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And now onto the show.
From DataRails, this is FPA Today.
Welcome to FPA Today.
I'm your host, Glenn Hopper.
Today I'm excited to welcome George Mount to the show.
George is the founder and CEO of Stringfest Analytics, a consulting firm specializing in analytics, education, and upskilling.
He's an expert in helping individuals and organizations excel at analytics, particularly in bridging the gap between Excel and more advanced tools like Python and R.
George is the author of two highly regarded books on the topic, Advancing into Analytics from Excel to Python and R, and the recently released Modern Data Analytics in Excel using Power Query, Power Pivot, and more for enhanced data analytics.
His work has been instrumental in guiding Excel users and finance professionals into the world of data science and analytics.
With a background spanning economics, finance, and information systems, George brings a wealth of knowledge to the data and education space.
He's a regular speaker and blogger on data analysis and workforce development.
He's also a two -time recipient of the Microsoft MVP Award for his exceptional technical expertise and community contributions.
Today, we'll be diving into George's insights on the evolving landscape of data skills for finance professionals, the role of Excel in the age of data science, and how FP &A teams can stay ahead of the curve in an increasingly automated, AI -driven world.
George, welcome to the show.
Thank you, Glenn. Thank you for that thorough introduction.
Looking forward to speaking more about these topics.
Yeah, I was actually looking at that before the show, and I thought, well, I need to shorten this down.
I thought, what am I going to take out?
It's all gold. You're saying I appreciate the plug for the two books.
I'm sure we'll talk about this more.
Yeah, and actually, let's dive right into it.
Because your first book came out the year that my book did.
So in 2021, Advancing into Analytics came in.
And the goal of that was to help Excel users make the leap into data science.
And you and I have had a very similar mission on this.
And I think we probably came to it in about the same way.
But I'd love to hear kind of what inspired you to write that book and realize that you needed to get out there to find a way to bridge that gap for finance professionals.
Sure. Yeah. So for those of you watching, here's the visual aid of the first book.
It came out in 2021.
It's not too long. I tried to keep my books brief.
And the reason for that is I just want the biggest ROI for your time, because I know reading books is not easy, writing books is not easy.
And in this particular case, I wanted the quickest A to B jump from people using Excel into getting into more advanced techniques and methods and tools like Python and R.
I was in a graduate program where I was doing a lot of heavy statistical quantitative modeling and stuff like that.
And I was really starting to hit the brink of what felt comfortable to do in Excel.
And I thought about learning Python and R, just couldn't really get my teeth on it.
I didn't feel like there was a great set of resources for it.
And I just thought, well, let me write the books.
So I have it structured in a way, again, that it takes that knowledge that you have in Excel.
Because if you are coming from Excel, you know a lot about data, you know a lot about analytics.
So how can you take that information and get into new techniques and tools as quickly as possible?
And that was my goal for that book.
So first off, your mention of keeping the book short.
So I've got my next book is coming out in a few weeks.
And my first book was around 40 ,000 words.
And my next one is like 100 ,000.
So it's going to be like a 300 and something page book and I worry now because when we're writing about this stuff, we go pretty deep into the details and it's people have to want to get there.
But I think once you sort of see the way data scientists are working, like, I think the goal with your books is similar to the goal in mind.
It's like, let me broaden your horizon and, you know, show you this new world.
Well, you know, Excel is great.
And Excel, honestly, even with a huge dataset, because I can quickly get around it and it's just so user friendly, that's the first place I'm going is in Excel.
And I still use Excel every day.
And actually, to be fair, I probably in the past month have opened Excel twice as much as I've done anything in Python, you know.
But that said, when you kind of see the other side, you start to realize, I could replicate this.
I could do things more efficiently.
I've got more ability, you know, but there is that learning curve.
So I'm wondering, as you talk to people about your book, as you've done your training, how has the reception been, you know, to the idea of Excel users who've gotten, you know, there's people who do awesome things in Excel.
And now you're saying, well, what if you learn Python and R, you know, how do people respond when they realize I've got to learn a new computer language, a new way of interacting with data.
And have you seen, you know, so your book came out in 21.
