Jon Krohn: 00:00:00
This is episode number 741 with Dr. Alberto Cairo, professor at the University of Miami. Today’s episode is brought to you by Gurobi, the Decision Intelligence leader, by Intel and HPE Ezmeral Software Solutions, and by CloudWolf, the Cloud Skills platform.
00:00:20
Welcome to the Super Data Science Podcast, the most listened-to podcast in the data science industry. Each week, we bring you inspiring people and ideas to help you build a successful career in data science. I’m your host, Jon Krohn. Thanks for joining me today. And now let’s make the complex simple.
00:00:51
Welcome back to the Super Data Science Podcast. Today, we’re fortunate to have the visionary professor Alberto Cairo on the show. Alberto is the Knight Chair in infographics and data visualization at the University of Miami. He leads visualization efforts at the University of Miami’s Institute for Data Science & Computing. He’s a consultant for Google, the US Government, and many more companies and institutions. He’s written three best-selling books on data visualization all in the past decade. His fourth book, The Art of Insight, was just published. As a treat for you, I will personally ship 10 physical copies of his new book, The Art of Insight, to people who by Saturday, December 23rd, share what they think of today’s episode on social media. To be eligible for this giveaway, please share your thoughts by commenting and or resharing the LinkedIn posts that I publish about Alberto’s episode from my personal LinkedIn account today. I will pick the 10 book recipients based on the quality of their comment or post.
00:01:47
Today’s episode will be of interest to anyone who’d like to understand how to communicate with data more effectively. In this episode, Alberto details how data visualization relates to the very meaning of life. He also talks about what it takes to enter into a meditation-like flow state when creating visualizations. He talks about when the rules of data communication should be broken, he provides his data visualization tips and tricks, he talks about how infographics can drive social change and how extended reality AI and other emerging technologies will change data visualization in the coming years. All right, you ready for this enriching episode? Let’s go.
00:02:28
Alberto, welcome to the Super Data Science Podcast. It’s great to have you here today. Where are you calling in from?
Alberto Cairo: 00:02:34
Well, I am in beautiful and sunny Miami, Florida. And thank you so much for having me, it’s a pleasure.
Jon Krohn: 00:02:38
Yeah, yeah. I’m in increasingly cold and constantly rainy, wintry New York. Snow soon, no doubt. Probably by the time this episode airs, I’ll be wondering why I wasn’t one of my many friends that moved down to Miami in the pandemic.
Alberto Cairo: 00:02:58
Well, you will be more than welcome.
Jon Krohn: 00:03:02
I do hear that Miami is very welcoming indeed. So interesting story, you don’t know this, but Andrew Vlahutin, who is a data scientist on my team at my machine learning company, Nebula, he loves your books and he highly recommended that I keep an eye out for when you release a new book so that I can approach you and ask you to be a guest on the show. I guess he’s checked out some of your interviews and thought they were absolutely amazing, and so we’ve just been creeping you for a long time. It’s been at least a year of just keeping an eye on what you’ve been up to, Alberto, and then we saw that you had a book coming out. And now with The Art of Insight: How Great Visualization Designers Think coming out, we tried to get you on the air and you said yes, thank you very much.
Alberto Cairo: 00:03:55
Yeah, I did say yes. I really appreciate that. You’re very kind. Yeah. And I’m glad that my books have been helpful and enjoyable to you both.
Jon Krohn: 00:04:04
Yeah, no doubt. And so this latest book, The Art of Insight, it’s a journey into how top data visualization designers make their design choices. And you both start and end the book reflecting on a philosophical question. This is what everyone who is picking up a data visualization book is curious to know, it’s what is the meaning of life?
Alberto Cairo: 00:04:28
Yeah, absolutely. Yes. That’s a funny insight that you got from the book, but it’s actually true. I mean, The Art of Insight is very different to my previous books. Prior to The Art of Insight, I wrote the Functional Art and the Truthful Art. Both of them are introductory books essentially about data visualization, outlining my own take about how data visualization is made, how it is designed, my own opinions about the craft. There are semi-technical books, how-to books. And then I wrote How Charts Lie, which is the first book that I wrote for the general public, essentially how to become better readers of the data visualizations that we see every day in news media and social media, et cetera. But The Art of Insight is a completely different beast. I mean, The Art of Insight is essentially my own reflection about how not only data visualization, but information design in general because data visualization is part of a broader field, the field of information design. How it has shaped my life, how it has given meaning to who I am as a person, and I described that in the book as you know, both at the very beginning and also at the very end, and in some reflections that I sprinkle here and there throughout the book.
