The Big Data Knowledge FP&A Pros Need, Bad Excel Education, and the Function Everyone Should Use
Jordan Goldmeier started out with a degree in accounting, and went on to become one of the leading experts in data science and Excel. Recipient of the Microsoft Most Valuable Professional Award, Jordan is one of the leading global minds on data science, data visualization, and analytics. Jordan currently works with Fortune 500 companies and institutions. His latest book, Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning is a #1 Best-Seller across multiple categories.
In this wide-ranging interview for FP&A Today, Jordan gives his outspoken views on whether FP&A pros need to know coding to advance their careers; the key things FP&A leaders need to know; and the problems with Excel education. He also reveals the most beloved Excel function he would die on a hill for.
FP&A Today is brought to you by DataRails. DataRails is the financial planning and analysis platform that automates data consolidation, reporting and planning, while enabling finance teams to continue using their own Excel spreadsheets and financial models.
Paul Barnhurst (PB) Host
Hello, everyone. Welcome to a brand new FP&A podcast. I am your host, Paul Barnhurst AKA the FP&A guy, and you are listening to FP&A Today. FP&A Todayis brought to you by DataRails, the financial planning and analysis platform for Excel users. Every week, we will welcome a leader from the world of financial planning and analysis, and discuss some of the biggest stories and challenges in the world of FP&A. We will provide you with actionable advice about financial planning and analysis. This is going to be your go-to resource for everything FP&A, I am thrilled to welcome today’s guest on the show: Jordan Goldmeier. Jordan, welcome to the show.
Well, hello. Thank you for having me.
PB
It’s our pleasure. We’re really glad you could join us. So let me just give you a little bit of background about Jordan and then we’ll kind of jump into things. So Jordan currently is located in New York city. He grew up in Ohio, and has a degree in accounting. He is a Microsoft MVP and runs the website advanced at Excel. He’s a lecturer, consultant trainer, and he’s an author. He’s written several books, including Dashboards for Excel, Advanced Excel Essentials, and Becoming A Data Head. And recently he just announced his fourth book. So Jordan, why don’t you maybe start and just, you know, tell us a little bit about yourself, your background. I understand you have a degree in accounting but you do a lot in data science now.
So maybe if you could just tell us a little bit about your story?
Jordan Goldmeier (JG)
Sure. So, you know, it is interesting. I did get a degree in accounting. How did that happen?You know, it really, I mean, really, it all started back when I was 10 years old and I used to go to the library and I would check out programming books. I really liked programming and I would read them. And, at that point I had grown up, religious as an Orthodox Jew and so we didn’t use electricity on Saturdays. So what I would do is I would read the books but I couldn’t actually do any of the code. So I would think a lot about the code. Okay. So for years and years, I was like this programmer, but then, you know, Y2K happened and it just seemed like everyone who I knew was programming they were struggling to get work. I mean, the programming world was definitely shifting. And so at the dawn of going into college I considered that maybe programming was not the best way forward, maybe it didn’t have the future that I had expected. And so in the end, I still minored in computer science because I still loved it. So, I did get the accounting degree and then I also got the finance major too. Because it was just one more class I think. So, I got the dual: the double major and the minor. And then I tried to be an accountant. I tried to work as an auditor for the air force. And I did a lot of great data mining for them and I redid their office macros, which got me some, you know, acclaim like as a junior employee. I had redone their macros. And so then they ended up on everyone’s toolbar across the world. It was significant because I was still in school. And so I got a, I got like a little award for that. And then I was on another audit, where I helped the government save $60 million. And I have, I have this award right here. I don’t know if we’re gonna show this on the screen, but I have it right here. I got this award, though they misspelled my name.
PB
I’ve experienced that a few times.
JG
That’s what happens when you play with the props in your room,and show them, but in any case, uh, so you know, I was doing that, but the thing is, and I’m making it sound like I was a good auditor, but I was not. I really did not know what I was doing. I mean, to be honest with you, like if it wasn’t programming, if it wasn’t using Excel, in some way, uh, I didn’t really know what I was doing at all. And I’d sit on these audits and they’d say, well, I, you know, it’s like, well, why didn’t you do this or that? And they’d be like, well, I didn’t really understand the guidance and I’d look at the guidance. I’m like, yeah, I don’t get it either.
I just really, I felt very sympathetic to the people we were auditing and I don’t think that’s what makes for a good auditor. So, I decided then that I was like I can’t really do accounting.
Jordan Goldmeier
Like I just don’t know. I just really, I felt very sympathetic to the people we were auditing and I don’t think that’s what makes for a good auditor. So, I decided then that I was like I can’t really do accounting. So I’ll go back into this idea of modeling. I’ll find something that lets me be a VBA coder. And that’s when I landed this role with what they called the Operations Research Analyst and that’s where I met my co-author of Becoming a Data Head, Alex Goodman, who was also hired for that. And, you know everyone was really good at math there, but not everyone was really thinking as a programmer. They didn’t necessarily have that background. So in the O.R (operations research) space, it’s a lot of like, just finish it, get it done. They’re thinking about operational complexity, but not about things that could slow a spreadsheet down.