Have you seen since then, especially with Python and Excel now, are you seeing like attitudes and adoption changed since when the book first came out?
Yeah, that was the big watershed moment, I think, for acceptance in Python and with Excel in particular.
Once it was available in Python, I really felt like that was the stamp of approval where people started to take it seriously.
Because before then, I got attitudes ranging from confusion to almost like outright hostility.
And I remember presenting at meetups about interfacing Python with Excel and hearing people say things like, well, why do I need Python?
Everything I need is in Excel.
Just not interested.
So that's been a big development.
And I'm seeing as this progresses, you know, I've been doing a lot of those challenges you might see on LinkedIn, where people post a data set, and hey, you need to do X, Y, and Z, how are you going to do that?
I've been doing them in Python.
And I think it's really opening people's eyes to the fact that this isn't always that challenging, right?
I mean, yeah, you can write a huge long function with eight parentheses scattered here and there.
It's not many, way more than that.
Or, you know, Python, sometimes is really easy on the eye.
So I think that just seeing it in action inside of Excel has really lowered that barrier of adoption, really got people thinking and brought it out of this area where it seemed like this outside quantity and into something that's part of the larger Excel universe.
And interestingly, and we talked about this before the show, but you know, talking about broadening your horizon going into R and Python.
And I always think R is like the gateway drug to Python, you know, because it's maybe a little more user friendly, but you start to quickly kind of run up against its limitations.
And then, you know, it's easier to bridge that gap over to Python.
But what we were talking about before the show, in your latest book, you kind of go back to basics or you bring it all back home with modern data analytics in Excel.
And here you're focusing more on empowering data analysts with like, you know, Power Query, Power Pivot, and the other Excel features.
So, you know, not to completely contradict what we were just saying, but what made you kind of decide to go back to Excel for this book?
So this book came out in 2021, was on the heels of me doing advanced statistics and things like that.
Then I got back out of school into the workforce.
And it was a good reminder that not everybody's there, not everybody needs those skills.
But there are a whole lot of people who aren't familiar with those tools that you mentioned, like Power Query, Power Pivot, other things like Dynamic Arrays, all these things that have come out in Excel.
So the way I like to think about it is that this book is meant to help you become a data analyst in Excel.
And this is your book for becoming a data scientist in Excel.
And while they are emphasizing different things, there's all there is a lot of crossover.
I was actually just working on a post earlier today about all the different modern Excel tools that are used for Python and Excel to even work.
So things like linked data types, dynamic arrays, pictures inside of cells, right?
There are all these new tools that have come out that Python and Excel actually needs to function.
So it really is kind of building on the shoulders of modern Excel.
So in a way, this book is a good one that will help you learn more because this book came out before Python and Excel ever happened.
So I don't talk about that specifically, but Python is Python.
So it's still stuff that's good to know if you want to get into that.
I'm sure you've thought about this a lot.
And this is kind of what prompted me to go back and write another book this year is thinking about when I was trying to make that transition, not having like a single text where I could figure out the path I need to go down.
And I think the combination of your books does a really good job of different doorways into the same space of being able to work with data better.
But for someone who's just starting on this journey, so maybe they're working in Excel and now they're in Tableau or Power BI and they're starting to do a little more with data and maybe even visualizations stuff that they weren't doing before, but they're starting to get kind of the concept of joins
and maybe venturing into SQL.
But to make that leap, I mean, I'm thinking of like incremental baby steps.
If someone came to you and said, hey, I'm in FP &A, I've been doing this for five years.
I'm killing it in Excel, but I want to go beyond that.
What's the typical learning progression you'd recommend for someone in that spot?
Yeah, it's a good question.
So the way I would think about this is you want to milk Excel for all it's worth.
You definitely want to get into Power Query, Power Pivot, all that stuff.
I would say focus on the interface and the things that you can do with the click GUI interface in Power Query before you worry too much about programming, Power Pivot, you are going to need to know some programming there, but just get well -rounded in that data analyst core for Excel.
And then from there, I think about these three legs of data professions in a way.
You have your data analytics, your data science, and you have your data engineering.
What do you want to lean into the most?
And we can almost think of business intelligence as an offshoot of data analytics because you already have the basics of data analysis down with all these tools in Excel.