00:05:53
And in order to undertake this journey, I selected a sample of 20-something designers from all over the world who produce data visualizations. I must say that this sample is not really representative of anything, it’s just like the top 25 people who appear on a long list that I created years ago says, “If I had to have conversations about data visualization and about life and how those things connect to each other, et cetera, what will be my top names?” And I came up with a list of 70 or 80-something people, but I didn’t have enough space for everybody in the book. I may need to write a follow-up, right? But it’s essentially an effort to reconnect with the field, to reflect about what data visualization is truly about. In my opinion, it’s a very personal book, very subjective, quite opinionated, and I try to reflect the very broad diversity of approaches that exist to data visualization. That’s essentially what it is. I know that I sound a little bit vague, but we can get into the details throughout this conversation.
Jon Krohn: 00:07:03
All right, yeah. So to kick things off, Alberto, how do visualization magicians, so these experts, how do they transform raw data into narratives that resonate with people’s emotions and intellect?
Alberto Cairo: 00:07:16
Well, it greatly depends on the designer because as I said before, I group all these designers into different beans in the book. I mean, I call some people the pragmatists, some people I call them the eccentrics. Then I have the ambassadors and then I have the narrators. And each type of person in the book approaches data and data visualization in a different way and with different goals and different expectations and different motivations. So the eccentrics, for example, are designers who appear in the book that try to push the boundaries of what is “acceptable”, quotation marks in there, acceptable in data visualization. They try to innovate, they try to create new graphic forms, new way to display the data, experiment with the form. And they often create data visualizations that don’t really have any, let’s say, analytical purpose. They just want to create things that look nice and interesting and evocative in some senses, poetic in some senses as well.
00:08:24
Then the ambassadors are people who are perhaps a little bit more similar to you and I, people who are interested in using data visualization as a means towards a broader goal, which is to educate people about scientific thinking, about data literacy and so on and so forth. And the narrators are the ones who are closest to my own career because they’re mostly journalists, people who communicate with data visualization. So each designer in the book has a slightly different method to create data visualizations. Broadly speaking, all of them follow the same path of I try to understand the data, then I try to find the main insights from the data, and then I try to select those insights and create something that highlights those insights. But then the purposes are completely different and beyond those very basic elements of a process, each one of these designers have a different way of doing things.
Jon Krohn: 00:09:28
Very cool, yeah. And so you might have to break this down again I guess by category potentially, but something that comes up in the first chapter, which is called On Magic, how does data visualization act as a reflective practice for the designers as well? So it’s more than just creating a narrative, it’s also about reflection and so this influences their personal growth and their worldview.
Alberto Cairo: 00:09:53
Yep, and their thinking in general. This is something that I sometimes explain to my students when I teach visualization, that I see visualization very similarly to how I see writing in many senses. Obviously, I have written plenty of books and articles, but I don’t write books only because I want to communicate something. I write books because I want to understand that something better, and one of the best ways to try to understand something better is to try to teach it.
Jon Krohn: 00:10:23
Yeah, 100%.
Alberto Cairo: 00:10:25
And to try to systematically write about it. That’s how you organize your thoughts. That process of self-reflection is also true of data visualization. You can only visualize something well if you understand that thing well. And one of the best ways to understand it well is to visualize it, to try to visualize it. And at the same time, people who appear in the book sometimes use the sheer process of designing visualizations as something that is similar to meditation, for some reason. They get so deeply into the design, and several of these designers describe these, and I have experienced it myself. You enter in state of flow, you forget completely about the world, and you immerse yourself into the work completely to the point in which your conscience disappears for quite a while. It’s a pretty interesting process.
Jon Krohn: 00:11:16
Yeah. That was exactly my next question, so that’s perfect. So I think this concept of flow, I always probably butcher this person’s name so my apologies, but Mihaly Csikszentmihalyi?
Alberto Cairo: 00:11:29
Yeah. Yep, that’s very close. I’m very bad at pronouncing that last name, by the way. But yeah, he has this book titled Flow, in which he… Sorry, I interrupted you. You were going to [inaudible 00:11:39].
Jon Krohn: 00:11:38
No, not at all. Yeah, it is his book Flow, and it’s exactly as you described. It’s when you’re deeply engaged in a creative activity. And so I mean, I’d love to hear of times or a time when you have experienced this meditative state.