And then the other thing that I did not realize is that all this background I had in business and particularly with the government would come in handy. That at the Dawn of data science or before we called it, that there was this need, as there still is, for domain knowledge. And I had come in with that, that other people who had graduated did not have. So, you know, it just kind of was a lot of things that I got lucky and it worked out, you know, I didn’t, I couldn’t have planned it. And then eventually people started calling me a data scientist and I was like, okay, sure. I’ll take it. You know, at first I wasn’t, cause I’m like, I don’t really do science, but you know, some of the work I did was hypothesis testing. So I consider that part of science. But, you know, for years I was like, well, I’m more of a practitioner.
PB: No, thanks. That’s a great story. I love, you know, you kind of sharing that and you know, being sympathetic toward the people you’re auditing and not understanding the guidance
JG: No, no. And it was like, you know, it was like that first big moment, where I was just like, did, did everything I planned, was that all wrong? You know, I wouldn’t realize that I would have that moment many times later. But like that was one of the first big moments like, wow, I really, this really isn’t working out how I imagined.
PB: Yeah. I’ve had a few of those in my career. I think we all, we all go through some of that. So you had mentioned as a 10 year old, you were re reading programming books. So you were always drawn toward computers and programming? Is that just something you kind of grew up with your whole life then?
JG: I was definitely always drawn towards computers. I mean always like if someone had a computer at their house, I was there. I mean, I remember I was babysitting my cousin and they called up to check to see that I was actually watching my cousin and not on the computer and I was definitely on the computer. So like, you know, like I, I, I was very obsessed as a young person and I think I just checked out a book once I saw, like in the children’s section on Programming for Kids. And I was like, this is cool. This is how they do it. Like, I always wondered how you put something on a computer? And I don’t know, I was always just messing around with computers and in school to the point that the instructors didn’t really know what I was doing. So they’d always yell at me, you know, because it always seemed like I was breaking it. I should say I’m always interested in business too. Like I wouldn’t say I was always interested in accounting though.
PB: Sure. No, that makes sense.
JG: I mean I do finance, I enjoy numbers, enjoy business, but the hard accounting stuff is not for me. That’s why I’m glad we have plenty of accountants out there that enjoy doing what they do.
He wanted to write a book for managers. I wanted to write a book for analysts. And so we just brought it together.
Jordan Goldmeier
PB: For sure. So great. So, you know, Jordan, you know, recently in the last year you published your third book on, uh, becoming a data head, you know, I’ve seen great reviews from that on Amazon. In fact, I even have it here. You’ll notice some on my desk. I have just started reading it and you know, I really enjoyed the book. Can you tell me kind of maybe take us a little bit through that journey, how you ended up writing that book, why becoming a data head was, you know, the book you chose?
JG Sure. So I mean really the answer to that question picks up from, you know, where I left off when I was working with the air force, with Alex Goodman. He was an operations research analyst and you know, Alex and I became friends really quick. And one thing that we would do is we always just felt like, because we were ending up explaining a lot of things to a lot of people and Alex was always great at explaining math and I, you know, I, you know, cause I can write, I’m like, okay at explaining just concepts that I can put words to. I know that sounds like nonsense. Like in this moment I can be very eloquent and show folks that, but uh, you know, I just, I, I don’t really always get the math. I’ve taken the GRE and the GMAT, the GMAT, my math score was so abysmally low.
It’s hilarious that I work in the space, you know. I failed linear algebra multiple times. I don’t really take to it as easily as others, but when I get it, I feel like I’m able to explain it. And when I understand it, I can relate it to things that people, uh, I have a sense that they understand. Because I feel like I know what they’d want to hear. And so really with that with both of us together, for years, we would attempt to explain things to each other, you know, like hard concepts. And I think the first one that he tried to explain to me was the Monty Hall problem. Cause I had just seen that movie about, was it 21? I don’t know. But we were just talking about how silly it was like in movies, how they present statistics and you know, there’s a very particular way in which I understand it and there’s a very particular way in which he understands it.
And they’re just not exactly alike. So like, you know, over time we did that, we did it for vectors. Like how do you explain anyone in, I, I vectors, I don’t know if you know what that is, but I barely know what it is. So, you know, like, and so over time we were realizing that people in data science really were not, they just didn’t understand what they were doing and the people who did understand, owned the ability to fine tune it and ask the questions about it. Meanwhile, you know, the business side is like bragging about it and not really understanding what’s going on. And so all of these things together create kind of a mess. And that’s why many data science projects fail. I mean, not to get too far into that subject, but like that is one of the reasons.