So if you're really into predictive analytics, machine learning, well, that's data science.
So you're going to be getting into Python, you can build basic Python scripts in Excel now.
So you're going to stay in Excel.
If you want to get into BI, probably going to be more of a Power BI thing over there.
But again, you're building on what you already know with DAX and you can get into EMCODE and stuff like that.
So what I'm trying to do is scaffold you into just say what you know from Excel and then building.
And then last but not least, there's data engineering, which is a newer thing, especially in Excel.
That's where you get into your Power Platforms, so getting into Power Automate, Office Scripts.
You might think about even learning JavaScript, for example, because there's a lot more that's being done in Excel with JavaScript now.
That's definitely at the point where people are resistant and hostile toward JavaScript, just like Python was a few years ago.
But that'll probably change.
So that's the way I think about it.
And I'm really sticking inside the Excel universe, because that's what I know the best.
And that's what I can give the best advice on.
But I think if you think of Excel as your home base, and then you think of, okay, is this data engineering, data science, or data analysis, or BI?
How do I play between those fields?
Yeah, so I think about this a lot.
There's that expression, if the only tool you have is a hammer, everything becomes a nail.
And I think about the way that financial people use Excel and then the way that other business people use Excel.
And the other day I found myself, I was making a list, just a straight text list, but I was doing it in Excel because I was going to have to sort it alphabetically.
And I thought now I've turned into the guy that's just doing everything in Excel weirdly, and this is just a list of like 50 items or whatever.
But before I made the jump into doing even the little bit of coding that I do, you can figure out ways to do so much in Excel.
I mean, it's such a versatile platform.
And I'm not even talking about using Python in Excel, I mean, just the basic Excel.
So if I'm someone who's trying to understand why I need to move beyond that, help me draw the line between, I can do this in Excel and this in Python.
But what situations does it make sense to work in Excel versus this is really better suited for Python?
Yeah. So if you're looking at your work and you are noticing places where Excel is just forcing you to get hacky or forcing you to point and click, and there's really no way to build this in just an A to B automated straight shot.
Those are things that Python might step in for.
So for example, you're looking at building some type of a plot, maybe you want to look at small multiples, which is a pretty common data visualization practice, right?
We're going to break this plot down by different categories, it lets you take in those differences a lot easier than necessarily maybe putting all those categories on the same plot where it gets busy.
That's not really something that Excel's cut out for.
And I see people build small multiples, but it's a lot of effort.
And there's really no way to write a reproducible script that, hey, maybe you want your coworker, or your coworker said, hey, how do I do small multiples?
Well, and then you send them to a blog post where it's like, well, click this, but actually not that.
You need to go here and hit the dropdown and all this like that kind of hacky stuff.
I think if you're seeing that, that's a good place for Python, right?
Because you're writing everything in code, but you're not writing code like a developer, you're writing code like what we would call a citizen data scientist or citizen data analyst.
These are packages that were built for people without formal programming experience.
So a lot of the stuff you're going to do, you're just using functions just like you do in Excel, you've written an xlookup function before, there are different arguments, some are optional, some are implied, that kind of thing.
And it's the same thing.
So when you are getting to that point, like plotting I mentioned is a good one.
I would say time series is another good one.
Everybody dates in Excel are kind of the butt of a lot of internet jokes.
So things where you want to do more advanced time series analysis, whether that's moving averages or sampling the data at different time frequencies, or even getting into more advanced things like building forecasts and things like that, I would say those are two good ones to focus on.
And I'll leave it at that for right now.
Yeah, time series plus.
Yeah, I love that you were talking about the citizen development.
I couldn't imagine any of my code ever being in a production environment.
If it's not in a Jupiter notebook or a Colab project, or if an actual coder looked at my code, what are you doing here, man?
And on that note, so another confession, I've really leaned in this year because I felt like I needed to understand the fundamentals.
But I finally started leaning into the low code, no code tools.
And I wish, I think we were talking before the show, years ago, when I first got deep into analytics, I went tableau, because it seemed like the easier route to go.
And I know I could have done both, because there's only so many hours in a day.