Alberto Cairo: 00:11:53
Oh, many times. Yeah, many times. Yeah, many, many times. For example, I don’t remember whether I tell this anecdote in the book. I think that I do in one of the chapters, but I remember myself years ago creating a cartogram of Brazil in which I represented the population of Brazil with little squares, each one of them corresponding, I think that it was to 1000 or several thousand people. So the population of Brazil is 200, 200 million people, so try to imagine the amount of squares that I needed to represent the population of each one of the States of Brazil. And then I had to arrange the squares to broadly represent the shape of those States as cartograms usually do. Now, if I had to make that design today, 15 years later, I would do it programmatically. I will use perhaps R and I will download a library and I will let the software do the cartogram for me.
00:12:53
But at the time, I didn’t know how to use programming. I made it by hand, duplicating those squares one by one and then arranging them, and it was delightful. I don’t remember how many hours I spent designing that graphic, perhaps two days, just arranging those little squares and adjusting their position and then coloring them according to the other variable that I was representing. It was absolutely delightful. I felt myself disappear into the work, and that is what Flow describes. That’s the state of flow, which is something that people who engage with arts and crafts experiment quite often. And in my opinion, data visualization is above everything else a craft, it’s something that we do with our hands in some sense, by manipulating things on a screen. But we still use our brain on our hands, and when we do that, we immerse ourselves in the work.
Jon Krohn: 00:13:49
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00:14:34
Yep. And so I was going to ask you what kinds of conditions need to be in place for you to reach that state. I don’t know if you have any insight into the specifically what kinds of conditions somebody might need in order for data visualization in particular to be something they can go into a flow state in. And as you were talking, I was thinking about how maybe it would have been easier before computers to be in the flow as you’re creating a visualization because there’s something for me, even when I’m recording a podcast episode, I’m taking notes by hand. And yes, it’s on digital paper so that I can’t lose anything. But there’s something about writing or using my hands that allows me to feel more connected with what I’m doing than if I’m say, typing. And I have hypotheses, I don’t know if it’s related to it being the case for me and probably for you as well, that I grew up writing but not typing as a child.
00:15:31
So maybe there’s something about that. But it’s also occurred to me that it could be something like that you only need one side of your brain, of the motor cortex, to be writing with your hand, but the keyboard takes up both sides of your motor cortex and so maybe there’s just less availability for creativity or flow. Anyway, I’m going off on tangents and I’m putting thoughts into your mouth, but the main idea here is what do we need in order to get into a flow state? Yeah, I don’t know if there’s particular conditions that you [inaudible 00:16:02].
Alberto Cairo: 00:16:01
Yes. Sorry, all right. So I have experimented it not only through the manipulation of objects on the screen, the same way that I would manipulate them in the real world. You can certainly enter a state of flow by manipulating objects, again, by engaging in the craft. But I have entered that state of flow by writing code or by doing another activity. All that it is required is that you pay attention to the present moment, that’s all that matters. So in that sense, it’s like meditation. I mean, meditation is all about focusing on the present, forgetting about the future, forgetting about the past, focusing your entire attention on what you are doing at that very moment.
00:16:46
And [inaudible 00:16:47] and whenever you feel your attention steering away from the present moment, you bring it back. So the same way that you can enter a state of meditation while you are walking, as long as you are paying attention, paying attention to what you see or your own breath, and there are different ways to focus your attention. It’s the same thing when you are doing some meaningful work, all that matters is that you pay attention to the present moment.
Jon Krohn: 00:17:13
Yeah. And as you’ve been saying that, I’ve been reflecting on my own experiences, creating visualizations with code and I think there is something that makes it easier perhaps than other kinds of coding experiences because often as long as we’re dealing with summary data, something that fits into a relatively constrained data frame on a single machine, we’re not talking about both reducing the size of the dataset and creating the visualization at the same time. So if you have this relatively constrained data frame, you’re going to be getting the results on your screen instantly.
Alberto Cairo: 00:17:51
Instantly.
Jon Krohn: 00:17:52
And so it’s this continuous flow of output, output. You can change arguments, see how that changes things. Okay, you changed this color, you changed this positioning.
Alberto Cairo: 00:18:01
And that prompts you to keep going, that’s the other thing. I mean, in the past, for example, I didn’t begin my career designing data visualization, I began my career designing information graphics, which is information design. So I did, for example, I made tons of visual explanations about, I don’t know, the latest NASA mission to Mars or whatever. And I had to design some of the trajectory, the trajectory of the spacecraft or whatever. I made this spaceship in 3D using a program called Maya, which is used in movies to create 3D objects. I know how to model and texture things in Maya. And whenever I had to create a project like that, I usually begun by modeling the object that I was going to show because seeing it in front of me rising up, appearing little by little and taking shape in front of me while I was manipulating it like if it were a piece of clay essentially, that put me in a state of flow and that prompted me to keep going, “Oh, this is so cool”.