So we felt like, you know, this needed to be a book. He wanted to write a book for managers. I wanted to write a book for analysts. And so we just brought it together. We said everyone needs it. You know? So, um, that’s kind of how it came about, but I should say the conversations happened for 10 years. I mean, 10 years we talked about this book then. Then, you know, he had put a chapter together at the beginning of 2020. I remember I was on a plane and I read that intro chapter and I was like, oh, I’m writing again. And I just like cranked out like how it can lay out. And then we sent that in, and the editor had said, oh, we really like this. So let’s go forward with the book. And then what happened was the pandemic happened like that week, you know? And, uh, I freaked out, I was like, this book is full speed ahead because I might need this, you know. I don’t know what my future’s gonna be. So, that’s kind of like really how it happened so quickly. So, we wrote it during 2020 and then it came out in 2021.
PB: Great. No, I appreciate that. That’s a great story. It sounds like, you know, it was several years in the making, but kind of came together quickly with the time of the pandemic and having time to write there. So what would you say is kind of the key takeaway? You, you hope people get out of the book, you know, if somebody reads it, what do you hope they come away with?
JG: Well, I think that the reason people might read it is they don’t feel like they have an understanding of data science on the one hand, so on maybe the business side.And then on the flip side, a person who actually is really good at data science would really enjoy it because as we know that nerds do like things well explained, even if they already know them because it helps, you know, other people explain it. Well, I say this as one who does like that. And, so, you know, it gives them a voice as well and also speaks to many of their problems. So I guess fundamentally the thing that we want people to walk away with is just being able to talk about data better. And you know, that doesn’t come from necessarily being a PhD. We came up with this idea of a data head as someone who’s an active participant in the conversations in his data literate and knows what these things are. He knows what they do and knows how to ask questions about the results and how to argue with the data. So they don’t necessarily need to be anything special. They just need to use what they already have, which is their brain.
PB: No, I love that. I love that you said use their brain and you know, not everybody needs to be a data scientist, you know, kind of interesting Recently, I saw, you know, an active person in the finance space do a poll on LinkedIn where over 60% of the people said that in finance, they think they need to be a data sci data scientist. And they had like, you know, 1500 votes. And then he went on to ask, you know, how many think they need to learn a programming language, you know, and 50% said they need to use R or Python. And it kind of got me thinking, you know, probably less than 5% of people in FP&A, maybe 10%, but a very small percentage are data scientists. Your book got me thinking about, okay, do people really need to be data scientists or do they just need to understand data, know how to work with it? Talk the language to the data scientists and do their analysis? So maybe if you can maybe talk to a little bit about that and your thoughts of the kind of data science and FP&A
JG: Sure. I think that’s a great question. You know, when you think about what a data scientist is, I’m sure you come up with many answers and like, we didn’t actually define data science in that book. We made an effort not to define it. Because look, I mean, on some level, maybe I’ll get, hate mail for this one, but who cares? Like, I mean, who cares anymore? Because like we started as operations research analysts, big data was coming about. Then, you know, I said, I worked with big data because I wanted to get a job, you know, did I really work with big data? Maybe a little, I don’t know. I mean, did I work with big data sets? Yeah, I did on occasion. I think that like, what is, and isn’t a data scientist. It gets people who maybe are in finance who think, well, this is the future of data and I wanna make sure I get promoted and I do the best stuff and I run the best models. That might lend them to wanna become a data scientist. And I think too there’s a lot of confusion because when you, if you get a master’s in finance or you do something in quantitative finance, you’re doing a lot of algorithms, you’re doing a lot of programming. You are thinking like a data person, same thing. If you get into a quantitative economics program, or a physics program, or something like that. Well, suddenly, you know, someone who’s a data scientist comes to where you’re working and they get to run the cool problems and you’re doing the stuff that’s “finance”. Right. and you want more of that. So I think that there’s something to be said about that, that, like the underlying view that maybe data, a data scientist has more range as it were than someone who knows quantitative finance.
if I were to go out and get a job, it probably would be an FP&A, because I love modeling. I know Excel really well. I can think in terms of data.
Jordan Goldmeier
I guess what I’m trying to say would be driving it. I think that really, for me, you know, I landed this operations research analyst role, but if I were to go out and get a job, it probably would be an FP&A, because I love modeling. I know Excel really well. I can think in terms of data. You know, is that pure data science? It’s hard because data science is so concerned with machine learning. And I think finance is more concerned with forecasting, but they’re all part of statistics.
I guess really what people need to do is instead of looking broadly for the skills that a generalist could have, they kind of have to look at their career, where they want to go and their current job and look at what you know, where they need to go next. And maybe it does require a data science degree or maybe that would help. If those are the things that it requires and it would be worth it to do it, but I would not say it’s worth it simply just to want more in your life, you know, to want like there are better ways to look at it. So I hope that that rambling answer, I do think that there was some practical information in there.
PB: Yeah, no. Thank you. I appreciate that. You know, and that helpful and that, that’s interesting that you mentioned you would do, you know FP&A, if you weren’t doing what you’re doing today and you know, there’s definitely some overlap, you talk about modeling and Excel. So maybe let’s take a minute and talk a little bit about Excel. You know, there’s obviously rampant debate all the time about Excel and data science and kind of, you know, where, where Excel fits into all of this? So maybe talk a little bit about your, your kind of journey with Excel and how you think about Excel when it comes to, you know, data analysis, data science and, and those things.