And I really didn't dive into Power BI, but I'm seeing now with just across the Microsoft universe, what people are doing with it.
I feel like, eh, I probably went with the wrong horse on that one.
But at this point, though, there are so many other tools.
And I guess Power BI does have some of this drag and drop functionality.
Are you messing around with any of the low code, no code tools that are out there?
And how do you see these tools kind of fitting into the toolkit for Excel focused financial analysts?
Because I don't know if you've used the ones I've, RapidMiner is probably the one I've done the most in lately.
Dataiku is another one.
But where do you see those tools kind of fitting in with Excel and Python and all that?
Or have you even messed around with any of those?
Yeah, I'm probably most familiar with the Power Platform and Power Automate, Power Apps, things like that.
I think there's definitely opportunity for finance people there, whether you are trying to build workflows or applications.
Maybe you've been tasked with setting up some kind of approval process for budgeting or expenses or whatever.
Or you want to set up some kind of an app that lets people track inventory expenses or whatever it is.
Thinking about Excel right now, it's every Excel workbook, it's kind of its own thing.
And we don't really have a good way of tying them into emails or workflows or projects or to -do tasks or stuff like that.
Using it as an approval process in Excel is kind of a nightmare, right?
Trying to track things.
And again, Excel is a database.
It's not a good connotation.
But I think with Power Platform and Power Apps, you're not necessarily going to use Excel as that database, but you are going to have the opportunity to use Excel as one piece of your wider stack.
So if people are looking at building those in -house websites and apps and things like that to help with those kind of day -to -day operations, that's more so on the development and engineering side.
You're not going to do a lot of predictive analytics and machine learning there.
But I've been getting into that slowly.
I think that's a pretty exciting opportunity.
I think Excel add -ins right now, they're kind of at an impasse where I try not to use them.
I know a lot of people do use add -ins or ask me want to use them.
There's probably going to be a big shift there where Excel has really been pushing things being built on a unified platform, which they chose to be JavaScripts.
You have Office scripts as I know a lot of VBA developers are going to be out for blood.
But if I say it's an alternative or a modern replacement for VBA, a lot of ways it is.
We can go into the basics.
Yes, there are always a few cases for VBA, just like there are always cases for COBOL and FORTRAN.
But I mean, come on.
For most people, I think Office scripts will be a good idea to learn.
Those are all really cool ways to get into that.
That really veers off of low code, no code.
But thinking about all these citizen developer data engineering tools that the Power Platform offers is a cool place for people there and finance to go.
Yeah. When you were talking about add -ins, you got me thinking about CoPilot.
We're really waiting to see how CoPilot becomes fully integrated into Excel.
I know there's the CoPilot finance package, which is really limited right now.
But as they roll out new features and anyone who listens to the show knows I can't go a single episode without mentioning generative AI.
So I'm going to go down the road here.
Okay, sure. Yeah, please.
Yeah. CoPilot in Excel has a way to go.
But you can see the writing on the wall and you can see what it's going to do in the coming years.
But already, GitHub CoPilot is out there.
I think about when I was taking my CS50 and when I was first learning Python.
I had that book, Python in a Day.
I don't know if you remember that one.
But there were all the stuff that I went through and how bad my code was for getting to close loops and just all the stupid mistakes I made.
How much easier it would be if you actually had the GitHub CoPilot back then.
I'm really jealous of people who are just learning or anyone who works in it all day to have that.
But I don't know how much you've messed around with GitHub CoPilot.
But I mean, how much is that changing coding for either experienced coders or just people starting out?
Yeah, I haven't done a lot with GitHub CoPilot, but I have worked with CoPilot in Excel.
I certainly see the overlap there.
I think a good point to be made with Python is that for a variety of reasons, it is a great tool for communicating with generative AI.
Again, not necessarily as the machine learning specialist, but even just general Excel or data analysts.
Often there's all you're going to get once you speak in Python.
Python is a programming language.
So it kind of speaks a similar language to the AI models.
So whether you're looking to get randomized data or you're looking to build a specific plot, Python is a really good common lingua franca thing for generative AI.
So we'll see how that works with CoPilot.
There's a little bit that I've been able to coax Excel CoPilot to do with Python.