00:19:08
It’s like your brain feels encouraged to keep working and working and working. And perhaps that is similar to what you are describing about the constant back and forth between writing a line of code and seeing the results immediately. That gets you excited, “Oh, this is so cool. Let me keep going, let me keep going”. And this is completely unconscious. You don’t describe that in any meaningful way. It is your brain acting on its own, and that is what makes you enter this state.
Jon Krohn: 00:19:35
And that describing like in meditation, once you’re thinking, as soon as you start thinking about meditating, you’ve blown it.
Alberto Cairo: 00:19:44
Yeah. You don’t think about thinking, it’s like you just let your body and your brain be present in the present.
Jon Krohn: 00:19:51
Yeah. So related to this idea of flow, earlier you talked about different kinds of data visualization experts, different kinds of designers. So for example, you mentioned the eccentrics. And later in your book in part two, you talk about the eccentrics as having an autotelic temperament. So actually I did an episode a while ago, I can probably look it up quickly. And so I did this episode about a year ago, episode 618, it was called The Joy of Atelic Activities. And so there’s a concept that I learned from a book called Four Thousand Weeks is what the book was called, which is about that’s the number of weeks that you would have in an 80-year life span, typical human life span. And the book was talking about how engaging in activities where there is no desired outcome, so going for a hike where you’re not like, “Oh, I’m going to get an extra kilometer this time”.
Alberto Cairo: 00:20:58
“I need to get somewhere”. And so the purpose of the trip is not to get somewhere, the purpose of the trip is the walking, is the path that you’re taking, right? Every single step that you take, it’s its own purpose.
Jon Krohn: 00:21:12
Exactly, exactly. And so you introduced a new word to me in your book, which is clearly in the same family of words. So this autotelic temperament you say eccentrics have. So autotelic meaning then where atelic means you’re doing an activity without any reward in mind, autotelic would mean that the activity itself is rewarding. So these designers, they have this autotelic temperament where the work is the reward rather than some external goal.
Alberto Cairo: 00:21:48
Yep.
Jon Krohn: 00:21:51
So do you have any insight into this? What kind of inner drive compels these designers to experiment [inaudible 00:21:56] sense?
Alberto Cairo: 00:21:55
Pleasure. It’s just pleasure, and that is another thing that I talk about in the book. I mentioned I think that is in the second chapter that my approach to visualization is also hedonistic just like in the sense of that I believe that pleasure has value in itself, and it’s a drive that we take. I don’t work in data visualization or in journalism in general, I didn’t get into a career in journalism because I wanted to make money. If I wanted to make money, I would have studied something else other than journalism. You don’t make a lot of money in journalism. But working in journalism for so many years before I became a professor at a university was extremely pleasurable. For my type of personality, it’s like being surrounded by people who were highly motivated to gather information and put it at the surface of the public, discovering new stories every day. It was a constant adrenaline rush in some sense, right?
00:22:58
So it was very hedonistic in that sense, it was related to pleasure. And I think that in The Art of Insight, I mentioned at some point that sure, I can talk about my values. Obviously, I like to create graphics that are reliable, trustworthy, that inform well, that don’t misinform people, blah, blah, blah. But when I take a look at my core motivations to be in this field, what really drives me, what really pushes me to keep going in this field is the pleasure that I feel. I feel pleasure by designing graphics, by teaching classes, by giving workshops, by writing books about the craft.
00:23:36
The core inner motivation is the state of pleasure that I reach. It’s a state of inner peace, of happiness and also pleasure. And I think that many people in the book describe this as well. It’s like they like what they do, and the purpose of what they do is not instrumental in the sense that there is not a final goal that they want to reach. The goal is the work. They enjoy doing the work, making the graphics that they create. Even if they don’t, those graphics don’t go anywhere. Sometimes the graphics that appear in the book have never been published before because they are graphics that people create for themselves just because they want to see the data visualize. And that’s it, that’s the purpose of the graphic.
Jon Krohn: 00:24:21
Nice, yeah. I think related to this idea of doing it for enjoyment is that you feature many artists in the book, like I don’t know how to pronounce his name, Nadieh Bremer?
Alberto Cairo: 00:24:31
Nadieh. Yeah, Nadieh Bremer.
Jon Krohn: 00:24:34
Nadieh Bremer? And so they take a creative approach to data visualization, so these are more artistic. So what kinds of extra value do you see added to these more artistic data visualizations that people like Nadieh do, and how can they enhance storytelling if you take this creative route?
