#228 Are Spreadsheets Still Relevant For Data Analysis? with Jordan Goldmeier, Author of Data Smart

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com I think a successful data mindset is the ability to think critically about the products in data. And just like the data space, everyone wants things to be clean. Data plays on this idea of objectivity. Oh, these are just the facts. But there are no real facts anymore. There's what you present. Even like a sensor that gets directly from a physical measurement still runs through people's idea of what it should say, of its precision. There's an interface between it, so we don't have true facts anymore. So that means that you need to be really skeptical about the things that are presented your way and the decisions you have to make.

Hello everyone. I'm Adele, data evangelist and educator at datacamp. And if you're new here, Data Framed is a weekly podcast in which we explore how individuals and organizations can succeed with data and AI. There's often bit of an unfair flak that Excel gets by data practitioners. Heck, I've been guilty of this as well. Why would anyone use Excel when they have awesome tools like Python, R, SQL or BI tools? But like it or not, Excel is here to stay. And there's a meme that's bordering on reality that Excel on its own is carrying a large chunk of the world's gdp. So today's episode takes a fresh, clean slate at Excel and poses the question, can you do data science in Excel? Today's guest really thinks that you can. Jordan Goldmeier is a data visualization expert, author and lecturer. He spent years in the data training trenches as the former Chief Operations Officer at Excel tv. He's the author of Advanced Excel Essentials and Excel Dashboards, among other books, and he is an Excel mvp. In his latest book, Data Smart, Jordan not only demystifies really important data science and machine learning techniques and concepts, but also shows you how to implement them in Excel. If you enjoyed this episode and the Data Frame podcast, make sure to rate it and wherever you get your podcast. And now onto today's episode. Jordan Goldmeier is great to have you on the show.

It's great to be here.

Thank you so much for coming. So I'm going to start off by saying that in many ways, your book DataSmart broke a lot of my preconceptions that I had about Excel. There's a trope in the data space that data scientists will refuse to do anything in Excel, no matter how easy the task is, even if it means just computing the mean of a column. And your book shows how Excel can be a very powerful tool for data science. So maybe to start off and set the stage, how relevant or not relevant is Excel for data science?

I think that the way Excel presents itself in data science is continually changing. So much of the critique that came, really, if you think about the history of Excel, first it was like your choice was Excel or matlab. Your choice was Excel or S or Jump or something like that. So it was either you do a lot of work in another coding language or you try to make it work in Excel and it's just your computer's freezing and overheating and catches on fire. So I think what happened was all these new open source technologies came about, so Python and R, and they were still in the executive level leadership this idea that we always need Excel. So this developed a lot of hatred from data scientists who were forced to use this product, who were like, this is not the right way to do this. And having lived on both sides of that, I think that Excel does slot in very nicely within data science and that many data scientists are at their own expense, not including it in one of the skills that they should offer. So for me, assuming that it does continuously evolve, but the way Excel really slots in, as we talk about in this book, is creating algorithms where the components are actually very visual and very seen and they have formulas associated with them. So you can really understand the logic and you can walk someone through it. And as well as if you were going to start a data science project and you had to communicate it to leadership, you can come in with all this Python code, you can run a jupyter notebook, and maybe that works for your next level manager because they were you five years ago, but for their manager, their manager's manager, they just want the facts and they want something they can click on. And even if they are programmers, chances are like, I have my own company, I have staff, so I'm a CEO and I tell them all the time, just the facts, right? I only want the facts. And so Excel gives you just the facts. If you do it right, and you.

Know, we're going to definitely uncover a lot of the use cases that could be relevant for data science and Excel. But you know, I mentioned that trope of data scientists not wanting to use Excel at all. But there's also another kind of really popular meme in the kind of data space of literally Excel carrying around the world's GDP on its back. Right? So Excel is widely used by a lot of folks. It has quite improved a lot as a tool maybe. Why do you think Excel still has a bad rap today despite all of these improvements? Kind of especially expand on that notion.