But the way I think about this is that Excel, CoPilot, and Python really do form this new trinity for data analysis, where you're using Excel as your canvas, you're using generative AI and Python to get the answers that you want, and then you're using Excel as that way to really present it to the user.
Yeah, great points there.
As we're talking, I'm thinking if I'm looking to expand the tools that I'm working with, it makes a lot of sense to stay in the Microsoft universe.
And do you think that the logical first step is to go from Excel to start doing some stuff in Power BI?
What's sort of the prioritization there as you're moving through the technologies?
Yeah, I think I would go back to this idea of data science versus BI versus data engineering and looking at the prospects inside of your organization, outside market statistics and things like that.
There are plenty of other tools and vendors on the market.
So it is good to be able to situate how they all work together.
I think in general, we're getting to a point where there's not such a line in the sand between things like proprietary versus open source.
That was many years ago that that line really blurred.
I think one of the lines that's blurring now is this idea of low no code versus coding.
Because of generative AI, really anybody can write code.
It might not be good, but you can write it.
And this idea that it has to be low no code is falling by the wayside.
Yeah, really situating, where do you want to really plant your flag in the sand of data analytics and data science and all that stuff?
And then just looking at the landscape and seeing what works best as individual results may vary, like they say.
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You know, another thing I was thinking as we're sort of trying to put together this roadmap of how to move beyond and take on these new skills, you and I both had the advantage of starting out using R while we were in a program in school.
And, you know, R did help.
It was the first kind of writing, and I'm dating myself here, but since writing my basic programs in the 80s, you know, R was my first free introduction back.
I get, you know, HTML monkey kind of stuff and a little bit of JavaScript before that.
But now though, if I weren't in school, like if you're in a statistics class and R makes sense, it's easy to use and everybody's doing the same thing.
But if I'm already in the working world, is there any reason for anyone to learn?
I think I hid the kind of R studio on my laptop.
You know, I don't even I can't remember the last time I've opened that.
Is there a reason for people to learn R at this point or should you go just dive straight into Python?
I think some of that depends on industry dynamics.
There's certain places like a lot of academic research or biomedicine and engineering and things like that.
There is still some if you look at some of the really quantitative trading places, a lot of them actually still use R for research, right?
And then they put stuff into Python for production.
But so there are, you know, you might find certain pockets where R are still prevalent.
But I think with Python and Excel, that was really the knockout punch for Python to be the place for Excel users in general to go for better or worse.
I actually really like R.
I like it as a language.
If I could do my work my way, I would probably honestly pick up R a lot more often than I would now.
But, you know, just personally, I don't really get a lot of requests for it anymore in terms of like training and consulting.
So the market's kind of spoken to me, too, that it's not as big of a force as it had been.
Yeah, yeah, it makes sense.
You know, I don't want to beat a dead horse here, but I'm really looking at it.
It does feel like there's a bit of a sea change.
And what I always think is, you know, F .P .A.
folks, I feel like we were the original B .I.
people like we were doing B .I.
in Excel before there was, you know, before all this blew up.
And then when machine learning came along, like, and I think it's the amount of data that machine learning that marketing and sales had versus kind of the limited GL data that we had, and we had to figure out like how to get the data first.
But I do feel like sales and marketing like jumped over the finance side of the house using all this cool new, you know, machine learning algorithms for classification and regression and all the prediction that they were using.
And but now there are, I think with generative A .I., maybe opening some doors and making data science a little more accessible because you don't have to know coding as well to use the Python anymore.
And, you know, with like chat GPTs, data analyst, it can happen under the hood and you don't even know what's going on with Python.
But seeing now where more and more is going to be automated by technology.
If I know I'm an F .P .A.
person, my career path, I love F .P .A.
I want to stay doing this.
And you know, maybe that's ultimately a path to the chief financial officer.
See, what do you think like, where should my focus be?
Should I? How much do I have to lean into becoming a data scientist or a data engineer or, you know, just increasing my B .I.
chops and and thinking about as we get access to more data, that data science, like it's kind of like in finance, you have to know the difference between net income and EBITDA and gross margin and, you know, things that if you don't even have the domain expertise, you don't even know the questions to
ask. And in data science, if you don't know the basics of it, you're going to get yourself into trouble by using some wrong model or trying to forecast on something you don't understand.