Alberto Cairo: 00:25:00
So one thing that I mentioned in the book is that we should not mix things up in the sense that the work that Nadieh creates would not be appropriate in a different context, right? If you want to design a graphic that is, let’s say for analytical purposes, to extract meaning from the data, to analyze data, whatever, or a project that tries to communicate the main messages from the data to a public, to the public, that will not be the type of work that I will design myself. That is not the value that it has, it will not fit the purpose. And in The Art of Insight, I talk a lot about different dialects of data visualization or different ways of making data visualization the same way that writing is not a single thing. Writing is just the combination of a syntax, grammar, symbols. But beyond those syntax and grammar, et cetera, what we do with it greatly depends on what we want to achieve.
00:25:57
In some cases, we write to communicate something. In some cases, we write to understand something better. In some cases, we write just because we want to express ourselves and try to ignite some emotion on the part of the audience. So data visualization is not that different. The work that Nadieh makes is intended to look great, to look nice, to look interesting and unique. In some cases, mysterious, and I believe that that is the value. I mean, she creates art that is intriguing in some sense. Even if in some cases the graphics that she designs are very hard to decode, but that’s not the point. They’re not intended to represent the data to provide insights from the data, it’s just intended to look great and to attract people’s attention to the data to create some connection between the designer, the reader of the graphic, having the graphic in between, the interaction between the designer and the reader.
Jon Krohn: 00:26:59
To create this kind of interaction with the reader, is there a balance that sometimes needs to be struck between this emotional engagement and factual accuracy?
Alberto Cairo: 00:27:10
Well, they need to be factually accurate in the sense that the data needs to be correctly represented in a very general sense, right? So yeah, sure. I mean, above all, we need to try to represent the data as accurately as possible, but different types of encodings represent data at different levels of accuracy. It’s not that if we get into the nitty-gritty of visual encodings, we know for example that the encoding length or height to represent data is much more accurate, perceptually speaking, than the encoding area. So when we want to compare numbers to each other, it is better to represent them let’s say through a bar graph than through a bubble chart. If the purpose is to compare, that if is always super important. So in a very broad sense of the word sure, the data needs to be represented accurately, but that accurately may mean different things depending on the type of graphic that we’re talking about.
Jon Krohn: 00:28:09
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00:28:52
Nice. That was a great answer, yeah. I thought there might be some leeway on the factualness, but no. So when people are trying to appreciate, particularly maybe one of these especially creative data visualizations, and people are trying to reverse engineer or understand what’s going on in there, in your book, you talk about personal taste playing a significant role even if people try, even if experts try to justify them as universal principles. So yeah, as an experienced data visualizer yourself and obviously having spoken to all these kinds of experts, how do you balance having stylistic preferences with broader universal best practices?
Alberto Cairo: 00:29:40
Yeah. I think that there are ways to do that, and every designer will create their own path to doing this. In the book, I described my own. It’s like in my case, the way that I make choices in data visualization is informed obviously by my own taste and my own experience of observing what in general works or doesn’t work that greatly depends on the context on audience and other types of constraints. I also try to keep up with research. So I try to read papers that are published every year that are sent to conference, like IEEE VIS, and I try to keep up with the science, so to speak. But at the end, decisions in data visualization design are highly parochial, not universal. It’s like every graphic deserves its own design. Depending on the nature of the data, depending on the nature of the audience, depending on what we want to communicate or what we want to highlight, choices may vary. There are certain general, very general principles that may form these choices.
00:30:49
For example, in terms of color palettes, right? We know that because there have been studies about these that let’s say the rainbow color palette, which is a type of color palette that uses the entire spectrum of color that the human eye can perceive, is usually not a good idea to represent continuous data because it’s a naturally perceived categorical color palette that is being used to represent continuous data. So it’s not a good idea, there’s a mismatching there. And there are some research that has shown that the rainbow color palette makes people perceive artificial boundaries in that continuous scale, right? So that gives you a good reason to be wary about using the rainbow color palette I would say, right? That’s the way that my process works. Because I know these, I tend not to use the rainbow color palette. But at the same time, there are other factors that we may want to consider.
00:31:48
For example, if the graphic is designed for an extremely specific audience, for scientists who have used this type of graphic for decades, they may know how to read that color palette. They may be already aware that they should not see artificial boundaries between the colors, there is a continuous color scheme. And it might be helpful to have the entire spectrum of color, and that may give them a good reason to keep using the rainbow color palette, I don’t know. I still don’t recommend it just because of what the science says, but I’m open to discussion particularly with people who are very familiar with that type of visualization.