JG: Well, I think that’s a, you know, that’s like really a heated thing right now, right? Because I posted something on LinkedIn that was like Excel, is Excel a data science tool? And I think 20% of people said yes, and people who I respect were vehemently opposed. You know, it’s interesting because everyone kind of agrees, whether you say it’s a data scientist tool or not everyone agrees that Excel has a specific place. You know, some people just don’t agree whether that’s data science or not, or whether a data scientist needs to learn it with so many other tools.
You know, again, like the simple practical answer to this debate is it just all depends on who you are, where you work, what you do. What’s the traditional tool set? What are the things that people wanna see answers in? You know, no one wants to put stuff in no one like goes after PowerPoint and yet PowerPoint is like the major, extra step.
It’s like always an extra step. You know, why do I have to take what I did here and make it look worse in PowerPoint? Just so I can click in, you can pay attention to someone else. Okay. Or like, you know, your email, your phone, this is why I can’t work in a corporate environment as a worker. I can do it as a consultant. But this is why I’m not necessarily a good employee. When you think about it.
I don’t know why Excel is a bad choice here. I think it’s also really good for certain financial models. I’ve taken a look at other tool sets, the one that are the “Excel killers,” I am largely convinced that they don’t even come close
Jordan Goldmeier
So to me, I think that Excel has a, has like a, a lot of great things. It can do. Like it can prototype a big idea. You can get people a sense of like, what is it gonna look like in the end? Yes, it is true. You can do that in Python and in R but you can also do it in Excel. I don’t know why Excel is a bad choice here. I think it’s also really good for certain financial models. I am largely convinced that, like, though I’ve taken a look at other tool sets, uh, you know, the one that are the “Excel killers’, I am largely convinced that they don’t even come close to, like they have a very specific modeling type. And either you have to hire their consulting team to build a complicated model for you within a program, which means that I don’t know if that’s like a better solution, maybe it is for you. Or they just don’t really, they have a very specific type of modeling that, and this is all they can do. And so with the companies I work with, you know, they’re, they usually run a portfolio of something. And the financial forecasting that they do is just so complicated that it is made easier to understand and more transparent by Microsoft Excel. And there’s no, I, I like this subset, this slice of niche that I support, this is real and I don’t care what these other data scientists say, because I know it’s real and I know I support them.
So, you know, I think that there’s always a place for that. Like that’s just one slice. And, as far as the data science toolset goes, I think every data scientist should know it because it’s easy to know. And you know, being a data scientist today means knowing a lot of different things, and this is one of them, you know, and like there’s no reason people say Excel is the devil to me, I’m like, geez. You know, like, uh, like I post stuff on, on Instagram and they, they remind me that they hate it. And I, and it’s just kind of like, it is the most used software program in the world. So I think that in some cases it’s like, it’s, it’s hating the sports team that does the best as well. Yeah.
PB Hating the dynasty, the Cowboys, the Yankees, the Lakers, whatever it might be.
JG: Exactly.
PB: Here at FP&A today we’re huge fans of Excel. I’m a big fan of Excel, you know, I agree with you for financial modeling. It’s still the standard in the world. There are other tools out there, but you know, 95% of, you know, all the modeling is still done in Excel. You know, obviously there’s times you may need other tools for database and backend and whatever else, but you know, it’s a key part, especially in FP&A, if you ask any professional in FP&A they use Excel, you know, you might find 1% that the answers no, and that’s probably high. So, you know, I agree with you. I appreciate that answer. And I know recently you launched, uh, a course, Advanced Excel and I’ve actually had the opportunity to take some of that and have been really enjoying it. And you know, it’s been a great, great course so far. Maybe can you talk a little bit about why you thought there was a need for that course, you know, who, who kind of your target audience was and what you hope people get out of it?
JG: Sure. So my target audience, I mean, is really the FP&A community because, you know, in my years working as a data scientist, in one of the roles, I remember I worked in the decision science role and we worked right next to the FP&A people. And we were often working together a lot because they were doing a lot of things that were very similar and, you know, we helped each other a lot and they were definitely using Excel. And so over the years, having like, you know, worked in an operations research department, worked in the more like finance marketing side of data science than worked in like other areas, project management and data science, you know, I realize that there’s really this skill set that a lot of people who I guess would define themselves as intermediate Excel users, that there was more that they wanted to learn and everyone who was so convinced it was VBA.
And while I love VBA, I think there’s a lot more you can do without it. And so I felt like there was really a need to get people to think differently about what advanced Excel looks like and, to use a lot of the pieces of Excel that they weren’t taught in school and Excel knowledge is very traditional in the sense that you go work somewhere, someone tells you what to do. What I’ve learned in these organizations, because you know, having become like the Excel expert is that there’s often another Excel expert there and they don’t like me, you know, they’re like, this is the way to do it. It’s like, okay, well let me just show you, you know what I’m doing, what I’m thinking about, which comes from experimentation, which they weren’t doing.