So I mean, where should someone who knows F .P .A.
is what I'm doing now and what I want to do for my career?
Like, how do they focus what they need to stay on top of to be very good at that?
I think monitoring search trends is a great way to do that.
One thing that I've noticed is that there does seem to be a stepping back where if you think about a few years ago, data scientists was the sexiest job of the century.
Everybody wanted to get into machine learning and big data and all that stuff.
And now it's, well, hold on, right?
Like, is all this data worth collecting if it's not the right data?
Is just hiring a bunch of data scientists and throwing them at poor quality data going to solve your problems?
So I think the market is kind of correcting for that.
The next thing that is happening with what I'm seeing with generative AI is that, okay, generative AI can just start building our dashboards and our analyses and we don't really need data analysts anymore.
So I think that's probably a good place to really fight the fight right now where, okay, sure, we have generative AI and co -pilot.
That doesn't mean that we should just set this loose in among the organization.
We don't need data specialists anymore because then I think you're going to get the bloat of all these dashboards that nobody looks at or having these data scientists building these models that nobody can use because they don't really do anything.
So that's probably a really good place to be right now, which is, as I'm thinking about it, how do you, as the Excel person, become the source of reason and the kind of guide of adoptance for generative AI?
Starting with that data quality problem, which maybe we can finally get right now that there's so much attention on data and analytics.
So starting there, getting that data quality right, and then doing whatever makes sense, what you like, what your organization is looking for, whether that's building predictive models or doing business intelligence, just depends on different things.
But yeah, getting generative AI and data quality is probably a good place to start.
Yeah. Data is the foundation.
And I think especially if you're not at enterprise level in your business, the amount of data you have, it's not as much to work with.
You can pull in macroeconomic factors and all this external stuff, but I work a lot of times with businesses that are in say the 10 to 15 million dollar range and they talk about where they want to go with AI and you get in there and you look at it and you think, we got to address A as an accounting
before you can even move on to AI, because we got to go and clean up their chart of accounts and we've got to get their data straight and they don't even have that foundation.
But unless you've truly done it and you've kind of stepped up the data maturity scale, it's very hard to make AI work.
And especially if you've got a limited dataset and it's not clean data or you still have data silos, which you see all the time.
Systems not talking to each other and stuff living in Excel is a database and all that.
But I think it's sort of like the bells ringing now.
If you haven't gotten your data house in order and where we are with AI, you're going to start being left behind if it hadn't already happened.
And thinking about being left behind, apparently you don't sleep because we did talk before the show that you're already working on your next book.
And I think we both kind of are in the same place where you see how quickly AI is moving and it's like, we need to get out there and talk about this.
So tell me a little bit about your next book.
And I guess so. It's what?
Python, AI, data engineering skills for Excel users.
What's the focus of it?
Who's the audience?
Will people need to have read your first two before this one makes sense?
Walk me through what you're doing now.
Yeah. Okay. So yes, it is somewhat set up to be a trilogy.
You have your data scientists book, you have your data analytics book.
Number three is going to be more of a data engineer machine learning kind of book.
When I wrote Advancing in Analytics, that was pre -Python in Excel.
I even have a chapter on using Python to automate Excel in the second book.
So I do want to update all that.
I want to get into Copilot.
I want to get into AI.
I want to get into the basics of Power Automate.
So it really is starting with the premise of, okay, it's great that we're talking about Power Query.
And yes, I love Power Query, but it's almost like 15 years old at this point.
I mean, come on. What else is out there?
And what are these AI skills that are, in some ways, evergreen so that I know people are reluctant, in a lot of cases, to use Copilot in Excel.
It does present, in some ways, a catch -22, where the only people who are going to get benefits of Copilot in Excel are people who don't know enough about Excel to use Copilot.
So I get that there's a lot of resistance.
But to say that there's no place for AI in finance data, Excel, we got to meet somewhere in the middle.
So what I'm going to try to do with this book is here's some foundational things that even if you're not in love with Copilot yet, it's going to get better.
