Practical Charts for Finance Teams to Turn Data Visuals into Strategic Tools with Nick Desbarats
In this episode, host Paul Barnhurst is joined by Nick Desbarats, a data visualisation expert, bestselling author, and independent educator, to discuss data visualisation. Nick talks about the skills needed to create effective charts, how to avoid common mistakes, and the importance of understanding the "job" of a chart. He shares his journey from software executive to becoming an authority in the field and explains why the true skill of visualising data goes beyond knowing the software.
Nick is the author of Practical Charts (Amazon #1 New Release) and the upcoming Practical Dashboards. He has taught thousands of professionals globally, including teams at NASA, Bloomberg, Visa, The United Nations, Shopify, and more. He is also the first educator authorised by Stephen Few to teach his foundational data visualisation workshops.
Expect to Learn:
Why understanding the "job" of a chart is more important than the data itself
The key skills needed for creating effective charts
Common mistakes in data visualisation and how to avoid them
Why empathy for your audience is crucial when designing visuals
How to decide when to use default charts vs. creating custom visuals
Here are a few relevant quotes from the episode:
“The real job of a chart is to serve people, not the software.” – Nick Desbarats
“Data visualisation is not about the tool, it’s about the message you want to convey.” – Nick Desbarats
Nick Desbarats shared valuable insights on creating effective data visualisations, emphasizing the importance of understanding the purpose behind each chart. He highlighted key skills, common mistakes, and the need for empathy with your audience.
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LinkedIn: https://www.linkedin.com/in/nickdesbarats/
Company: https://www.practicalreporting.com/
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In Today’s Episode
[03:33] – Nick’s Background
[05:32] – Key Skills for Data Visualisation Experts
[09:50] – Executive to Data Viz Educator
[12:35] – Book Recommendations
[19:35] – Moving Beyond “It Depends”
[26:08] – Common Finance Chart Mistakes
[34:13] – The Pie Chart Dilemma
[37:54] – AI in Data Visualisation
[45:56] – Chart Type Selection Skills
[58:30] – Final Wrap up
Full Show Transcript
Host: Paul Barnhurst (00:29):
Welcome to another episode of FP&A Unlocked. Are you tired of being seen as just a spreadsheet person while others get a seat at the table? Well, then welcome to FP&A Unlock where Finance Meets Strategy. I'm your host, Paul Barnhurst. Many of you know me as the FP&A guy. Each week we bring you conversations and practical advice from thought leaders, industry experts, and practitioners who are reshaping the role of FP&A in today's business world. Together we'll uncover the strategies and experiences that separate good FP&A professionals from great ones helping you elevate your career and drive strategic impact. Speaking of strategic impact, our title sponsor for FP&A Unlocked is Campfire, the ERP. That's helping modern finance teams close, fast and scale faster. Today's guest is Nick Desbarats. I'm super excited to have him on the show. So Nick, welcome to the show.
Guest: Nick Desbarats (01:28):
Delight to be here. Looking forward to it. Yeah,
Host: Paul Barnhurst (01:30):
So lemme give you a little bit of background about Nick, but first I'll tell how I first learned about Nick. So I shared a post working on my data vis course, and one of the people I've had on the show that many of you have listened to, and I always recommend his book, is Brent Dykes Effective Data Storytelling Commented. He goes, someone else you should be following, someone else you should be looking at. That does some really great work. Here is Nick. And then you went ahead and commented and you shared a post on YouTube. I believe it was about when to use tables versus graphs is where that started. And so we got in touch and you agreed to come on the show. So I feel honoured to have you. Well,
Guest: Nick Desbarats (02:07):
The Feeling's Mutual. It's a great show. I've been listening to some episodes.
Host: Paul Barnhurst (02:11):
Well, thank you, I appreciate that. So lemme give a little bit about Nick's background and then we're going to jump into data vis today. We're going to go deep on that topic. So as an independent educator and bestselling author, Nick has taught data visualisation and information dashboard design to thousands of professionals and over a dozen countries at organisations such as nasa, Bloomberg, visa, the United Nations, Shopify, the Internal Revenue Service, and the Central Bank of Tanzania. Nick is the author of the Amazon number one New Release Practical Charts. I have it here thanks to Nick. He sent me a copy and the upcoming Practical Dashboard books, and he regularly contributes articles to the Journal of the Data Visualisation Society, Nightingale, that are among that publication's most widely read and shared. He also regularly delivers main stage talks at conferences such as the Tableau Conference, TDW, world Conference, SaaS Explorers Innovation Summit, and others, and has lectured at Yale, Columbia, the University of Toronto, and the Victoria University of Wellington in New Zealand. So love the background. One last thing to share on his background. He was also, and I really found this fascinating, the first and only educator to be authorised by Stephen Fe to deliver his foundational data visualisation and dashboard design workshops. And I just have to say, probably the first book I bought on data visualisation was show me the numbers. So I'm a huge fan of Stephen fue.
Guest: Nick Desbarats (03:53):
Got some copies right up there. There we go.
Host: Paul Barnhurst (03:56):
Yeah, yeah, I have it right over here. I could grab it so I get it. So you taught that from 2014 until launching your own workshops in 2019. Prior to that, he held senior executive positions at several software companies, and it was a co-founder, a Bit Flash, which raised over 20 million of venture financing and was sold to OpenText Corporation. In 2012. Nick was granted a United States patent and the decision support filled. Love the background. It's great background you have there, Nick. Yeah,
Guest: Nick Desbarats (04:29):
Yeah. When people ask me how I got to where I am, my one word answer is circuitously, indirectly, kind of all over the place. But yeah, this is now the best job I've ever had, so can't complain.
Host: Paul Barnhurst (04:40):
Love it. There's nothing better than enjoying what you do. My training partner I've had on the show several times, Ron Montero, he wrote a book called Love Mondays. The whole idea to help people find more fulfilment in their job because so many people, we've all seen it. It's like, is it Friday yet? I hate my job. When can I leave? Is it I get to leave at five? Oh, it's four 50. Maybe if I just punch out earlier, whatever the kind of things are. So I'm glad you love what you're doing.
Guest: Nick Desbarats (05:06):
Absolutely. Yep. Wouldn't have it any other way.