So like, you know, I don’t really know. Where I’m going here is that if you get all that traditional knowledge, you would only ever use the things that you were taught. Right? So only use V- Look Up this way. I think that this is demonstrated best when I interviewed to work with a training company in New York. This is in the early days of trying to get this going. I was teaching very basic Excel. And so like, this was one of my lower paying gigs and one, the person there was quizzing me, which, you know, I didn’t love. But like they asked me something about, well, V Lookup won’t work when? and I was like, when, what they’re I’m like, I can’t see an issue. And they’re like, well, when they’re duplicates, I’m like, it still works.
They’re like, well, you won’t, I’m like, they’re like, well, who would ever use it? And I was like, if you sort the list, you may only want the top item. Like I’ve used it that way before. So I don’t really, you know, I guess what I’m trying to say is like, this is the sort of traditional knowledge that I’m, that I’m bumping up against, you know?
And so I think that finance people do know that there’s something better, bigger out there because they’re, uh, you know, they love Excel and they love learning, like me. And I think that really these are the skills not to like sound like a salesperson, but these really are the skills, that will make you stand out because they are focused on, on data visualization, automation, focused on using all the tools and the pluses and minuses of each and you, the, it really allows you to meet a lot of the different problems. So you don’t have to be like, well, I only know how to do this.
PB: I like that. And I love the fact that, you know, so many of us learn Excel from others we’ve worked with, you know, most people have probably not taken formal Excel training. It’s getting better, but many people just kind of learn it on the job and you don’t even realize that, oh, Excel added these 20 new things. Oh it has this thing called power query or Lambda right. Or data types. The list goes on and on. If you look at the last couple years, I think it’s great that, you know, you have a course out there that brings awareness to a lot of things. People don’t know about, you know, X lookup, you know, there’s always debate. Is it V Lookup? Is it Index Match? And now it’s like, why aren’t you using X lookup? And I love, uh, Oz de Soleil. One time he’s like, can we just end this battle? It doesn’t matter. As long as you’re getting the job done, who cares what formula you use?
I love Merge and Center, you know, I love Merge and Center. Don’t edit that out, please. Like, I’m just gonna say it again. Merge and Center it’s satisfying. Okay.
Jordan Goldmeier
JG:
I agree with that. You know, I used to care about stuff like that. I just look at it and I think, look, everyone’s gonna come to this in their own way. I have things that people just vehemently don’t agree with me. I love Merge and Center, you know, I love Merge and Center. I hope if we can, don’t edit that out, please. Like, I’m just gonna say it again. Merge and Center it’s satisfying. Okay. You highlight the whole thing, you hit it and it’s just like, it comes together. But I do wanna say, you know, with this advanced Excel thing, I was looking at it like other universities who have advanced Excel classes because sometimes I’ll pitch myself to them and we’ll run an open enrollment. And if you just look at an advanced Excel curriculum top today, okay: Macro recorder, data tables, goal seek. Stuff people should never use. No, I shouldn’t say never, but, you know, like we don’t need to teach this anymore.
I mean like, macro recorder should not be considered an advanced skill. I’m not saying it should be considered a beginner skill, but data tables are not something that I think anyone should really use. They’re very dated. That’s my take on it. You know, like sheet tabs, stuff like that, or not sheet tabs, uh, chart tabs. This is considered advanced content.Or you know, using filters on a pivot table when we have slicers. People are still teaching Excel of 1999 or maybe 2003.
if you just look at an advanced Excel curriculum at a top university today, okay: Macro recorder, data tables, goal seek. Stuff people should never use. No, I shouldn’t say never, but, you know, like we don’t need to teach this anymore.
Jordan Goldmeier
But like I that’s, what I think is, is very scary out there. And so that’s why I’m trying to remind people that this thing actually has improved over the years.
PB No, that’s great. I’m glad, you know, I’m glad you’re, you’re doing that. Because there needs to be that, you know. I’ve done something similar recently, and taught one of my first courses and we talked about advanced Excel and dynamic arrays and X Lookup and almost nobody even had a clue what they were like, what, you know. They say ‘they redid the calculation engine, excel works differently than it used to?’
JG Right.
PB You know, and it’s just, it’s, it’s amazing to me, you know, people in finance that use it every day, how little they know. So I think it’s great that you’re working to change that and help people be more efficient in their job because it makes a huge difference when you can automate and find ways to reduce the amount of time it takes to do something.
You know, there’s kind of two things that, you know, FP&A deals with a lot. And I would just like to get your thoughts on both these and we’ll start with analysis, right? That’s a key part – financial planning and analysis. We’re doing all kinds of different analysis, whether it’s looking at a marketing data set, you might be sitting with the operations team, or a call center.
Could you walk through some of your advice for someone earlier in their career? Where they start just starting to work with data. How do you go about a project to analyze something? Maybe just some ways people should think about that?