00:32:28
Familiarity with the type of graphic that we use is also a factor to consider whenever making choices in data visualization. And tradition, traditional familiarity go hand-in-hand in these cases. So that’s the way that I weigh all these things, it’s all very subjective at the end. One of the points that I make in the book is that, in my opinion, decisions in data visualization are always subjective, but they should never be arbitrary. For every decision that I make in a data visualization, I can provide reasons that are backed up either by tradition familiarity or by empirical science or by experience throughout the years.
Jon Krohn: 00:33:09
Fantastic analogy. And yeah. So perhaps related to this idea of not using rainbow colors for a continuous scale, in chapter one, you talk with Ed Hawkins and he discusses the origin of his warming stripes visualization, which has become an icon of climate change awareness despite breaking visualization conventions. So why did that visualization break the rules? Maybe you could describe it a bit to the audience.
Alberto Cairo: 00:33:38
Yeah, yeah. The warming stripes is exactly what the name of the graphic says it is. It’s a bunch of vertical stripes, each one of them color-coded according to the variation of global temperatures. It has blue shades of color and red shades of color that those represent a year, each one of the stripe represents a year. And if the stripe is blue, it means that the temperature that year was below an average, I think that the average is an average of the 20th century. And if the color is red or the shade of color is red, it means that it’s above that average, and that graphic breaks a lot of conventions. I remember seeing it myself for the first time and wondering, what is this? I mean, it doesn’t have a legend, it doesn’t have a scale. The years represented by the stripes are not labeled, so we don’t really know what year the graphic begins and when it ends.
00:34:40
So it breaks a lot of conventions in the way that the information is represented. The encoding is clear, the encoding is using color hue and color shade, but then we don’t have a clue about the specifics of the graphics. It’s a little bit bustling if you are a data visualization designer, but when you think about it and when you know the origins of the graphic and you understand what the graphic was designed for, then you understand the decisions that were made, and this is critical to criticizing any data visualization. One of the points that I’m making in the book is that in some cases, I’ve made the mistake of criticizing a graphic without learning about the intentions of the designer, and that’s wrong. You need to know the intentions of the designer. You don’t know what the designer was trying to do or communicate with that graphic, what the purpose of the graphic was.
00:35:30
And the warming stripes is not a graphic that was used or created to communicate anything specific or accurate about the data or to analyze the data. Hawkins was going to attend a conference, a panel in a conference. He was going to sit with other people and have a chat with all the… And he wanted to design a cool looking visual to put in the background to get the audience interested about climate change, to intrigue them to prompt questions, “What is this about?” “This looks so cool. This looks so interesting, tell me more”. So the visualization was designed specifically with the purpose of opening up the door to providing more information later on.
00:36:12
The visualization doesn’t provide that information, but it opens the door, it gets people curious about the data. And then you can show them more analytical graphics, right? Take a look at the Hawkins [inaudible 00:36:23] chart, which is a very, very classical way of representing global warming throughout the years. That’s a more analytical graphic in some sense. So graphics need to be, I believe visualizations need to be judged and critiqued according to their own purposes, to their own standards, and to the standards of the intentions of the designer who created them.
Jon Krohn: 00:36:43
Perfect. Yeah, great explanation of visualization. And I am familiar with it, it’s the visualization that you don’t forget about. And so yeah, so I can see how even though you couldn’t refer to these lines and say, “Oh, look at that particularly red line or that particularly blue line, I want to know what year that is”, it doesn’t matter. The point of the visualization is to convey, “That’s a lot of red”.
Alberto Cairo: 00:37:09
Exactly. That was the whole point, and to create a striking visual that will get people curious about the data. That’s it, that was its entire purpose, and it fulfilled that purpose because that graphic has become one of the most iconic visualizations of the 20th century so far. And good for Hawkins, I mean, great. He did a great job at designing that graphic.
Jon Krohn: 00:37:31
Yeah. It seems like that is an example of smart brevity where you take away information that most people would think is important, like the year, the specific year. And so in chapter 18 in particular, you praise Axios the news provider on their smart brevity approach to data visualization storytelling. In your view, maybe you can summarize for us the keys to distilling data stories down to their essence. Yeah, is there a process we can follow?
Alberto Cairo: 00:38:05
That relates to one of my favorite books about design, which is Simplicity by John Maeda. So that book is essentially his theory about what simplicity in design is truly about. And there is a great quote from that book that I have cited in some of my books in which Maeda says, “Simplicity is about subtracting the obvious and adding the meaningful”, that’s what it’s all about, right? So in some sense, you try to reduce the presentation of the information to its bare bones, to its essence, but you should not go too far into that process. You need to keep that essence. If you reduce too much, then you are going to sacrifice the effectiveness of the graphic. There’s a point in which you cannot reduce more, and then you need to also think about whether you should add more to the graphic in order to make the information more understandable.