You know, I like to compare the like use of excel to the TI83 calculator. I guess it's TI83 plus now. I don't know what the kids are using. All right, Maybe they're not even using that one anymore. During those years when I was a kid and I'd hang out with these tech nerdy people who were coding, they'd have the jobs I want. Like I go to synagogue, ask them a million questions and I Talked about the TI83 plus and they went on this thing about how Casio makes the better calculator, right? That may be true. There may be products that do what Excel does better. But the way tech works in our world is it's not about what we idealize, it's about what people are using. So we can debate what the degrees that Microsoft made people use Excel or took them away from other people. But as you said, it does run the world's financial system for better or for worse. Like I can sit here and list all the things that it does well. You can list the things you don't like. It's not like you're wrong, but it doesn't change this fact. So I think if you are an analyst and you can send your hate mail to jordananarchy.com in the career field, the data scientist sits above the analyst on the ladder. But the truth is you're in the broad sense, you're analysts, okay? This is what you do. Use your brain to analyze this stuff. You need to know Excel. I mean, if you work for a big bank, you may be right about all these products and this and that, but over the years they've been using Excel. And so even though I love vba, like the macro coding language for those who don't know behind Excel, I wouldn't recommend people use it for most problems. But the entire banking system is run on it and there are cottage industries to approve that. But it Wouldn't change the fact that a lot of things that are important are run on it and people don't want to change those things quickly.

Okay. And you mentioned, you know, early in our discussion that there are quite a few use cases where Excel could be very relevant for data scientists. One is where have to build systems or algorithms, where it's very visual. One is when you have to show a prototype to leaders. Maybe walk us through these use cases in a bit more. And how Excel could potentially work with other popular tools in the data space, such as R and Python as well.

For sure. So I think it's not like that. I'm an Excel maximalist on this. All right. Like certain things you have to do, even things that we do in the book, like the book is more of a textbook to explain all these things. But we built a spam detector using the bag of words in a naive Bayes. Okay, so to classify. I wouldn't do that in Excel. It's not deployable in Excel. But if you wanted to explain to someone how it worked as a professor, as an analyst, as a manager, Excel is great. And if you didn't want to run the whole entire training set, but rather just needed to explain to someone using a small amount of data, Excel is great for that. Again, you can't put that in production, but it's good for that. The other thing Excel is great for is data cleaning. If you've used DPLYR and R or you've used Pandas in Python, it's very similar. But if you had to explain it to someone, again, what you can do in Power Query is actually much quicker than you can do with dplyr. And I love dplyr and I can crank it out, but again, it makes it a little bit easier to understand what's going on. I can for my own work use R, but for my clients I'm going to use Excel because talking to people who are finance people, they're not coders, but they understand what Power Query does. All these other technologies are the better choice. But there will be a point in the future where you could plop something in Excel, send it off to be worked on by some algorithm in the cloud and get the results very quickly right back. The same with Power BI as well. So it's good to know it now because that's our future, because the difference between these tools, like right now, is very obvious. But I think that we're going to reach a point where they converge.

It's interesting that you mentioned convergence because there's also another big elephant in the room here, which is generative AI. Right. We've seen a lot of, you know, even Data Camp has Data Lab which uses generative AI to fix your code to generate code. We've also seen, you know, Power bi having Copilot where you can generate charts, generate functions and Dax queries. Do you see that for most analysis use cases, we're going to converge on a set of tools that have a GUI interface, for example, in the future that leverage generative AI in the backend to come up with insights. Is that kind of the future that you see, as you mentioned, convergence here?

Yes, I still think there's going to be coders. I think it's really going to get the people who are analysts who are doing analytics, let's say. So they're less code and they're more reporting for them. It's going to be a boon and I think that it's going to fundamentally change the way we use Excel. And I have to say this with a sense of nostalgia because the way I used it was all about automation and so that way is going away and now it's going to be more like using Google. Like if I didn't know something, like if I didn't know how to code something back in the day, I use Google and people like how do you know this? Well, I learned how to Google. Now a next generation of people will learn how to copilot or learn how to use ChatGPT and this is going to help them and I'm going to be left behind. I'm going to have to move mentorship role because I'm not looking to master those skills. I'd rather help people who are mastering them get jobs, get the things that they want and live the life that they want.

