I analyze it all there, kind of do that wireframing and work. Then I go to Power Query and figure out, okay, how do I get it to do that? I think that's one example. Yes, I get Power Query is technically part of Excel, but it's a separate application that works within Excel. So I think that's one area. I use it a lot. Next couple of questions. I want to have a little bit of fun with you here. What's your favorite Excel shortcut?

 

Guest: George Mount:: Ctrl+T.

 

Host: Paul Barnhurst:: Yes, I love it. Tables guy.

 

Guest: George Mount:: I'm trying to shift however to ctrl+L because ctrl+T doesn't work so well on a lot of browsers. I think Excel Online is catching some momentum. So you might want to try a ctrl+L here soon because ctrl+T, I think, opens a new tab or something like that on a lot of browsers, not web compatible shortcut.

 

Host: Paul Barnhurst:: Yes, I still try to avoid online Excel. Though I know it's catching up, I still find myself 99% desktop, so that's a good trick to know. Favorite formula?

 

Guest: George Mount:: I agree. Yes, I'm more of a desktop guy.

 

Host: Paul Barnhurst:: Good. I'm glad to hear.

 

Guest: George Mount:: Just to go online every so often and see what's going on there.

 

Host: Paul Barnhurst:: Well, I might have to start doing ctrl+L, that's from when it used to be a list. I'm guessing because it was a list in like 2007. Then it became, I think in 2010, or maybe it was 03 and 07 or something like that.

 

Guest: George Mount:: Yes, you might be I just thought it was arbitrary, but no, you there's something to that.

 

Host: Paul Barnhurst:: Yes, that's what I'm guessing because it was lists originally and then they turned it into tables. So I'm guessing that's where it came from. Who knows though? But favorite function in Excel, what's your favorite right now?

 

Guest: George Mount:: I've been having a lot of success with XMATCH lately. I keep forgetting that it's a lot better than a MATCH. I see a lot of people using the match function with the dynamic arrays, but I mean we should be using XMATCH. You don't have to specify what match type you want, and even if you want to specify, you get a lot more options. So I think that's my favorite one right now.

 

Host: Paul Barnhurst:: Funny enough, I finally started using that one. I've incorporated my training. I'm like, I'm not going to train them on MATCH anymore. I'm just going to train them on XMATCH. Like I just never picked it up. now I'm like, I should be using that. It has a lot more power. So funny you mentioned that one because that's just in the last month, I've finally kind of started to catch on to go. Why am I not using this? The next question I want to ask you here is, what's the one lesson you've learned over your career that's helped you the most?

 

Guest: George Mount:: So I remember the Wall Street Journal had an article a few years ago, and the title was something like, the first rule of knowing Microsoft Excel is not to tell anyone that you're good at it. I think I've made a career by subverting that rule. It is unfortunate that in a lot of organizations. If you are that Excel wizard, you're going to get a lot of unsolicited and underappreciated requests for help. Ideally, you're going to find yourself or build a culture where it's not that way and that culture sharing is both accepted and beneficial to you. As a professional, I think everything that I've accomplished has been through sharing my knowledge freely, whether it's starting my blog, which I did start going back to my beginnings with Excel as just like a log of, oh, here's what I learned today that I thought was interesting. It was just me kind of summarizing because it personally helps me a lot to write things to understand and retain them. So whether you can find yourself an organization or work through your own social media network or whatever community you've got, try to put yourself in a way where the first rule is to share what you know about Excel, rather than think that you need to hide it.

 

Host: Paul Barnhurst:: I like that. Great point. So we're coming toward the end of our time. So I want to move into a section. It's kind of what I call a rapid-fire section. What are the rules? You get 15 seconds for the answer. Then at the end, you can elaborate on 1 or 2 of the questions. Most of them will be quicker than that. You can't tell me 'it depends'. You have to pick a side. That's where the fun comes in. That's why we let you elaborate at the end. I think we just have 7 or 8 here for you. We've modified a little bit of the list, knowing you're not a full-time modeler like many of our guests, you're not building a lot of three-statement models, but the first one, what's your view on circular versus no circular references in models?