Host: Paul Barnhurst (05:08):
Good deal. So I modified our first question. Usually I ask what makes for great FP&A, but I wanted to ask you a different question since we're focusing on data viz. What makes for a great data visualisation person? What do they need or what do you see in those that are great at it?
Guest: Nick Desbarats (05:23):
Unfortunately, the most common answer, it typically revolves around software expertise. People think, oh, if you want to be really good at making charts, you should be really good at using Excel or Tableau or some other data visualisation product. But I think that's only actually a relatively small part of the skills that are needed. And actually, maybe if I can share my screen, I have kind of a list that I often use in talks and presentations about,
Host: Paul Barnhurst (05:50):
So if you're listening to this on audio, we encourage you to go to YouTube.
Guest: Nick Desbarats (05:54):
Yes. Yeah, I mean, I'll run off the list that I'm showing on the screen right now. But basically, like I said, most people tend to assume that good software expertise makes you great at creating charts. But that's kind of like saying if you want to be a great writer, become an expert user of Microsoft Word. It's like, no. There's like, yeah, you need to know how to use word processor, but there's all sorts of other skills on top of that. And so the list that I'm showing on the screen right now is showing things like data handling expertise, basic statistical knowledge, situational knowledge, database fundamentals, and those are skills that I consider are necessary to create any kind of chart. So regardless of what kind of chart you're creating, I think you need to know those. And then there are other skills like storytelling, graphic design, coding, advanced data visualisation, software expertise, advanced statistical knowledge.
(06:41):
And these are skills that are required to create certain types of charts. If you're creating, for example, data art or an infographic or a very technical or scientific chart, these are additional skills that you would need. And so really it's just focusing on software expertise really, I think is, like I said, it's only a tiny fraction in fact of what you need to know. And unfortunately, in a lot of organisations, they don't understand this. And chart creation is often shuttle off to more junior people who perhaps haven't mastered a lot of these skills, especially things like situational knowledge. If you're creating charts about insurance, then you better know something about insurance, how that domain actually works. And I see too many people basically saying, oh, I'm a data visualisation expert. I can create charts about anything. It's like, no, you can't have, it's writing about a topic. If you don't know a lot about it, then your charts are not going to be very good.
Host: Paul Barnhurst (07:42):
I agree. And I like how you said the software is a small part of it. That's what I say with financial modelling. So many people, if I'm just an expert in Excel, I can be great at financial modelling. No, you need to understand accounting. You need to understand business, how to validate assumptions. It's very similar to what you're sharing here. And I think so often people want it to just be the software that's easy to measure, and that's a technical skill that you can just go out and learn. But it's not where I think great charts come from that come from understanding so much more science. There's a little bit of art in that judgement , et cetera.
Guest: Nick Desbarats (08:22):
Yeah, I mean that I put under database fundamentals. And so that's things like knowing how to choose chart types and how should I include zero in the scale? Should I use a sequential or divergent colour palette? And that's really important. Instead, it is in the list here. But again, it's just one piece. There's amongst many that are needed, I think. I agree. It's an important piece. That's what I teach, but I would never say it's the only piece for sure.
Host: Paul Barnhurst (08:52):
Yeah, I think it's a great point. I know we'll come back to some other lists throughout that you're going to share and a few other things, but I'd love to have you tell your story of how you got started. You were the only educator authorised for some of the workshops that Steve F offered. How did you go from an executive out of a software company to teaching Stephen Fs data visualisation? That's quite the route.
Guest: Nick Desbarats (09:17):
Yeah. Yeah. I know, like I said, indirect, but I guess it probably really started. Originally I was a software developer, and so I've always had an interest in data and coding whatnot. But then probably 20 years ago or something like that, I just started to develop kind of the sideline interest in cognitive psychology, the psychology perception, how we process information, how we make decisions. And I just started inhaling a lot of books on that topic. I read over a hundred books, I think, but I always considered it to be kind of my sideline interest. It wasn't my age job. But then in 2013, I attended a workshop by Steve, by Steven Few, and if anybody listening has ever been to one of his workshops, it's a magical experience. I mean, he's just an incredible educator. He's retired now. And it just blew my mind because it was kind of the intersection of these two areas of interest, data and technology and psychology, because in fact, most of what he talked about in his courses really had more to do with psychology than to do with data or technology.
(10:20):
And so I was fascinated. We've stayed in touch for a little while. I got laid off company shrank by a lot, and then that was my first phone call was asking Steve, has anybody ever really approached you about teaching his workshops? And people had, but they tended to have very data heavy backgrounds, not really on the psychology side, but because we had been in touch for the time after the workshop, and he knew that I had kind of a strong kind of base in that at that point, he agreed. And so spent about seven months pretty intense, getting up to speed, sent me a pile of books to read, and then, yeah, so I basically sort of taught his courses for about six years. As you mentioned, he retired in 2019 and encouraged me to launch my own courses, my own books, which I did straight into the Pandemic, of course, but it turned out okay because it turns out that people are actually, now they're okay learning online. And so right now still probably about 60 or 70% of the workshops I deliver are still online, even though I offer in person. But of course, everybody hired so many remote people now that trying to get everybody in the same city is sort of a big deal. So kind of like I said, an indirect route to where I am today.
Host: Paul Barnhurst (11:37):
Thank you for sharing that. And so I got to ask, since I know you've done a lot of the cognitive psychology, I think there are some great books out there in that area. Do you have a favourite or one or two you might recommend to people on that subject?
Guest: Nick Desbarats (11:49):
I mean, I guess it kind depends a bit on what you're interested in, but also be how new are you to it? I mean, the Bible is thinking Fast and Slow by Daniel Kaman, right?
Host: Paul Barnhurst (11:59):
I figured that's the one you were going to say.
Guest: Nick Desbarats (12:01):
But to be honest, it reads a bit kind of textbooky. And so there are other books. It's one of the first ones that I read. Maybe we can put it in the show notes, but A Made to Stick also incredibly useful book, just basically about how to communicate information in a way that stays with people. Those would probably be at the top of the list, I would think.
Host: Paul Barnhurst (12:24):
Okay. Haven't read Made To Stick. I've read stories that Stick. That's a good book. Different, little bit different, but really good about just how you, the psychology behind making stories stick.
Guest: Nick Desbarats (12:36):
Yeah. Yeah. Yeah.