And yeah, and how do you see, maybe use cases of generative AI and Excel, how do you see generative AI transforming the Excel experience?

Right now if you've got copilot, it's very limited, but you can just sort of see what they want. So if you've got Copilot preview, you can use an Excel table, which for those who don't know, an Excel table is actually kind of similar to a data frame in Excel. So not just a range of of data, but rather they call it a control T table. I used to call it a capital T table. So in that you don't think in terms of cells, you just think row wise and the same with the way you like dply or just add this to this column and Then it runs it down. So the big thing I would think is right now, if you want to add a new column, hey, do this column that does this. It will work in Copilot. Copilot can do Excel tables. If you say, can you highlight cell A7? It will work. Can you generate insights? Okay, it will generate insights. Those insights are terrible. They don't do anything. They're just like, this value was noticeably greater than this value. It's like, no shit. Yeah, you know, like, I don't know. It's like, this has the most percentage of this. It's not very good right now. But you can't imagine a future where you hit a button on your computer and the mic turns on you say, copilot, let's filter this, this, this and this. Cause that's someone's idea. Boom, it does it. And then someone says, hey, save that and do this. So we're getting closer to this idea that we tell the computer what to do, and it just does it. Excel is going to be a great platform for this, especially for very basic stuff.

Yeah, I can really imagine, because kind of the visual component here of working with data visually rather than through a coding interface, I think is going to be extremely valuable for Excel. And you mentioned here voice, for example, you press a button, a mic opens. Right. The interface will be very interesting in the future. Now, you mentioned Power Query as well, and how it can be a much more simpler way of doing data analysis and data cleaning than dplyr or Pandas and not in certain use cases. Maybe walk us through Power Query more in depth. And how does it differ from Excel's VBA or other scripting features within Excel as well.

So Power Query is like a data cleaning tool. The best way to think about it is that I usually take people through this in my Power Query classes. It's like, basically in 2010 or 2013, everyone was concerned with big data, and Microsoft wanted a way to deal with what I call the Business Intelligence workflow. So if I asked you what business intelligence is, if I asked you what analytics is data science, or if someone asked me what it was, we'd all come up with different answers. And usually these answers are insight, blah, blah, decision making, you know, who cares? All right? But really, to me, it's like asking, what is love? What are relationships? You know, the answer might be 42, but most of us are mortals and don't know. Basically, the BI workflow works like this. You get data, you transform that data, clean it up in some way, and then you Present it, really. Microsoft came up with three technologies to do that. Power Query, Power Pivot, then Power View, in that order. Power Query lets you take data, just transform it, move everything around, and it keeps every step for you as you do it. So dplyr, you would write, select all, you would chain all your things, and it keeps the steps. This was abstracted that and gave it to the everyday regular person. Then big data happened. And so Power View and Power Pivot actually moved off to another product called Power bi. But this product of Power Query exists across multiple spaces in Excel, and it lets you take in a data set from just about any source, notwithstanding, if you have to pay for it. But even then, you may have to pay for a connector to that data source. It brings it in. And of course, we know data never looks good when we. So you can just imagine a table with something the top and something on the values. Exactly. So Power Query lets us knock out all this stuff, take it all and turn it into a data frame. And from a data frame, we can actually analyze it, whether that's doing a report as a pivot table, or we take it to another piece of software to actually do the analysis from there.

So Power Query, in a lot of ways is a data transformation tool, right? Absolutely. That lets you clean up your data. And if you kind of look at, you know, maybe Power bi, DAX is very well known kind of to the audience here. How would you compare it to something like DAX in terms of, like, syntax complexity?

I think it's easier than dax. I mean, EMMET has some type of learning curve to it. The language behind it is called M stands for mashup. It does have a little bit of a learning curve, but in truth, you don't need to know any of it because you can just point and click and it'll write it for you. It's only when you have to get to more really harder things do you have to learn it. But I used to Power query for about 3 or 4 years before now I'm like learning M, but before I thought I learned M because it's like not that hard to understand. Once you get the hang of Power Query, it just, this is the next step. And that, that was the last step, which again, that's dplyr in R. Yeah, 100%.