Here's why, because this is how AI works.
Here are things that you should know about how to structure your data, how to troubleshoot, things that Excel is already using that RAI that actually worked really well.
Even Flash Fill is AI as pattern recognition, doing fuzzy matching, building forecasts.
There are a lot of things that Excel is doing right now in a much narrower sense.
That's still AI. So let me show you all those things and get you thinking about how AI works, how does AI learn from data, how does AI need things structured to work at its best.
So there's going to be a lot of that.
And then getting more into, okay, so now that you know Excel pretty well and you're writing scripts with Python and you've got your workflows down in Power Query, okay, how can we start automating stuff both inside and outside of Excel with Power Automate, Office scripts, and things like that, and really
turn Excel into a modern development platform really in a way that VBA just can't do?
Sorry again. Every time I mention that, I just think of angry VBA developers.
I don't want to offend anybody, but it's not really a cloud -first tool.
That's the way things are for better or worse now.
We use the cloud for pretty much everything and VBA does not use the cloud.
So you do the math on that one.
And I'll be talking about Office scripts.
Yeah. And not to knock VBA because I get the same feedback that you do, but you talked about what Power Query is now 15 years old.
How old is VBA at this point?
Right. I mean, Python's been around for a while.
I don't age in some ways the longer a tool exists and has staying power.
That could be a good thing, but that's only if it is adaptable to write the current environment.
And Python was able to do that, and Python's been a really great tool for web development, for example.
Version control, you can build modules and packages with Python.
VBA, for better or worse, just isn't set up for a lot of those things.
So I think that gives an opportunity to try out some new stuff.
And that's what I'm going to write about in the book.
And people can like it or not.
But I will say that I was talking about Python and Excel way from the get -go.
So we'll see what happens with the next trend.
Yep, yep. And so do you have a timeline on the book yet of when we can expect it?
Or is it early stages?
How are you sitting with it now?
Probably sometime next year.
I don't have a great timeline.
There's a lot of other things I'm working on.
I want to hit on that for a minute, because we did dive straight into your books and the approach that you've taken and how it applies to FBA folks.
But you also are a consultant with Stringfest.
So tell me a little bit about your consulting work and the size of the companies and where they are and the data maturity level and what you're coming in and doing and how much you work with finance teams in that kind of work versus the sales and marketing and other teams.
Most of my clients are in, I guess, I would say three buckets.
I do work with a lot of operations, types of teams who work in manufacturing.
And they're working with data from ERPs or other planning systems that they're getting into Excel.
They just want to build some basic data cleaning and analysis workflows.
So those tend to be more like medium, smaller businesses.
I also do some work because I do have a background in academic research.
I was in a PhD program.
So I do get work from academic organizations, nonprofits.
And those are the people that are actually still using R for research.
And they're in the same kind of place where this is very, very small data, often because they've collected it themselves or they've gotten it through a third party policy, nonprofit or things like that.
And they're doing the same kinds of things.
They want to be able to report on this data, clean it efficiently.
So they're on more of a research -heavy kind of a path there.
When it comes to banking and finance, that's a good portion of my audience, a good portion of my clients.
A lot of those really are at large scale.
So we're talking very large, top 10, top 30 kinds of banks.
And these are groups that at a high level, they're thinking, how do we plan for our talent?
How do we develop this talent?
And how do we stay ahead of it?
So what are the things that people coming in need to know?
About Excel, about data analytics and where do we go from there?
So how do we keep that learning pathway and plan for what's coming next?
So those are the three main clients that I serve.
A lot of it is through workshops.
I do some learning, pathway development, and things like that.
But I love training.
That's a big part of what I do.
I don't really do a lot of Excel consulting in the sense of just building somebody's workbook and sending it over.
I tend to be a lot more pedagogical in terms of walking people through what they need now, what they're going to need later, and then really building that path along with them.
Gotcha. Gotcha. Well, with everything you've got going on, I'm surprised you're able to find enough hours in the day to do everything.
But outside of writing the books and doing the consulting and all the work that you've done, around this, what's something that on the personal side, maybe that most people don't know about you or something that we couldn't find just by Googling you?