 

Guest: George Mount:: I'm a pro, pro circular.

 

Host: Paul Barnhurst:: Alrighty. Vba or no VBA in models?

 

Guest: George Mount:: Anti-VBA.

 

Host: Paul Barnhurst:: Anti-VBA, I like it. External workbook links in models? Yes or no?

 

Guest: George Mount:: That's a big 'no' for me.

 

Host: Paul Barnhurst:: Yes, that's what I like to hear. Do you have a favorite data visualization tool?

 

Guest: George Mount:: I think right now it's the Seaborn package in Python.

 

Host: Paul Barnhurst:: Okay, cool. If you could only use one of the following for the rest of your life, which would you pick? R, Python, or SQL?

 

Guest: George Mount:: This is going to surprise people, but I'm going to say R.

 

Host: Paul Barnhurst:: Why?

 

Guest: George Mount:: It's just not that popular anymore. But I just find it very intuitive. It knows me, it gets me, a lot of languages don't. I can't explain it.

 

Host: Paul Barnhurst:: Interesting. I wouldn't have guessed. I was surprised by R as well. I figured you'd probably pick Python, maybe SQL. I would have put our third. So there we go. Learn something new every day. Will Excel ever die?

 

Guest: George Mount:: Quote, John Maynard Keynes, “In the long run we are all dead”. So yes.

 

Host: Paul Barnhurst:: Yes, it's like I had one guest say 'yes'. I just hope it's not in my lifetime. Will I build models for us in the future, like financial models?

 

Guest: George Mount:: Yes. Not that they'll be that great, but they will build them.

 

Host: Paul Barnhurst:: Yes, it'll be interesting to watch for sure. I think there always will need to be human interaction. The more you can structure it, yes, the more it can build or get you started. But I think there's going to be a balance. What is your lookup function of choice? So I'm going to give you four options. There are others if you want to choose them you can pick one, CHOOSE, VLOOKUP, INDEX/MATCH, or XLOOKUP.

 

Guest: George Mount:: I'm with XLOOKUP. It's the newest. It's got to be the best. I mean, that's not always true, but I think in the case of dynamic array, dynamic array functions are just something that I can't imagine that I lived without in Excel. So whenever I have the choice to use a dynamic array function, I try to err on the side of using a dynamic array function.

 

Host: Paul Barnhurst:: Isn't it funny how so many of us have now used them? How do we live without this? You can put multiple inputs and get it to spill and so many things you can do. I think it was you that posted last week about the nested XLOOKUP.

 

Guest: George Mount:: Yes.

 

Host: Paul Barnhurst:: First, like you hadn't done that before, Is that what you're saying?

 

Guest: George Mount:: No, I'd never done a two-way XLOOKUP. Somebody asked in training and I did spin my wheels a bit on it, but I was able to figure it out, and I guess that part's it. It's not my favorite. I did find INDEX/MATCH a little more intuitive, but I'll get used to it. It does just give you that ability, like you said. I mean, maybe I want to look up the whole row or something like that. You have that opportunity with XLOOKUP. So I just find it more flexible. It works well with tables as your source input. I just find that syntax. It's very like it's very programmatic. Right? It makes Excel feel like more of a programming language. It lets you build cool things. Like if people have used group by or pivot by, I've been experimenting with letting people. I mean, pivot tables are great, but I'm sure we've all had people who aren't great with them. But with group by or pivot by, you build a dropdown, let people choose what values and categories they want, and that data refreshes automatically. So it is a great tool to make Excel even more interactive.

 

Host: Paul Barnhurst:: Yes, and speaking of dynamic arrays, I'll put a plug-in. We just recently had an episode with two brothers, Cameron and Nicholas. They run a consulting firm over in the UK and they're doing their three statement models 100% dynamic. They're not using everything in the entire model is dynamic. They work with a guy by the name of Craig Hatmaker. I don't know if you know him, but he's big on lambdas. He's built some 5G models to help manage things like corkscrew. Because how do you manage opening and ending balance with a dynamic array? You can't by yourself. So he's written some lambdas that can handle that and other things. So with his lambdas, they now do dynamic arrays with 100% of their clients. So that was kind of interesting to see. So it's exciting to see how people keep pushing the envelope. I'm not there. I don't build my full I haven't built a full three-statement model using dynamic arrays. I'll build things with dynamic arrays for sure, but that is full just because of some of those challenges. But it's so exciting to watch. I'm going to ask you just kind of 1 or 2 personal kind of fun questions here, and then we'll let you go. So the first one, if you could travel anywhere in the world tomorrow, where would you go?