And then given here, we're talking about a lot about Excel use cases for data science. And you mentioned Power Query, which I think maybe something the audience hasn't heard of before. What are other strong Excel features that people may not be aware of that you use to operationalize a lot of the use cases mentioned in the book.

Well, the big thing that this book does that the last book could not do and did not do was the use of dynamic arrays. So this is a major change to Excel, which allows us to reference cell ranges that are not just one cell, but can automatically grow to other lengths. So that's one of the things. Now, I didn't really do a whole lot of lambdas in the book. I just talk about lambda. But you can think of a lambda as like an L, apply an R, or like, I know other languages have lambdas, but basically you can just create a function out of nothing very quickly. And you can use Excel formulas to do that. And so this opens the door to things like recursion. So if you have an algorithm that requires recursion, it's not just data science, computer science, too. This is very easy for it to do. If you wanted to fake recursion before in Excel, you had to deal with circular references, which was just how many references do you go until you just give it a threshold to stop. So the dynamic arrays thing changes the game because also, it was very hard and clunky to do it. And although I won't say it's not clunky as compared to writing it in code, it's actually like all the dynamic arrays where before you had to write all this and drag that and change that. The way I've set it up is you type in one formula, boom, comes up with your covariance matrix. So I made it a lot easier. The other function is the let function. So with the let function, you can declare variables in your formulas. So that's a lot better too. The thing that has not improved is optimization. So, like, it's good to optimize in Excel. Like, you can show people how the optimization works. But if I had a choice, I would use the algorithms in another program because it's just very confusing. Excel, when you run these optimizations. And again, like, is the linear optimizer better in Excel than the choices you get in packages you could get access to if you use other languages? I don't think so. I mean, I think it's better, but in terms of these other things that I've mentioned, like just building out the infrastructure of your tables using dynamic arrays, it's so much easier than it used to be.

And intuitive kind of circles back to what we discussed when we set the stage right, and how Excel is relevant or not relevant for data science. How would you advise data scientists to start learning Excel. And you know, if I'm a data scientist working in a bank right now, when should be the next time I consider to use Excel?

One obvious use case for me is dashboards. So you know, you could use shiny in R. Is that still a thing? I guess. I haven't coded it on. Python's got a similar thing. I can't think of the name bash. Okay, yeah, so you could do that. That's one way to go. It turns it into a website. I think doing it in Excel is very useful because I think it's even quicker if you do it in Excel and it exposes the data a lot more easily. It's not like I have to create a table, it's just like I right click on a chart, what's the data, you know, and then it takes me to the guts really quickly. So I think that it's great for dashboards if that's something you want to start with. I also think that you should start learning lambdas because that's going to fulfill your desire for things. If you're more of the sort like with a lambda you can, like I said, you can run on every row, you can run a specific function, you can like do running totals. You can do a lot that you could do in these other languages and it's very flexible and I think that that would satisfy many people. It is a data science tool. And then same thing with let, you should learn the let function as well as Excel tables. Excel tables will make your life a lot easier. You'll just see it because you can't really interact if you use R with a data frame the same way you can in Excel with an Excel table.

Let's maybe pivot the bit. You know, we're talking pivot here, no pun intended. Maybe let's pivot in our conversation and talk about that prototyping use case. I love the prototyping use case because I think the visual interface of Excel shows the power to bridge the gap between a data professional and a non data professional and an executive. To illustrate the power of a use case. Right, because we always know like the common thing in data science that you may have the best algorithm in the world, but if you're not able to convey, convince an executive to get it operationalized, it's not going to drive any value. So I would love if you could comment on the role Excel plays in gaining executive buy in on data science use cases illustrating the concepts that have a lower barrier to entry for non technical profiles and how it can be used as a tool to scale data literacy potentially within the organization.