Host: Paul Barnhurst (12:37):
Cool. Well, thank you and appreciate that. And I do tend to agree with you thinking Fast and Slow is a little bit like a textbook. It's good, but it does read a little heavy at times. Yeah.
Guest: Nick Desbarats (12:47):
I mean, in terms of pop kind of more popular books Drive by Dan Pink or any of Dan Pink's books are also great kind of first books to start to get familiar with some of the research. Ian Cognitive Science as well. Makes sense. He's a good one. I have his selling is Human. Yes. Yeah, also a fantastic book. Yeah. Yeah. Or Jonathan, actually, that would be another one that I would, social psychologist, Jonathan, H-A-I-D-T. Any Eva's books. Excellent.
Host: Paul Barnhurst (13:16):
Yeah. Perfect. Well, thank you. Those are some great resources for people because like you said, there's the data vis side and there's the cognitive psychology side of images and the storytelling and how you make impactful
Guest: Nick Desbarats (13:29):
Charts. One of my goals when I was writing my books and designing my courses was to actually only include the minimum amount of theory that I thought people needed to know. And when I went through it all, I realised that in fact, that amount was zero. There is no theory in my books, and because he's kind of the name, right, practical charts, it's all practical basically. No theory. I did realise that. I mean, yes, it's very interesting to know about things like pret attentitive attributes of visual perception or visual hierarchies, but you don't actually need to know any of those concepts in order to create what I call sort of good everyday charts good enough. It's not data art, it's not super technical charts, at least the ones that I focus on. And actually, I don't think you need to know any theory for that. I know it's kind of a controversial point, and I've written a post or two about it. Maybe we can link it in the show notes. But yeah, it's very interesting and I encourage people to look into it, but technically, if all you want to do is just create charts that are going to go over well with your audience and that nobody's going to laugh at, or your boss is not going to shoot you down, you don't actually need to know any of that.
Host: Paul Barnhurst (14:42):
Interesting. I'm going to have to give that some thought. I always feel like it's helpful to know that, but I could see where you're coming from, so I'll have to read one of those articles. Interesting. So kind of speaking of the book, love the title practical charge. You already mentioned how the goal is to be practical, but as I was reading through it, there's one thing that jumped out to me that I had to ask about if the acknowledgement section of your book. You mentioned social media and how many people on social media help you with writing the book? Curious. Talk a little bit about that. Definitely in general, going to social media is not the place I would go to for advice on charts. Just so much noise out there.
Guest: Nick Desbarats (15:20):
Yeah, well, it was more, I guess not so much occasionally I guess things that people are posting, but more the discussions that happen. And so it was more around the things that I would post and then I would see how people would react to it or questions. Sometimes I'd be going back and forth between, oh, should I recommend this best practise or that one? And so I'd just throw it out there and see what people said. And nine times out of 10, there's stuff that came back where I was like, oh, that's true. I hadn't thought of that, or That's an interesting way to maybe reframe the question. There was also lots of garbage, of course, lots of comments or rep applies. I was like, no, that's really not helpful or whatever. But invariably, I got a lot of useful material out of it, but to the point where I almost felt kind of guilty because I was like, oh, that's a great idea.
(16:08):
Thanks. And it goes in the book or the course, and it happens during workshops all the time too. I tell people at the beginning of my courses, I update these all the time, and it's often because I teach smart people, I would never say I'm the smartest person in the room. And sometimes people think of things they notice, things that I hadn't thought of or that I had missed. And this happens, I would say almost every workshop, there'll be at least one thing, even a minor thing where somebody points it out and I'm like, good point. And then of course gets a little better. So now it's been iterated and iterated many times over the course of the last five or six years. And so I'm grateful to all the people who have basically answered me on social media or put out comments during my workshops.
Host: Paul Barnhurst (16:51):
That makes sense. I can get that. And it is definitely true. Often the best learning comes from others when you're teaching. So often you're learning from one other share like, oh, never thought about that, or I didn't know you could do that, or whatever it might be.
Guest: Nick Desbarats (17:06):
And just also learning where people's knowledge generally is, what do they know already versus what are they wondering about? And so yeah, I consider every workshop I teach is basically market research where I get a better and better idea of what people find to be most valuable or what they, they're like, yeah, no, not so valuable for me. That's why I didn't end up writing the book until I'd been teaching the course for about three years, because then I had a much better sense of what I could cut because people didn't find it useful or they knew it already. And so really the goal was that it was like all meat, no fat, just stuff that most people probably didn't know already about charts.
Host: Paul Barnhurst (17:49):
Talk about that. What motivated you to write the book? What was kind of the impetus that pushed you to do it?
Guest: Nick Desbarats (17:53):
Well, really, most of the heavy lifting happened with the course, right? Because the book is, I mean, in essence, just kind of almost like a transcript of me delivering my course. I mean, I did think it would be relatively easy to write because that's what it was. Essentially. It was just a transcript of me giving the course. It turned out to be much harder. And anyone who's written a book will tell you that that process forces you to rethink everything. And it totally did. I ended up going back and just ripping the course apart and putting it back together again afterwards. But probably maybe the more pertinent question is what prompted me to develop the course? I was teaching Steve's courses before, which are excellent, but I mean, I did notice some things that when I was teaching those courses where it's like people had more of an interest in certain topics, and really I felt that there were more opportunities that to be more specific.
(18:46):
So one of my goals in writing, well, designing the course and writing the book was to avoid the phrase, it depends as at all costs. There are a few times where I had to fall back on it and say, I'm sorry, this is just too nuanced, it's too complex. I'm going to have to fall back on. It depends, so you're going to need some experience and judgement , but 95% of what's in my course and my books, anyone can follow those guidelines even if they have very little experience because should I use a line chart or a bar chart? Well, often the answers that I was seeing out there pretty much boiled down to, well, it depends. Sometimes it makes sense to use a bar chart, sometimes a line chart. And so I really went down all the rabbit holes and all of the edge cases and everything to come up with, in many cases, trees, luchar specific.
(19:39):
It's like it's not just, it depends. It's what exactly does it depend on? Is the story more about overall patterns of change over time or are you focusing more on individual periods? For example, do you have all the data or are you missing a lot of data? Does the data occur at regular intervals of time or is it the kind of data like sales transactions where you might have five in one day and then nothing for two weeks? And so I basically isolated all of these factors and distilled them into very specific guidelines that, like I said, anyone can follow even if they don't have a lot of chart making experience. And so that was really probably the main kind of motivation for me to create the course in the book, was that I felt that I could be more helpful, especially to people maybe who don't have a lot of chart making experience by avoiding that dreaded, it depends phrase, and I know it's one of my friends and colleagues, Andy Reve, that's one of his favourite phrases.