 

Guest: George Mount:: I have enjoyed traveling to Santa Barbara. I've been doing some courses with LinkedIn learning, and I just find it my place. The weather, the culture, the food, it's a very, very nice atmosphere. So I think I'd go back and maybe I could record a course if I'm there. I don't know.

 

Host: Paul Barnhurst:: I figured you might say somewhere else in Ohio.

 

Guest: George Mount:: Well, the state fair is coming up, so if I have a time machine, I guess I could head down to Columbus. But Yes, I'm sure there are other places, but I think about Santa Barbara a lot. So if you've got listeners out there and in the area if you're looking for an Excel trainer, not going to hesitate to come by Central California.

 

Host: Paul Barnhurst:: Put it out there. If your team needs to get a little better at Python, some of the data analytics you want to upskill your modelers call George. How's that? Was that a nice plug?

 

Guest: George Mount:: Good. Yes, absolutely.

 

Host: Paul Barnhurst:: Alrighty. The last one is, do you have someone you like to follow around? Excel? What are 1 or 2 names you would recommend to people who have helped you in your Excel journey?

 

Guest: George Mount:: I don't want to go with the obvious, but I think Oz is kind of my go-to guy. I feel like he and Jordan were the two that opened the Jordan Goldmeier. Opened the door up for me as an Excel person. As I came up the ranks, they were always supportive. Oz, he cares about the community like nobody else I've met, comment on your post, will answer your questions, just a really, good guy. So I think that's one person that even not just for Excel, just to like, follow him and get to know him as a person has been a great thing for me. So those are my two guys. Jordan's been tremendously helpful as well. He's dominated a lot of MVPs and done a lot for the community. So I think those two guys have shaped the Excel community, but also my journey in Excel highly.

 

Host: Paul Barnhurst:: Yes, they're both great guys. I've got to know both of them. Funny enough, Jordan was the first person I ever had on for a podcast.

 

Guest: George Mount:: Oh, that's cool.

 

Host: Paul Barnhurst:: Yes, over two years ago now. I had Oz on when he referred to the angels came down, the lights parted, how Power Query was his Jesus or whatever. I had him and John Acampora on and John.

 

Guest: George Mount:: Oh, John. Yes. John's a good guy too.

 

Host: Paul Barnhurst:: And John commented with he goes, well, I guess if that was his Jesus, then Ken Puls is my Jesus. That's how he learned Power Query. So it was a fun conversation. Now, Oz is quite the character, but probably should wrap up here. Others will be like, what are you guys talking about? We don't even know. So those are great resources, I agree, but if our audience wants to learn more or get in touch with you, what's the best way for them to do that?

 

Guest: George Mount:: Yes, I think LinkedIn is a good one. I have a pretty easy name. It's very monosyllabic. I'm George Mallet. So you can look for me there. I post pretty much every day on data analytics and Excel. You can also, while you're there, go to my website and you can link to it or you'll find it on my website. And there are a lot of resources as well, in longer form than you would find on LinkedIn. So those are the places to find me.

 

Host: Paul Barnhurst:: All Well, hey, thank you for joining me today, George. I enjoyed chatting. I would just encourage our modelers to get out there and expand their knowledge a little bit. Python data analysis, that kind of data engineering can help with cleaning up the data as you're building the models, visualizing it, and statistical analysis. Thank you for sharing some of your thoughts with our audience.

 

Guest: George Mount:: Thank you, Paul. Take care everybody.

 

Host: Paul Barnhurst:: Financial Modeler's Corner was brought to you by the Financial Modeling Institute. Visit FMI at wwwfminstitute.com/podcast and use Code Podcast to save 15% when you enroll in one of their accreditations today.

Ankita Ghosh
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