JG
Sure. So I think that like, you know, when you’re young, um, at least when I was young, I always sort of dreamed of the solution before I knew what the problem really was. And so like what happens is yeah, so you’re laughing cuz you know, I’ve been there. Um right. And so like someone gives you a project and it’s like, here are the things we wanna find and that becomes like your focus. And I think the broader thing is to always take a step back. And actually it’s not that you have to forget what other people say, but you just have to take in kind of put it on a shelf and remember it, but also to come in with fresh eyes. And remember that data is interesting. Technology is interesting. Solutions are interesting. But the most important part of a business is people.
And so to really understand the data, you have to talk to the people who really get it. And maybe my auditing days helped me deal with people who didn’t want to talk to me. But what I did learn is if you’re not an auditor, they do like to talk to you. So I think that, you know, the broader thing is with the project, you need to figure out the problem it solves. It’s not like everyone says, well, we need a dashboard, we need this or that. We need a model. If only we had a forecasting model that could do this.
Okay, well let us take a look at the problem. What is the problem? And let us look at the objectives of what it’s trying to solve. So an objective is usually something that is, could be minimized, maximized or optimized, right?
Do we want to increase customer satisfaction? Do we wanna decrease recidivism of, uh, I don’t know, whatever. Do we want to decrease return rates? Do we wanna know how many people, a trip do this or that? I don’t know. I’m just speculating. Sure. But I think you get the point. Yeah.
Anyone can code something up, you can hire anyone to do anything. You can get a program to do it for. These days, what is really meant by analysis is what I’m describing is, you know, really critically thinking about the problem, getting the story of the situation.
Jordan Goldmeier
Those are the problems that you’re trying to solve with data. So I guess what I really wanna get to is that you wanna figure out the objectives and then you wanna figure out what is the value to the business. You know, even if someone says, do a dashboard, do this or that really it’s important for you to actually define what, what is the effect that’s going to happen?
And then I think finally, like I said, with people, you should really take time and understand the people who are affected. What does it mean for the business process? Who’s gonna run it? These are really actually the most important things.
Anyone can code something up, you can hire anyone to do anything. You can get a program to do it for. These days, what is really meant by analysis is what I’m describing is, you know, really critically thinking about the problem, getting the story of the situation.
PB: Thank God. I love your answer. I love the emphasis on, you know, one critically thinking people that it’s about the people it’s about talking to ’em understanding how it impacts people and understanding the objective. Right? I love your story at the beginning of how, you know, you think of the answer, you have this grand idea. I’m gonna build this master model or master spreadsheet, and you really don’t even fully understand what the problem is. And you know, you kind of run. I think that’s great advice to really step back. Think about the big picture, the, about things critically and how it impacts people. Understand that objective before you just start diving into data. Like you said, any, anything can be coded. Any program can be written, but you know, the human element is what we bring to that. That’s so important. So thank you. That’s a great answer. I appreciate that. You know, another thing we obviously do all the time in FP&A is charts, graphs, putting together presentations. You mentioned PowerPoint, right? You get it all nice in Excel and then you get to go put it into a second system as, as you mentioned. So I know you wrote one book about visualization dashboards for Excel and you talk about that in your course. So maybe some, you know, some of your tips and advice you’d offer to people around, you know, visualization and just how they should think about that.
JG: Sure. You know I have lots of thoughts on this. So the first thing is because there’s like this visualization journey for an analyst, so first you don’t know anything. And then you learn that other people have thought about charts and graphs and you’re like, yes, that’s cool. So you go take a look at that and you get really deep into that. Start reading Steven Few, who wrote Information Dashboard Design. Yeah. You have it.
Yeah. Fantastic book. And also now you see it as a great book. Um, and then of course, Edward Tufte The Visual Display of Quantitative Information. Then what happens is you become, as I did and know- it-all. Then other people coming up are afraid to build stuff, because they’re afraid you’re gonna rip it upon. Okay. So like, and then eventually you realize, well maybe pie charts aren’t the worst thing, maybe they’re the third worst thing. But they’re better than nothing sometimes. Um, not always. But like I guess what I’m trying to get is you, can you soften your approach, you have strong opinions held loosely. So if you can get to that last part really quickly, instead of going through all the other parts, you’ll be better off.
You really just wanna connect your chart to the message of what you’re trying to say. You know, color design, these things do make a difference, but they’re not something you need to get a master’s degree in
Jordan Goldmeier
I think that on the one hand, people get a little, uh, analysis paralysis thinking, well, is this perfect? Is it this or that? You really just wanna connect your chart to the message of what you’re trying to say. You know, color design, these things do make a difference, but they’re not something you need to get a master’s degree in. You really just need to read one book on it and that’s it. And then you can decide for yourself what you want to do. I find that a lot of people who talk about these books and then in practice, they don’t actually do things that are a good aesthetic from a data viz perspective. I do think though we did not make any mention of this in Becoming A Data Head, I did go through and make these charts, data viz nicely, uh, visualized, because I knew how important it was that they would not like that they followed some nice data visualization principles.