Well, it is on my website, but I don't know how many people find it.
The origin of Stringfast has some different meanings.
The most obvious to, I think, more technical -minded people would be strings as in computer science.
In fact, I do a lot of writing and that on tech.
But it does have another meaning, which is a string fest as in musical instruments.
I've been playing the violin since I think I was like eight maybe, and the guitar since I was like 13.
So it's been a long time.
People ask how many years I've played these instruments at this point.
I can't do the math.
So I do that a lot.
I've been trying to do it more.
So music is a big part of my life.
I won the King Oscar sardine haiku writing contest.
This was pre -generative AI.
So I had to write this haiku all by myself.
And they sent me this lovely pewter Viking longship that I get to have at my desk now.
And that was from eating King Oscar sardines.
So that is my literary claim to fame.
That is beautiful. That's awesome.
So I'm guessing if you're writing haikus and you play instruments, do you write music as well?
I'd love to do more of it.
That's on my list to get into songwriting.
I've never tried it myself.
It seems like it's something I could handle, but it's one of those.
It's like learning a language or learning Python, right, where if you don't see that learning pathway, it's really hard to know where to start.
Yeah, I think the trick is when they turn one of your books into a movie big short style.
Who would have thought big short would be a movie?
One of your books has turned into a movie, you could do the score.
You could have a very exciting analytics score.
Or at least play at least play in the orchestra performing it.
So this other question, it's funny, we ask everybody and I'm always curious to see and we have some kind of go -to answers that most people give.
And then there are some people.
If I talk to a CFO who's been a CFO for 20 years and I ask them this question, mostly they're like, I don't know because people hand me things in Excel.
I don't get in there.
But the question we ask everyone, I'm sure you know it's coming is, what is your favorite Excel function and why?
Yeah, I'm going to off -road a little bit here because my favorite Excel function isn't technically a function.
But I love the spill operator.
If anybody's played around with the pound sign that lets you refer to a dynamic array.
So you're not referring to an individual cell, you're actually referring to the whole array.
What I love about this is that really becomes your gateway between Python and Excel.
Because anything you write in Python, if you bring those results into Excel values, Python is an object -oriented programming language.
So it stores things in objects.
In Excel, we don't really have objects.
We just see what you get.
So you want to convert your Python objects into Excel values.
What that spill operator lets you do is take the results of those Excel values that were generated from Python and then you can refer to them elsewhere.
So let's say you build some kind of a model in Python.
You want to feed that into some kind of a chart or whatever downstream analysis you want to do in Excel.
That's really that link that's going to let you build between Excel and Python and vice versa.
So that's the one. I guess the opposite one of that would be the py function that lets you take Excel data and bring it into Python.
But I really like...
So I guess that would be the more vanilla.
That's actually a function.
But I was thinking more of that spill operator.
And it's funny because I think about all the...
Index match, I think, is probably our most popular response there.
But as the abilities in Excel just morphs and changes over time and more people get the skills that you're teaching in your books, I see in the future, over the next few years, we're going to start getting more and more responses like what you just gave, rather than the traditional old school Excel
stuff because it's going to be integrated and people are going to work through and have different ways to do it.
And certainly your books are a great start down the road of having that level of understanding.
And so we'll put in the show notes, we'll put the links to your books and everything.
But if anybody wants to get in touch with you about Stringfest or anything else you have going on, what's the best way for people to get in touch with you?
Yeah, so stringfestanalytics .com is my website.
You can get in touch with me there.
You can follow it. Subscribe to the newsletter.
I post pretty often on all topics, data analytics, and Excel.
I'm also very, very active on LinkedIn.
I post basically every day there.
So people are welcome to follow me there.
Speaking of LinkedIn, I do have a couple of LinkedIn learning courses on AI and Excel.
There's one on Copilot and one on just miscellaneous AI tools and Excel.
So if you're connecting on LinkedIn, you may as well hop over to that LinkedIn Learning page and check out those courses.
So those are the two major ways that I stay active in the community.
Great. Well, George, I really appreciate having you on the show.
Love your books, keep up the great work.
Can't wait to see the next one.
Awesome. Thanks very much, Glenn.