And I just want to add that Copilot is part of that data literacy idea that type it in and then it even shows you what it's doing in terms of Excel and using it with non technical professionals or even technical professionals obviously, like you can build something very quickly and you can show it to them like you can use your Excel as PowerPoint. You can show them exactly what you're doing and give them a very constrained way of moving dials and things like that. Now a lot of people will still say, well, there are other ways to do that and there are better ways to do that. But what they don't realize is that most organizations, if there's an Excel test, all right, if we can't do this and understand it in Excel, we don't want it because everyone has Excel. Microsoft, I can call them up. Give me Excel. Okay, here you go. Can I do that with we're going to run our own R server. Well, I'm sure maybe there are ways to deploy that compared to when I used to code. But the implementation of these other technologies is a lot more complicated. At the end of the day, your algorithm is great, but to deploy it you need to be frictionless. And part of being frictionless is giving it to management in a way, getting them to understand it in a way that has no friction. This is what it is, this is how we can understand it. Does it pass the Excel test? So a lot of people don't understand that because it's tacit. They think that they're going to come in and show how smart they are and this is the best way to go, this is what to do. But it's like we have an infrastructure based off of Microsoft. This is a 30 year old tool. It's the most popular tool in the world, the most used application in the world. And we got to get something else. We don't want it.

I couldn't agree more. Especially when you think about a lot of the time when you mentioned kind of passing the Excel test and being able to get executives to buy in on what the concepts are. Maybe can you give me some use cases of ways that you do that in the book? For example, ways to illustrate a technical concept to an executive using Excel that you think works really well to illustrate this concept that we're discussing here.

I think that this is more some of the content of becoming a datahead, which is more about how to communicate non technical topics to technical people. So the way to fuse that in Excel is just to use things like charts and graphs and data visualizations that convey these concepts very quickly. I think, though, what's not being said is that to make someone make a decision, what they need is simplicity. Like, they need to understand this concept very easily. They need transparency. They need to know that if someone needed to take it apart, maybe it's not them. They can know what they're doing and they need just the facts. Right. Like this is all the important stuff and that's it. I don't want to feel overwhelmed. So I think that in its limitations, it forces you to think of like, what is the most important stuff. And then there's just one last thing that's non Excel related that people need, which is you need to appeal to people's emotions. I mean, that's just a fact. We don't if data scientists want to hear that because we love facts. But if you want someone to make a decision, commercials don't give facts. They appeal to your remote counterfact, you know. Yeah.

And extending that even a bit more because data storytelling is such an integral part of driving impact with data science. What are some of the best practices that you advise? You mentioned appealing to your emotions. How do you best approach that as a data scientist? What's your advice here?

So my advice is, first of all, lay out all the facts that you want to say. Get it all out. Because this is how I write my books. I get it all out. When we turned in Datahead, it had 400 pages and then I chopped it down to 215. So my co author, Alex, that was the first time he'd really seen me do that. But that's how I operate. So. So get it all out and then start looking at what are the most important things. What can I combine? I mean, think of yourself as running principal components on it. Like, what are the key components of all these facts that basically explain most of them? That's what I would do to start out and then to construct a story. It's really not that hard. We overcomplicate this. So beginning, middle and end. All right, here's how we started. Here's what we tried. Here's here is the result. And if you want to get more advanced, you run it as up and down. Okay, so we tried this project. It didn't really work, but then we figured this out. Okay, middle point. Right. And then we kept running this way until we ultimately realized a problem. Third act break. All right, so what are we going to do now? Well, now we're going to go. We realize this is our end goal and this is what's going to take us home. We run, you know, you can plus plus negative and then end on a plus. All right. Or you end on a negative. That's the essence of that. I haven't put that into a full course yet because I'm still putting ideas together. But plenty of people have written about it. There's a great book Nancy Duarte I think wrote, can't think of it right now. I don't want to Google.

We'll definitely add it in the show notes. And you know, you mentioned here your book becoming a day ahead. Precisely. A great book. Highly recommend that everyone reads it. That book really touches upon kind of the, the underlying concepts needed to think and develop a data mindset and think like a data professional. Maybe expand on what a successful data mindset looks like here.