So like we, I really did try to make them sort of minimalist focused on the data. I would say for people coming up in this space, just read a data viz book. Get your head around it. You don’t have to agree with all of it. And then the other thing is just even if you don’t have a Data Viz book, just remember that as analysts, we want to tell an entire story. Like we want to tell everything and if I can make an analogy to the book, you know, Alex and I turned in 399 pages, the final book is like 210. So I have come to a place in my life when I do the big chop. Right. I build everything and then I snip it out and I optimize it. And people are just beholden. Yeah. How many pages is it?
PB: 213. You’re really close. 210 before the “what’s next” section.
JG: Yeah. Right. And so people just get very beholden to their stuff. They don’t wanna let it go. I built this and that, you know, there’s, there’s later on down the road, you can always reuse it for something else. You can always spin it out and turn into something bigger or something else. I guess people are just so focused on telling the entire story that they forget that there’s really only one story they need to tell. And that is whatever the most important story is. And really, they should be taking their energy and focusing on that specifically.
PB: Thank you. I love, you know, some of the actionable advice you gave there. One, just pick up a book and read it. There’s plenty of them. Don’t feel like you have to read one because everybody says it’s the best, but just pick one and they’re gonna have some good tips. You’ll agree. Like you said, and you’ll disagree. You know, I really like to connect your chart to the message you want to say, right? We could talk about colors and everything you need to do, but if you’ve really connected the message, that’s the key thing. If your audience can take away what they need to, that’s what matters.
You know? And so I really appreciate that. And you know, I like how you said you would kind of go through that journey where you get to the point where we think we know it all. We start at the beginning, you know, for me, I remember starting at the beginning and I cringe now thinking, you know, I remember, oh, exploding pie charts, this is new. That’s cool. Or all these bright colors that Excel has in the defaults. Yeah. That nobody can read. I’m like, I look back now and I’m like, why did I ever use that? So it’s a journey and that’s one of the things I’ve definitely learned over the years. So thank you.
Shifting gears here, just a few more questions. So, I have this book here at my desk, The 40 Greatest Excel Tips of All Time. There’s a section that you provided much of the information for. In fact it has a picture of you in there. It says Excel jokes with Jordan. Maybe if you can talk a little bit about kind of how that came about and your best Excel joke?
JG: Um, okay. So let’s see here. The original Excel joke came from Chandoo’s forum – link to the forum. We were all posting jokes. Okay. And I think I wrote something like an analyst walks into a bar chart, and then I had come up with this other joke, because I should say broadly, I’ve always loved humor. I used to read books on it because I thought I’d do stand up at one point, you know, I’ve done, like I’ve done like a few open mics. I like speaking and presenting now, but I was always bothered by the fact that the jokes in the accounting and Excel space were just like the same jokes. It was just like you could move accountants and replace it with economists, place it with Republican, replace it with Democrat.
You know, they were just like the same jokes that I go and hear, you know, someone else saying that about other people. And I just thought there’s gotta be like a way to be funny. And so Chandoo had that forum post. So I started thinking of all of these. And so I think the two that I thought of were an analyst walks into a bar chart. Ouch. And then it was like, uh, the other one was a pivot table, walks into a bar. Bartender says, Hey, should I start a new tab? So that was like the original. And then at the same time Rick Grantham and Oz du Soleil, we had just met and we were talking about the Excel podcast and Oz and I were just kind of trading. Cause you know, Oz is a bit, not a bit, Oz is a performer.
Yes. As well. And like, you know, he can do, he can, he can make jokes. So I started writing them down. Like we just started writing the jokes down and then I would just tell them to Alex at work. And I, and so like, you know, we just started, um, collecting them over time and then bill and I would tell ’em to bill as well. Then Bill [Jelen] was writing this book, this crowdsourced book and everyone was kind of like contributing and I thought, Hey, wouldn’t it be great if we had like a joke section in the back? And so I just sent him the jokes and I didn’t think he was gonna do all of them. Because I thought, you know, some of these are just so dumb, but you know, he did. So now there is that joke book in the back of there. And it’s so funny. Because people will say, Hey, you should write a joke book on this. I’m like, well, I kind of did. I think that I had talked to Bill’s publisher about how many books Excel would sell. And it was not, it was not high enough to get me interested in turning into something else besides what it is right now.
For anyone who ever gets too concerned about whether the joke landed or not just remember, it doesn’t matter. Especially in a corporate workshop where everyone is expecting you to not be interesting and to not be funny – any bit of humor is gonna go a long way.
Jordan Goldmeier
PB
Yeah. You might have a hard time with that. Although, funny story, I don’t know if you’ve seen him, there’s a guy on the internet (Matt Parker) that does an entire standup or routine around Excel. It’s a comedian’s routine.
I Did see that. Yes.
PB: Yeah. It gave me a good laugh. I was like, that was pretty well done. So I mean maybe you could go that route.