(20:38):
It depends, but I kind of pushed back on that a bit and said, yes, that's true, but it's also not helpful. And so let's try and take the next step and find what does it depend on. Right, exactly. But I mean, I'm also kind of careful to say that these are, I call them guidelines, not rules, because once you do achieve a certain level of experience of expertise, you might deviate from what I recommend. And I say that at the end of the book, that's cool. You totally can. But the first kind of rule of knowing when to break the rules is you got to kind of know what the rules are in the first place and then you can break them. And so that's really where I see my role is in helping you get up to speed on about rules, although I hate that word, the guidelines, because a lot of my audience, they're not passionate about data visualisation. This charts is just something they have to do as part of their job, and they're like, I just want to create competent charts and I want to learn that as fast as I can. Great, I can do that. I just want to be able
Host: Paul Barnhurst (21:38):
To do it for my job and move on. This is not a career for me.
Guest: Nick Desbarats (21:42):
Yeah, I mean some are right. I do have people who are very passionate about it and who attend the courses as well, but I'd say probably two thirds or something are like, great check now I can create competent charts.
Host: Paul Barnhurst (21:56):
Next. The average person out there wants to be able to do competent charts that has to do 'em. There are those that are completely passionate and love it and want to go deep. I hear you. And so it kind of leads me to the next question. Going back a little bit to the book, but in the book you mentioned that data visualisation has its own spelling and vocabulary. Can you explain what you mean by that? Elaborate on that a little bit.
Guest: Nick Desbarats (22:18):
In a lot of ways, I think learning how to create effective charts is almost like learning a new language. And in fact, there are a lot of similarities I think between language and charts more than a lot of people realise. A lot of people tend to think of charts as very kind of technical, but I mean, charts are only for people. Charts serve no purpose for computers. Computers do not need charts and graphs. And so in a lot of ways it's like charts are almost essays in a lot of ways. And there are specific guidelines as I was just mentioning, that you can learn just like when you're learning a language, you can imagine if somebody was learning English, let's say, and they were trying to learn when to use the three versions of there, right? T-H-E-I-R-T-H wire. And imagine if you had an instructor who said, well, it's just something that you'll sort of get a feel for over time and with experience, you'll eventually kind of figure out which one to use. Whereas if you have a good instructor, let's say, no, no, no. Okay, here's the situation in which you would use T-H-E-I-R. Right? It's when it's to indicate possession or whatever. And here's the situation where you use THEY re E when you get substitute with like they are, and so you can learn it. It's not this of touchy feely as a lot of people present it to be.
Host: Paul Barnhurst (23:45):
That makes a lot of sense to me. So it's kind of that idea of, look, there are, I use that term loosely, rules that you can learn. I know you mentioned guidelines, but there are things that can really help guide you in building more efficient, effective charts
Guest: Nick Desbarats (24:00):
And without relying on having just years of experience and judgement and intuition. It's like, no, no, no. I mean, that's required in certain cases, but there's opportunity to be much more concrete about it, I think.
Host: Paul Barnhurst (24:12):
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So let's get into a little bit more of a visualisation for the finance FP&A audience. So the first question I'd like to ask is, I know you do a lot of training for finance and FP&A people, obviously we build a lot of visuals. What are the biggest or most common mistakes you see from FP&A finance people in general? Well,
Guest: Nick Desbarats (25:33):
I mean, I think probably even more broadly, so yes, FP&A people, but just generally, people tend to think of charts as visual representations of data, but I actually don't really think of them that way. I call 'em graphics for doing a job. And this is something, especially for people with finance backgrounds, this is a big kind of leap for them when they sit down to create a new chart. Typically they'll ask themselves, what is the best way to visualise this data? But I don't think that's a good starting point because the reality is that for any kind of dataset, there's a literally infinite number of ways you could visualise it. I think a better starting point, this is what I teach in my course, in my workshop, is do I know why I'm creating this chart? Am I trying to persuade the audience to do something?
(26:21):
Am I trying to explain something to them, or am I just trying to make them aware of a problem or an opportunity? And if so, exactly what is that problem or opportunity or am I trying to answer a question? If so, what exactly is that question? To me, that's the starting point. It kind of has to be, because if you don't know why you're creating a chart, then a lot of your design choices are actually going to be random things like choosing chart types and colour palettes. Those depend actually mostly, not entirely, but mostly on the job of the chart. Yes, the nature of the data is a factor. How many values do I have? Are these time series values or the breakdown of a total? Yes, those are considerations, but they're almost secondary compared to the job of the chart. And so really the process of creating a good chart is because kind of combining that information, the job of the chart, the nature of the data, and then coming up with a chart that has as high a chance as possible of doing its job.
(27:18):
And so if we were, for example, creating a chart to persuade people to donate to our charity, what ultimately matters is how many people saw that chart and made a donation? Things like, well, how quickly can they interpret it or how precisely can they perceive the values? It's like, yeah, those matter. But I call those the secondary attributes of a chart. The primary attribute is did it do its job regardless of how much cognitive effort it took or whatever. And these are often things that are measured in labs when people are studying data visualisation, they measure how much time it takes people to process or how much information they can recall or how precisely they can perceive values. But to me, it's kind of missing the point. It's like, yeah, those matter, but they only matter because those things tend to improve the odds that our chart will actually accomplish the purpose, the reason why we decide to create a chart in the first place. And so for everybody, this is a big mental leap to move away from what is the best way to visualise this data towards how can we create a chart that is going to do its job? And oftentimes people, particularly people with finance backgrounds, but like I said, everybody in general, it takes them halfway through my 14 hour, two full day in person for half day online course until they really start internalising that idea and they stop asking, what is the best way to visualise this data?
Host: Paul Barnhurst (28:50):
You make a really good point. You have to think about what are you trying to accomplish with your audience, and does the chart help you accomplish that? Is there a decision, an outcome that you're hoping to get from that visual? You show not, oh, is time series the best for this data? That depends on what you're trying to accomplish with that data.