00:39:06
So this probably sounds very vague and very abstract, but we can actually derive some very concrete practice from it. And so whenever I design a visualization myself, I take a look at every single component of that visualization, the axis, the legends, the tick marks, the grid lines, every single object, and I try to make an honest assessment. Does this object fulfill any purpose? Why is this feature of the graphic still on the graphic? Does it make the graphic more understandable, easier to read, or more visually attractive or engaging or whatever? Then I keep that particular feature. But if that particular feature doesn’t fulfill any of these purposes, it doesn’t make our graphic more understandable or more attractive, more visually striking or whatever, then it doesn’t really have a purpose at all and therefore you can remove it. That is what that principle of simplicity ends up being useful for in data visualization.
00:40:11
But again, can we create general principles of graphic simplicity? I don’t think that we really can. Every graphic is its own story, every graphic requires its own assessment because you need to take into consideration not only what you want to communicate, but also you need to take into consideration the nature of the data. Some data needs more detail in order to be represented accurately, some data can be represented at higher levels of aggregation, therefore the graphic can be simpler, right? You need to take into consideration constraints. For example, the size of the platform where the graphic is going to be displayed, it’s not the same thing to create a graphic to be shown on a computer screen than creating the same graphic for a mobile phone. That’s a constraint. And you also need to take into consideration the audience, the content, the purpose, the intent, the insights that you want to highlight. All those things need to be mixed up together in order to make the right choices, or I would say not the right choices, but appropriate choices depending on what you want to achieve.
Jon Krohn: 00:41:12
Yeah, it sounds like there’s a lot of specificity related to exactly what you’re trying to achieve, the specific story that you’re trying to tell, the particular kind of visualization you’re working with. But overall, the idea is to remove elements that are not useful to whatever you’re trying to convey.
Alberto Cairo: 00:41:28
Or to add elements that may be important to make the story more understandable. It’s not only about reduction, sometimes it’s about augmentation.
Jon Krohn: 00:41:36
That makes sense. Are there situations, are there prominent examples that you’re aware of where brevity has gone too far?
Alberto Cairo: 00:41:43
Sure. I mean, many graphics that, for example, don’t include annotations when they should have included annotations.
Jon Krohn: 00:41:49
Right.
Alberto Cairo: 00:41:50
Why do we have, you have a line graph and suddenly you see a sudden spike in the line and you don’t explain that spike? Readers will immediately feel prompted to ask, “What is going on there? Tell me more. What is going on here?” Right? I have plenty of examples of that type of graphic in my previous book, How Charts Lie, in which graphics that were not designed with the intention of misleading anybody end up misleading somebody or at least part of the public because they tried to reduce the amount of information too much.
Jon Krohn: 00:42:27
Very cool. So we’ve just been talking now for a little while about norms and convention breaking, and related to that perhaps is this idea that you cover in your book of using data visualizations to make a social impact. So not just to break conventions in the art form itself, but to try to break conventions in society. So you mentioned, for example, the emergence of feminist discourse in data visualization focused on highlighting and addressing inequalities related to gender, race, ethnicity, disability status, and so on. In your view, what is the responsibility of data visualizers and journalists in general when shedding light on these kinds of systemic biases?
Alberto Cairo: 00:43:15
Well, I guess that it greatly depends on what you use visualization for, what the context of the visualization is. Obviously, if you are going to design let’s say dashboards within a company to analyze company data, perhaps these matters are not as relevant to you as they would be to a journalist who is trying to inform the public about particular issues.
Jon Krohn: 00:43:39
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00:44:21
It is interesting though that you say that because it could actually end up being the case, and this is just something that had popped into my head, but there actually is. Potentially there are even opportunities there where a dashboard designer in a corporate setting by just choosing to put in a toggle to break down some piece of data by gender or ethnicity, or maybe historically the managers in that company aren’t looking out for that, or maybe you could even make the decision to be even bolder and have it be the default view when the person opens up the dashboard, so yeah-
Alberto Cairo: 00:44:58
Yep. There are issues in all types of visualizations. What I was trying to perhaps convey is that there are degrees of importance, right? To me, it’s much more important than that people who are going to put visualizations in the open to be presented to the public, that they pay much more attention to these types of matters than if you’re going to design a dashboard to be consumed by 10 managers within the company, but it’s still important. For example, just going to the general thing, there is a specific example in the book about a survey that asks the news organizations about the gender composition of their workforces, and they just had two options to report the gender of their workforce, male or female, that’s it. Men or women, that’s it. Then binary, right? Why don’t you have an option in the survey for non-binary people? Right? So the non-binary people exist whether you believe it or not, they do exist, and therefore there needs to be other options in the survey that people can check.