I could, I could do that. I mean, I have, you know, I have the ones that I like. Generally, most of my jokes I’ve come up with myself just as a point of pride. I should say my best joke is what comes after an Excel joke? a sequel! That one actually gets laughs like the pivot table one gets groans. I will say I’ve done a few like in front of data science, nerd audiences who are warmer to it. And they do better than let’s say a corporate workshop.
But even for anyone who ever gets too concerned about whether the joke landed or not just remember, it doesn’t matter. Like especially in a corporate workshop where everyone is expecting you to not be interesting and to not be funny, any bit of humor is gonna go a long way. My advice on that is just remember, don’t make fun of people and also don’t make fun of subjects that you may think are funny, but you know, other people don’t think are funny and you’re gonna make fun of them for being offended. Don’t just, don’t do that. That’s not funny. So like that’s my advice on using that stuff.
PB: No, I love that and great advice and appreciate the backstory and how that came about in the book. And you know, if there ever is a big enough audience and you publish one, let me know I’ll purchase it.
So just, uh, kind of two more questions wanted to kind of go over here. I know we’re kind of, we’ve been going for a little while now and a lot of great material we’ve loved, you know, the time with you. So first one is, do you have a favorite Excel function Or feature?
JG I mean, definitely my favorite feature is going to be Merge and Center. I mean, I’m just, I have to say Merge and Center
PB: I have to say I’m on the opposite end of that debate, but I’ll let you have that one.
JG: Okay. Maybe that one’s, maybe I’m being a little bit of a contrarian with that one. No, I love Merge and center. I would say it’s one of my favorites. I mean, to be honest, like it is, it is on my quick access toolbar where my favorites do go. So, I’m gonna stick with that answer.
PB
All right. We’ll get, we’ll give you that answer. Merge and Center It is. We will see what kind of debate we get from that one. So, you know, kinda last question here, what’s something that people may not know about you? Something they couldn’t find out on, you know, find out online something interesting for our audience.
JG:
Well, you know, I’m obsessed with a lot of things in life. So Excel is one of those obsessions. One of the other obsessions is hotdogs. I love hot dogs. I got books on hot dogs. I got this hotdog tattoo. You know, it’s a Chicago style, which, in case you care, poppy seed bun, Vienna beef, you gotta have, the Vienna beef or something with the snap. So that’s the natural casing. Then you gotta have that neon relish, the tomato, the onions, the sport peppers, and then technically this tattoo is wrong. Because the mustard should go on the hot dog and not on anywhere else. You don’t put it on the side, but you know, there it is on this. I’m okay with that. Then you gotta do celery salt on top. We are going to say my freckles are kind of the celery salt on that.
I love hot dogs. Everywhere I go, I try to get a hot dog, definitely from Chicago. Um, and then also like generally about food. I’m like that every place I go, I try to go to a diner. I love greasy food, you know, eggs, hash browns, breakfast for dinner. I eat breakfast all day long. So that’s kind of something like, you know, hot dogs, tater tots, grilled cheese, steak, all that stuff. And I know it sounds like I’m just listing foods, but I am obsessed with these foods, but hot dogs most specifically.
PB Well thank you for sharing that. I appreciate you walking us through the tattoo. That was fun. And you know, I hadn’t heard of celery salt on hot dogs. I do appreciate a good hot dog and you know, Chicago is known as one of the best and you know, one of these times, if I get to get out there and we get to meet in person, we’ll have to go have a good hot dog or, you know, go to one of your favorite restaurants there
I appreciate you sharing that. You know, I really appreciate the time you’ve taken today to meet with us and just any closing thoughts or anything you’d like to leave with the audience from, you know, kind of our conversation today.
I would just say that, you know, right now people are really evaluating their careers, right? So The Great Resignation is happening. Whether you were part of that resignation or not, you know, there’s still this major reflection, both from businesses and employees. And so this is a moment if you thought, well, I can’t do something and I’ve always wanted to do it. I do think this is a moment to go try it. Whether that’s like getting a promotion going somewhere with better benefits. If you think you can do that whether that’s taking time off to like focus on, on yourself, taking vacation from the grind, because working is a grind now. I mean, it’s really hard on a lot of people out there and it’s not as we’re learning, you know, it’s not good for your mental health to be this obsessed with work. So if you thought that you wanted to do these things, I would say now is a moment to start planning. You know what that could look like for you and to put yourself first.
PB: Thank you, I appreciate that. That’s great advice. And you know, when you talk about The Great Resignation and putting yourself first, I’ve recently done a career change, you know, starting my own business, something I’ve always wanted to do. And I figured if I don’t do it now I’m gonna do it and just kind of made that plunge so I can totally relate to that advice and really appreciate it. Well, Jordan, it’s been a pleasure talking with you today. We’ve loved having you on the show and you know, hopefully we’ll be able to have you back again sometime, but thank you and you have a great day, Jordan.
All right. You too. Thanks.