Guest: Nick Desbarats (29:13):
It will be more what kind of time series chart, if that's the kind of data you're showing, right? There's actually six or seven major chart types for showing data over time. People automatically tend to think, oh, it should be a line chart data over time. It's like, well, sometimes, but sometimes it should be bars or dots or a step chart, even in certain cases, it's a heat map if you have a lot of time series in a chart, but you can't really make any of those design choices unless you know why you're creating a chart in the first place. Now out there in the real world, of course, there are unfortunately situations where we don't know. We've just been told, Hey, make a chart of this data. And we're like, okay, why? And the audience is like, well, I'm busy. Just make a chart. Now. Those are tough, and I talk about, those are my workshop. I have this technique called spray and pray, where you basically create a couple of different charts and you hope that one of them actually is what the audience had in mind when they were asking for it. But yeah, always coming back to the job, the job of the chart.
(30:14):
I like the spray and
Host: Paul Barnhurst (30:15):
Pray. We've
Guest: Nick Desbarats (30:16):
All done that before for, yeah. Well, a lot of people don't actually try and create, they'll put all their eggs in one basket and they don't know why the audience wants to see the data. So they'll create a chart based on an assumption about why they want to see it, but the odds that assumption is going to be correct are very, very low because like I said, for any dataset, there's a literally infinite number of ways that you could visualise it. And what are the chances that you happen to pick the one that is going to resonate most with the audience if you don't even know why they wanted to see you the day in the first place? It's pretty low. And this kind of explains why there's a lot of dissatisfaction often amongst audience members. Charts get thrown back at people all the time, that's not what I wanted. What the hell is this? How come you didn't understand what I wanted? And it's like, well, you didn't tell me what you wanted,
Host: Paul Barnhurst (31:07):
A real simple answer for that. You gave me no details.
Guest: Nick Desbarats (31:10):
Yeah, but oftentimes people don't even ask. So they get this request saying, Hey, I need to chart this data. They're like, okay, boss. It's like, no, you need to come back and say, okay, no problem, but I need to have a better understanding of why you're asking me for it. Like you said before, is there a decision you're trying to make? If so, what is that decision? Tell me. Or are you going to show this to a client trying to persuade them to, you're trying to upsell them or whatever. You got to know that in order to create an effective chart.
Host: Paul Barnhurst (31:42):
You make some great points there. So I'd love to ask, what advice would you offer for people to get to improve data visualisation? Say they're early in their career, kind of finance people or just in general, where do you recommend they start?
Guest: Nick Desbarats (31:59):
Well, right here. Yeah. I know you have
Host: Paul Barnhurst (32:03):
Practical charts. Here we go. Read the
Guest: Nick Desbarats (32:04):
Book. Yeah, yeah. No, well, that'll get you basically one of the nine or 10 sort of skills or areas that I was showing earlier. But you do need to know that the basic kind of spelling and vocabulary of database ization, how to choose chart types and colour palettes for sure. But yeah, it takes time because that's not the only thing you need to know. Database fundamentals, which is what I teach you. Also, of course, you have to know how to use a Visa software. That's typically actually the easy part, especially now with ai, if you're not sure how to do something in Excel or Google Sheets or whatever, you can just ask and it'll tell you how to do it. So it's typically actually the easy part now. But you also need the data handling expertise, some basic statistical knowledge, understanding things like survivorship bias and when to use means versus mediums, because if you don't understand those things, you're going to plant those.
(32:55):
I call those statistical landmines in your charts. You have to have that situational knowledge. That's usually where the real heavy lifting is, especially if you're kind of new in an organisation. Well, you need to get up to speed on what are people worried about in the organisation? What are the kinds of concerns? What are their objectives? What kinds of charts have they seen in the past that they might be used to seeing? I'm not saying that you need to replicate that, but you should be aware of that. If there are charts about healthcare data, then you need to start learning about healthcare. So yeah, like I said, I showed that list before. Just start trying to check off as many of those as you can essentially, and it might take months or years. I kind of have
Host: Paul Barnhurst (33:39):
To laugh when you said, knowing what type of charts people like to see. I had prepared what I thought were some really good graphs. Almost everybody loved the way they showed some data, and the VP came back and said, Nope, he just wants his pie charts. It's like 20 slices on 'em to show the two different years. And I just kind of went, all right, that'll take me about two seconds. I don't know why I wasted all this time to build this nice chart. But
Guest: Nick Desbarats (34:02):
Yeah. Well, I don't actually have an inherent objection to pie charts. Unlike Steve, for example, Steve, he never uses pie charts. I actually, I used to be kind of a never pie charter, 20 slices a little lot. Yeah, it
Host: Paul Barnhurst (34:16):
Definitely not a great use case
Guest: Nick Desbarats (34:20):
In my opinion. But yeah, well always coming back to the job of the chart, although I have actually created, it was actually a 25 slice pie chart, but that was basically because the job of the chart was to show that the market was very fractured. So even things like that, you always got to come back to the job of the chart.
Host: Paul Barnhurst (34:39):
I'm probably closer to Steven than you, although I have come off that I'm not as strict as I used to be. I'm like, generally, if there's four or five and that's what you want to do and it makes sense, go for it. I don't like using them myself, but as long as you use 'em effectively and you're not abusing 'em, go for it. That's kind of where I've come to.
Guest: Nick Desbarats (34:58):
And it is surprisingly tricky to actually know when it does actually make sense to use a pie chart. I have this whole decision tree for it, and most people kind of get wrong. They use a pie chart when actually a bar chart would've been a better choice or a tree map or a S stack bar chart or something like that. There are actually about eight considerations there. Do you actually have all the parts? Is your story more about fractions of a total rather than comparing individual parts with one another? All these things need to come into play, but I can teach this to somebody within a few minutes with a decision tree. Of course.
Host: Paul Barnhurst (35:35):
That's a great way to think about it, the decision tree, and I could appreciate that. So yeah, that's always a fun one is I like to say I put pie charts in kind of the art side of data visualisation in the sense of where people have opinions. And what I mean by that is there are some things that are lean a little more toward rules, and there are some that lean a little more toward opinion, and everybody has an opinion on pie charts.