Jordan Well, I mean, lots of people will probably disagree with this. I think a successful data mindset is the ability to think critically about the products in data. And just like the data space, everyone wants things to be clean. Data plays on this idea of objectivity. Oh, these are just the facts. But there are no real facts anymore. There's what you present. Even like a sensor that gets directly from a physical measurement still runs through people's idea of what it should say, of its precision. There's an interface between it. So we don't have true facts anymore. So that means that you need to be really skeptical about the things that are presented your way and the decisions you have to make. We take for granted. For instance, someone gives us a study using a T test, right? 95% is the usual confidence level. So are you comfortable with 1 in 20 results being potentially random? That it could look like it's correct, but it's not. I'm not saying this in the most data science statistical way, but I think people, I'm saying, are you comfortable with that 1 in 20, like, we don't think about that. It's like, oh yeah, it passes. It's good. So I mean, for me it's just a matter of being skeptical. It's about understanding that you're not going to save the world with your data project and really realizing that even though we hold data scientists up here, we work in teams. So you need to be able to understand how your influence and what you want to try to get done spreads across that team. I know in the back of the book we talk about different scenarios of data project failures as a result of lack of communication and anyone who reads that is going to be like, oh, my God, that's me too. And I just want to add one last thing, because we got a recent review on the website or on Amazon, I should say. That said, this book takes a really negative view, like, nothing is good about AI and they gave us one out of five stars. So if you want a book that praises AI and talks about how data is great, it's not that book. If you want realistic assessment that's going to help you become a manager because you didn't make the wrong mistake because you put too much faith in your own abilities or the abilities of others. It's a great book for you.

Yeah. And maybe you walk us through kind of what the book tries to say about AI that you think the community would disagree with here or some members of the community I disagree with.

Well, I mean, we wrote the book before generative AI became such a big thing, so there's some mentions of generative AI in the potential future, but clearly it's taking over our businesses in a way that this book does not describe. Although I wouldn't say that. I mean, we were never trying to be that. Honestly, I think by and large, people will not disagree and they'll find humor and I in the irony of, like, what data promises, what their life really is in working as. But I do think people might disagree that, you know, we. We talk about neural networks as just being one giant formula, and this makes them not as special as we think. You know, they were born at a time when we kept saying the brain was like a computer. And in some ways it is. But the way that a neural network works is a metaphor for how our brain works and maybe approximates the randomness of it or just like the little mints that our neurons make. But it's not our brain. And a neural net will not. Will not become a brain yet. And I think if you were hired as an AI person or if you felt your career was going away because you planted your flag on the Internet of things, now you're going to plant your flag on generative AI and you get to go to all these conferences and they talk about how great it is, and you write a report to your CEO, but at the end of the day, they're just trying to keep you out of projects because you're too optimistic about. But you're some emeritus status. Like, that's. You probably don't want to hear this. I have its hierarchy.

I mean, definitely everyone do read that book. It's a great one. And you mentioned as well projects failing because of lack of communication. I think this is such an important thing about developing a data mindset here is the ability to communicate with others by using a common data language. Let's say, maybe walk us through these common failure patterns that you've seen and how best to avoid them.

One of my favorite patterns that we talk about in the book is the telephone game. So the idea is that an analyst looks at a bunch of survey results and they say, oh, 75% of people said they would rebuy our product. So they're at a manager meeting and they say, well, early analysis shows we have a 75% of survey response to buy our product again. Over time this idea morphs into something that it's not. So she, the analyst, she goes back and looks at the data and sees, oh, well, only actually 8% of the respondents actually filled out that information and they said that they would want to buy the product again. So that really doesn't look very good. But that little factoid takes on a life of its own. So suddenly at a manager's meeting, she hears someone say, guess what? We have a 75% rebuy rate based off the analysis of our data scientists. And that's not what the data was. It was eight respondents said that they would buy the product again, but they haven't actually done it yet. So as an example of just how these things don't communicate well. And then the other thing is we use terms like average to mean a lot of different things. So if someone said, well, the usual way it happens is are they talking about the mode? I mean, that feels usual to me. Are they talking about the average? Well, you can make a case for that. Are you talking about the median? Maybe less of a case. But what does it really mean? Talking about the upper quartile? Like, we don't actually have a definition for those things. So when it comes to talking to data, we talk about in the book, say average or mean. All right, that's okay, say average or mean. But the average refers to the arithmetic average words like typical, usual. Sometimes. Often things have very wiggly meanings. And so we want to stay away from those words. And one last thing I want to point out is that people often misunderstand. Like even smart people will say something like, well, if the average person is this, then 50% of the population is below average. Think about that. Right? But that's not average, that's median.