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Guest: Nick Desbarats (37:24):
Yes. Yeah. Although when I published this very kind of detailed article about it in Nightingale, the Journal of the Data Visualisation Society, and I haven't seen a lot of great counter arguments to it, and I did manage to change a lot of minds, and so I'm always a bit hesitant. It's like, yes, of course there is some art, there is some science here, but I think there's a lot more science than people tend to assume that there is. Actually, I can maybe show that decision tree.
Host: Paul Barnhurst (37:54):
That'd be great.
Guest: Nick Desbarats (37:55):
I'd love that. Yeah, I'm just bringing it up. As you can see, yeah, pie charts are, they're in there, but there are of course other alternatives, other different ways of showing the breakdown of a total, and there are a lot of considerations in terms of what goes into this chart type choice. And so when people ask me, when should I use a pie chart? It's like, well, this is true, and this is true, and this is true and this isn't, and is, then you should use a pie chart. And so it's kind of unsurprising that people often don't use them appropriately because it's not as simple as like, oh, if you're showing the breakdown of a total use a pie chart. It's like, well, no, all of these are breakdowns of totals. There's other considerations that come into play. However, I can teach this to somebody within a few minutes. It doesn't require years of experience or a lot of art. It's mostly science.
Host: Paul Barnhurst (38:43):
It's a really good point. And far to the whole idea, that's mostly science, right? It's a decision tree that's not art.
Guest: Nick Desbarats (38:51):
No.
Host: Paul Barnhurst (38:52):
A decision here and there, and when you get done, you boiled it down to where you should probably go or the one or two
Guest: Nick Desbarats (38:59):
Option. Yeah, there's probably not a lot of subjectivity here. I always ask people, it's like, look, if you disagree with anything in here, then tell me. But once people step through it, 99 times out of a hundred. Yeah, I guess so. Can't really see any problems with it. There are some people who just say, no, no, I am just never going to use pie charts. But I'm like, okay, obviously that's fine, but can you point out where this is wrong? And I don't get good answers. So
Host: Paul Barnhurst (39:27):
Yeah, you have the people that are just, I'm not going to use a pie chart, but yeah, my joke that I always use, and I kind of have fun with one of the guys online, a fan of pie charts. I always joke, pie is only good for two things, eating and playing Pacman. Obviously I do that jokingly. I'm like, no, there are cases where it makes sense. I just don't like using more of a, I recognise that's a preference thing versus a rule.
Guest: Nick Desbarats (39:56):
Yeah, well, I mean it's fine. Basically, you want to make sure that you understand why, what are the specific reasons? And for example, there are actually chart types that I actually do not recommend using things like box plots. Oddly enough, I wrote a whole article about this a couple of years ago, and Nightingale as well, and it turned out to actually be their second most read article. It really hit a nerve with people. But nine times out of 10, because I got a tonne of replies on that, a huge response, and most people were like, yeah, I think you're right. I think we need to retire this chart type. Not inherently bad, but because alternatives, things like strip plots and distribution, heat maps, things like that are just easier to understand. They're less subject to misinterpretation. And so yeah, maybe we can put a link to that in the show notes as well. My box plot article, and there are actually about 10 other chart types that I actually, at least for your everyday charts, I'm not talking data art or anything like that, but for your everyday charts for reports and presentations, I just don't recommend using, not because they're bad, but because alternatives are always better. They're just easier to understand, less subject to misinterpretation. Things like connected scatterplots for example. I don't actually recommend using those just because again, there are better alternatives essentially.
Host: Paul Barnhurst (41:13):
It makes a lot of sense. Box plots are something I'm almost never using in FP&A or finance in the data and generally visualising, so I haven't even thought about that one, but I'll take your word for it, right? I've never thought I should be using this over that. It's just not a chart type I use in the work I'm doing.
Guest: Nick Desbarats (41:30):
Yeah, and that's fine. Although I would encourage people to use distribution charts in general. Things like strip plots where each value is a dot on a line kind of thing, those can actually be very useful and they're a bit kind of underused. Oftentimes, if you're showing an average, like a mean or a median, you really probably should be showing a distribution chart of some type. It just shouldn't be a box block.
Host: Paul Barnhurst (41:54):
I'm excited to dig into that a little bit more, but it makes sense on the surface. I get that. Alright, so here's one I want to ask you. Almost all charting tools come with default charts. I still remember early in my career going into Excel and selecting the 3D default pie charts with all kinds of colours and thinking it was cool. And so how do you decide if you should use a default or customise the chart? What advice would you give people? I think that's where a lot of people start. You just take what Excel gives you, and maybe it's just me, but I don't think the defaults in Excel are usually great charts that are following best practise kind of guidelines.
Guest: Nick Desbarats (42:31):
Often not, often not. It's gotten a lot better. All data visualisation products have gotten considerably better in terms of the fault. There's none of these crazy super saturated colours and heavy, thick black grid lines and things like that got that part figured out. So part of it is, yes, the defaults are still not great in a lot of cases, but even if they were, the software will only kind of get you so far because the software doesn't know the job of the chart. It doesn't know why you're creating this chart in the first place, what question you're trying to answer, what decision you're trying to support or whatever. And so the software is always going to be limited in that respect. It'll only get you so far. And then you have to basically bring the expertise to the table. Now of course, this is going to beg the obvious question about ai.
(43:20):
Well, can we just dump some data into Chad GBT or Claude or whatever and have it produce an expert level chart? And I try this all the time. Every time there's a new model that comes out, I kick the tyres. I try to use it for different kinds of data visualisation tasks, and it's still pretty hit and miss. I find basically where what it'll get you is, well, first of all, you have to know what to ask it for. And so that means you have to have data visualisation expertise to begin with. Essentially, you have to know things like I have to tell it what the job of the chart actually is, any constraints, that kind of thing. And so if you don't have data visualisation expertise, your initial prompt is probably not going to be very good. But more importantly, you also might not be able to spot all the problems with the result because there almost always are be an interval scale in your histogram, but the intervals are not all the same size.
(44:15):
If you don't know that, that's kind of a no-no, that's going to misrepresent the data, well, you're not going to spot the problem. And the other thing is just in terms of brute efficiency, most of the charts that I create, and I suspect that most people create are actually quite simple. Mostly bar charts and line charts and pie charts and things like that. And those, especially if you know how to use the tool well, like Excel or Google Sheets or whatever you're using, I can create those in 20 seconds, something like that. Whereas if I have to type out a description in full natural language, it just takes longer. And so for 80 or 90% of charts, it's actually, there's no gain there at all. It's just slower. And then what remains is basically charts that are more complex charts that would require many steps to create in Excel or Tableau or whatever you're using.
(45:11):
And so that's when I will say, okay, well let's try AI for this one. And it does take a while because if it's a more complex chart, then it takes a while to actually explain what you want as well. And then usually the result is the first crack anyways, it's kind of 80%. It's like, oh, okay, that's not bad. It's not exactly what I wanted. And if you had the expertise, you'll spot, there's some issues, the colour scale is off or whatever. And then you go into this kind of fix it loop with it, okay, change this, change that, move the legend up, whatever. But that's where often things in my experience kind of break down. You say, move the legend up. It goes, yeah, no problem. And then it just doesn't move the legend up, and then you have an unpublishable chart or it introduces a bug that it can't recover from.
(45:56):
Because when most AI tools create charts, what they're actually doing is running JavaScript code that calls a charting library like mat plot lib or D three or something like that. And it introduces some bug at some point in iteration number six that it just can't recover from. And so in a lot of cases, you don't actually end up with a publishable result or you end up with something that's like 80% of the way there, but then I'll have to bring it into Photoshop or Excel or something and do the last 20% myself because it just can't handle it. Yeah, I don't think ai, I do use it for certain relatively specific tasks, but just assuming that, oh, because AI is out there now, everybody is, you don't need data visualisation expertise. Everybody's a database expert now, not now. And to be honest, I think with the kind of trajectory that I see large language models on, I'm not an AI expert or whatever, but I suspect that that's going to be the case for the foreseeable future unless there's some profound fundamental new kind of breakthrough or direction of research or something like that. And I actually think that's the case for almost anything that requires expertise. If you want a good legal contract, you should be a lawyer. You have to know what to ask the AI for AI for, and you have to know how to evaluate what it gives back to you. And the fact that this clause would be unenforceable in court, if you're not a lawyer, you're not going to know that. So if you want great charts, you kind of have to have that data visualisation expertise.
Host: Paul Barnhurst (47:34):
This gets to something I've been preaching around financial modelling. We tested a bunch of these agents that will build models for you. I've tested, I dunno, 10 of 'em now. We did a whole podcast series and two conclusions I came to, I said, the average financial modeller will get a lot more benefit learning excel and modelling better than trying to get AI to build the model. And what you're saying is kind of the same for data vz, and I think it gets to this idea that ai, as I put it, is a magnifier. If you know what you're doing, it will magnify that. If you don't know what you're doing, it will magnify that. It's just a question of time.
Guest: Nick Desbarats (48:08):
Yeah, I think that's probably a good kind of framing in many domains and probably most in fact, yeah, medicine, if you want a good diagnosis, you should be a doctor. You got to know what the relevant information is you need to provide to the AI and how to know when one of the diagnoses that shoots out is like, no, no, no. That is definitely not it. But if you're not a doctor, how would you know? Right?
Host: Paul Barnhurst (48:35):
Yeah. It's like some guy, I can't remember what he wanted to do, but it told him to take a certain medicine that was poisoning him, and he ended up in the ER and almost died. He followed AI's advice
Guest: Nick Desbarats (48:46):
Or people using AI as therapists. It's like,
Host: Paul Barnhurst (48:52):
Good idea. Yeah,
Guest: Nick Desbarats (48:52):
I would for use it as, no, a lot of people are, anyways, different topic.
Host: Paul Barnhurst (48:56):
Yeah, totally different subject. But I think the message, whether it applies to data visualisation, look, AI can help you. I think that's the bottom line. I don't think any of us would disagree, but if you want to know where to start with the fundamentals,
Guest: Nick Desbarats (49:10):
You need the domain expertise. Just like with financial modelling as you're saying, or anything else that requires any kind of specialised expertise. AI is just, I think your framing is good. It's a magnifier. And so if you don't have the expertise, it's just going to make you a loaded gun. Right. It's going to point anywhere though, right. It's not going to point in the right place.
Host: Paul Barnhurst (49:31):
Agreed. Alright, so we're going to move on to a section. I have, I kind of call this the FP&A section, and I'd love to get your thought for people who want to build great charts, dashboards, what would you say is the number one technical skill they need?
Guest: Nick Desbarats (49:46):
I would say that probably chart type selection. I would call that a kind of technical skill. I know a lot of people, they would say it's more subjective, it's more arts, more intuition. But I think that is a technical skill that you can learn without having years of experience and a lot of judgement and intuition. And there are many other kind of technical skills, not just choosing chart types, but choosing colour palettes and scale ranges and things like that. But I would probably start there because choosing chart types is what causes a lot of, not just charts, but dashboards as well to flop. And I see it everywhere charts that it's a regular bar chart when it should have been a stack bar chart or it's a stack bar chart when it should have been a clustered bar chart or whatever. And so learning that kind of technical skill chart type selection, I think it's going to have a lot of bang for the buck. Got it. That
Host: Paul Barnhurst (50:42):
Is a good one. I hadn't directly thought of that as a technical skill, but makes a lot of sense. What about soft skill,
Guest: Nick Desbarats (50:48):
I guess? Yeah. Maybe sort of empathy, not sympathy, but empathy in terms of really trying to get into the audience's head and understand, especially if they've asked you for data so you can really understand why. Are there concerns that they have objectives that they have? Kind of touched on this a little bit before, but that's a skill that unfortunately a lot of data people sort of lack. They want to live in their spreadsheets, they want to live in their databases and their software, and they don't want to go into the messy sales meetings and marketing meetings and operational arguments and things like that. And so one of the things that I really encourage people to do in my workshop, it's like, no, you got to get out of the spreadsheet, get outside, go to meetings. Even if they're not going to be talking about data, they're just going to be talking about, I don't know, customer personas or something like that, go to the meeting. Because if you don't get that what's happening outside of the data and in your user's heads essentially, then you're going to be of limited value to them.
Host: Paul Barnhurst (51:59):
As you were saying this, preach the same thing in FP&A. Get out of the spreadsheet, go sit in the meetings. Funny how much you really learn with any job that sometimes you think is technical and you can do all at your desk. That empathy, that's understanding is really critical to so many of those jobs. I love that you shared that. I talk about that all the time in different areas, and I love that you think the same way, but just relating it to data visualisation, it's that reminder of get out from your desk.