That's median.

Yeah, and I've talked to people about this and someone said to me. Well, in my school we learned, we call these the measurements of average. That mean median mode were all measures of average is what his instructor called it. Okay, I don't know. I mean, I've never heard of that, but let's say it's possible. Well, all the more reason that you should be specific in your words and create a common language within your organization. I mean, we also see this in machine learning. Precision and recall have a bunch of different names, statistics, type 1 and type 2 error. You will never hear me say those words. Okay.

Bias is used like so many different use cases.

Absolutely. I want to stick with the type 1 and type 2 real quick because I think this was named by a lazy programmer, like false negative. That's it. Those are the words I would use. I use type one or type two.

Yeah, it's a bit jargony. And then maybe, you know, as we're, as we're discussing these pitfalls, Jordan, like, how do you grow that data mindset? Like, what are ways that you can build it in yourself and your team and your wider organization?

Well, the official response that I will give you is that you read my book and you work on times like you, maybe you have once a week you discuss a chart or you discuss something. Nobody's wrong. You just sort of try to tear it apart and think together as a team. Like, what are the critical things we're missing? And we'll become very clear what everyone's good at. Some people, like, it's hard to get them to agree and they're just like going to argue about everything. But maybe they're the person to have in there and you just don't take it personally. Right, because they're going to, even if they do get hooked on things, which they often do. But I think that, like, there are people who have led small teams who are able to bring this critical thinking mindset. That's the official answer. The less official answer is that, you know, I've read Carl Jung. I don't think people change like the way we think that they do and that there is no true answer and that we have to accept people's personalities as they are. And it may just be a matter because the next generation works in data and they just may be better at it than we are.

Let's just say we'll find out in the future. But I like the official answer and the unofficial answer. And then, you know, at what point do you reach a limit in working in Excel and need to jump to other tools?

The easy answer for that is, you'll know it. You'll know it when you see it. All right? When you hit it. I can't say specifically. I think that for me, it's just a matter of, like, I sit down and I'm like, this is too hard. I'm going to move on to another language. And sometimes I'll move back to Excel. I think that, like, when your data is at risk, when your work is at risk of crashing, it'll run button and it goes white. And maybe, you know, if you wait 10 minutes, it will likely go, but you don't want to risk that because this is critical stuff, then it's time to move. But if you have to set your calculation mode to manual, I don't like doing that, turning it on and off. If it can't stay automatic in Excel, then it's too big for Excel. But a lot of this stuff just can be optimized. So if you take my online class, you can read in my books that we do things very optimized so that we're not. Well, I mean, I won't get into it too much, but for those who have a computer science background, you know, there's big O notation, sets of instructions, and apply that in Excel. All right? You can think about optimizing your work so that you're not doing 100 VLOOKUPs.

You'Re doing one that's really great. And maybe as we close out, Jordan, what is one final advice that you would give to practitioners or leaders listening on the podcast today that can take away for their next week of work?

I think the big takeaway for me is that these changes that we want, you install this software and your life gets better. Right? You do this with your team and life gets better. But in truth, it's more like the way your team operates right now. Whether it's data or anything else, it's like a ship and you can move the rudder a little bit. In a few years, it will have changed the direction dramatically, but you can't do that tomorrow unless you start tomorrow. So my view is, if you found something useful here, pick one thing that you want to try to fix and make that the focus of your group or your team. And when you've netted that win, pick one more thing and just keep growing it, but don't try to do it all at once. It won't work.

Thank you so much, Jordan, for coming on the podcast.

For sure. And I'm just going to show this. Just.

Yeah, definitely do show the books again.

If you guys want to find these, these are on Amazon.com is the best way to get them. Also in ebook format. And of course, if you want to find me and ask me questions, best way to get to me is LinkedIn.

Thank you all so much. Thank you, Jordan, for coming on the podcast.

Absolutely. Thanks for having me.