Guest: Nick Desbarats (52:29):
Yeah. And it would be the same if you were a coder or any other kind of technical person. I think the same advice would apply. Yeah,
Host: Paul Barnhurst (52:36):
Agreed. It's a good reminder. All right, so we have a few questions just to get know you a little bit better. So the first one is, what do you like to do in your spare time? What do you like to do for fun when you're not charting,
Guest: Nick Desbarats (52:48):
I guess? Yeah, nothing terribly exciting. Just running. And there's one unusual kind of hobby that I have which is Israeli self-defense. I've been studied, that's
Host: Paul Barnhurst (52:59):
A first on the show
Guest: Nick Desbarats (53:00):
Called Kraft maga, which is, I'm not Israeli or anything, but I won't say anything about the political situation there. But basically it's kinds, a bit martial arts sort of. But it was invented actually in the 1930s because fascist gangs were essentially attacking Jewish populations all over Europe. And so they developed this of fighting technique and I just kind of took it on a whim. I took a course years ago and I was like, this is actually super interested. I've never been interested in martial arts or MMA or anything like that, but this is actually, I do now see some parallels between, it's kind of how I teach because people think, well, fighting is all chaotic. And it's like just with experience, I guess you would learn how to defend yourself, but it's like, no, no, no. Actually, you can learn very specific techniques and ways of thinking that are actually going to allow you to survive much more likely anyways if you're actually attacked. And it's very practical because it's not martial arts, which means there's no rules. If somebody's attacking you in a streak, they can do anything. They can do all sorts of things even that you would never be allowed to do in an MMA right punch in the throat kicking the groin, like bite your ear or whatever.
Host: Paul Barnhurst (54:22):
The rules go out the window when it's survival.
Guest: Nick Desbarats (54:25):
And so what do you do though when there are no rules? And so I just became quite fascinated with it and it's an amazing way to stay in shape. It just, it's everything. It's strength, flexibility, endurance, the whole shebang. So yeah, that's my unusual hobby.
Host: Paul Barnhurst (54:42):
We'll do one more get to know you question here. I'm debating between the couple I have, but we'll go with this one. What's on your desk right now that would make people say, that's so Nick?
Guest: Nick Desbarats (54:52):
Or it could be on your bookcase as well. Yeah. Well, the thing that's on my desk is actually what I was talking about is this. It's a new book about craft, and it's actually written by my instructor. He just wrote a book, so I just started reading that, but he wouldn't say that. So Nick, because most people actually aren't aware that this is my hobby. Something that's more typical would probably be, yeah, there's lots of books about this. Psychology, psychology, psychology, psychology, psychology. So that would be more probably maybe typical.
Host: Paul Barnhurst (55:26):
Alright, we're going to do one more for fun. I just want to see what you say. If you had a ban, I think I may know where you're going to go. One chart type from existence, which chart are you banning?
Guest: Nick Desbarats (55:36):
Yeah, well, like I said, there's about 10 that I don't recommend using. And yeah, probably number one would be not histograms, box plots,
Host: Paul Barnhurst (55:46):
Histograms are fine. I figured that's what you were going to say based
Guest: Nick Desbarats (55:49):
On that. Yeah. And oddly enough, I think I might be well on my way to actually achieving that goal, because like I said, the article that I wrote about this a few years ago went very viral, and I know now it's been circulating, especially in the research community where box plots are pretty ubiquitous and really forcing people to sort of, oftentimes they'll just use a box plot without even thinking about another tar types. Like, oh, I want to show distributions, I automatically use box plots.
Host: Paul Barnhurst (56:20):
That's what I do right now. It's just not something I do very often. So I just default to a box plot.
Guest: Nick Desbarats (56:25):
But really I kind of lay out my reasoning in that article. And I've had a few people, not a lot, but a few people come back and say, well, I still like box plot, so I'm going to continue using 'em. And I'm like, that's absolutely fine. You do, but can you tell me why? And I don't get good answers. And so I think maybe it could be that oddly enough, even though it wasn't really intentional, it might be my legacy. I'm the guy who killed box plots.
Host: Paul Barnhurst (56:51):
Well, we all got to have a legacy. Congratulations.
Guest: Nick Desbarats (56:54):
Yeah, I don't hate them. It's just that there were better alternatives. And one of the big concerns I have though is that I used to teach them and I just saw so many people get so confused by them and basically it made them feel stupid. And so I was like, okay, if you absolutely have to use a more complicated chart type, because a simpler chart type just can't say what you need to say, okay, sometimes you got to do that. But in this case, there were simpler alternatives that could communicate exactly the same thing, could do the same job. And so it was just making people feel stupid for nothing. And so I was like, I think we should stop doing that.
Host: Paul Barnhurst (57:30):
Makes sense. Alright, so we've got a little longer than I plan, but I think this has been a great conversation. So just kind of wrap up, if people want to learn more about you, the courses and resources you offer, what's the best way for them to get in touch or learn more about what you have available?
Guest: Nick Desbarats (57:46):
Yeah, my website is just practical reporting.com, all one word. People Google me, they'll find my LinkedIn page connect with me on LinkedIn. I'm always happy to do that. I'm the only person with my crazily spelled name. I guess it'll be probably somewhere in the show notes or the video title or something. So you can get the spelling from that. And yeah, of course, always happy to connect with people. There's a contact form on my practical reporting.com website. You can always reach out to me that way as well.
Host: Paul Barnhurst (58:15):
Yeah, and I'll say, I know you're more than willing to connect. I know you gave me some advice on some of the stuff I was doing, and I really appreciate it and made some changes from that. And appreciate you carving out an hour of your time to chat today. So thank you so much for joining us, Nick. It was a great conversation. Yeah,
Guest: Nick Desbarats (58:30):
Likewise. Yeah, I really enjoyed it. It was great questions.
Host: Paul Barnhurst (58:33):
That's it for today's episode of FP&A Unlocked. If you enjoy FP&A unlocked, please take a moment to leave a five-star rating and review. It's the best way to support the FP&A guy and help more FP&A professionals discover the show. Remember, you can earn CPE credit for this episode by visiting earmarkcpe.com. Downloading the app and completing the quiz. If you need continuing education credits for the FPAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FP&A guy, and I'll see you next time.