00:46:01
So that’s a limitation of the data, and I point that out in the book itself. And that’s something, by the way, that the designers of this survey have addressed in more recent editions of that particular survey, and good for them, right? So they learn through the process because we need to consider not only the fact that we are counting things, but also who is being counted and who is not being counted, and how are they being counted. So yeah, these issues permeate in everything I think, in data visualization to different degrees of importance. I cannot speak with any expertise on this matter.
00:46:38
So I would point out, I would just mention some people whom I mention in the book, like Catherine D’Ignazio, Lauren Klein, who wrote Data Feminism. Heather Krause, who has a website called datassist.com, and she thinks very deeply about ethical matters related to data science statistics, but also data visualization. It’s an area that I’m becoming growingly interested in. What types of inequities we insert in our visualizations? Sometimes with the best of intentions, we are just not aware of those, right? And perhaps we should be more aware of them.
Jon Krohn: 00:47:18
I thought that a particularly interesting example of this social impact came up in chapter seven talking about the work of, again, I might butcher her name here but you can correct me, Federica Fragapane?
Alberto Cairo: 00:47:29
Yep.
Jon Krohn: 00:47:30
And so when discussing her work, you distinguish between seeing immigrants as statistical aggregates versus seeing them as individuals.
Alberto Cairo: 00:47:40
Individuals, yes.
Jon Krohn: 00:47:41
So yeah, so how can we use data visualizations to combat dehumanization? Just seeing people as numbers.
Alberto Cairo: 00:47:49
Yeah. Show the people, show people. That’s what she does in that visualization in particular that you’re referring to. So this visualization is about immigration, and particularly not just immigration, but refugees and people who move from war-torn countries into Europe, for example, right? So to tell that story, obviously you can, and we should show the data, the aggregate, the total numbers, the sheer amount of people, whatever. But if we want to really create a well-rounded story about the human tragedy of migration through the Mediterranean, many people die trying to cross the Mediterranean Sea from Africa to Europe.
00:48:34
It is useful not only to show the aggregates, the total numbers, but also to show specific stories so people can connect, the reader can connect to these migrants, and that’s what she try to do in this project that we are referring to. Not just showing the total number of migrants, but actually focusing on the path of, I don’t remember, seven or 10 migrants. And she traced those paths so you can see the incredible amount, the incredible distances that these people had to travel in order to get to Europe. It’s a wonderful project I think.
Jon Krohn: 00:49:12
Yeah, this sounds like a really great way to show it. It’s wild how people, practicing data scientists like our listeners, it’s so easy to, even as you’re making data visualizations or building a machine learning model of the data that you’re working with, it becomes so easy to be detached and cold about what we’re doing, and so-
Alberto Cairo: 00:49:35
And we sometimes need to do that, don’t get me wrong. It’s just like it all depends on what level of aggregation we are analyzing. So sometimes we need to do that, we need to detach ourselves from the individual people behind the data and represent those aggregates because that gives us a different picture that we focus on the individuals, but we should never forget about the individuals. And if we want not only to analyze the data at hand, but also to communicate the insights extracted from the data, then it is useful to communicate about the different levels of aggregation in which the data can be presented, the general statistics down to specific cases like a random sample perhaps of the individuals that are being depicted in the data and then tell their story somehow.
00:50:26
That’s how you create a well-rounded story with this combination of the general and the specific. There is an interview, by the way, with Lena Groeger from ProPublica, and she talks about what in ProPublica, which is one of the gold standards in my opinion when it comes to data journalism, what they call the far and the near. The far is the general, right? The big dataset. And then the near is the specific. So we need both if we want to tell a well-rounded story, particularly about stories that are of human interest.
Jon Krohn: 00:51:00
It popped into my head that although we’ve been discussing going onto the individual level for the purposes of not dehumanizing people, it would actually also be in say a corporate setting, it is probably a useful way of conveying some point that you’re trying to make in aggregate as well. So going into a case study of, I don’t know, a particular user going through [inaudible 00:51:26] platform or yeah.
Alberto Cairo: 00:51:26
Yeah, your customers. You can create a random sample of specific customers that have communicated with you and tell you their stories. It’s like, what are the stories? Who are the people behind those aggregates that you’re presenting? Show me some examples. Exemplify the data, make the data a little bit more approachable, something that I can humanly connect with. So even if these types of settings, this type of approach might be useful.
Jon Krohn: 00:51:52
Yeah. As social animals, I think we’re hardwired to appreciate and remember those kinds of examples more strongly than maybe the general trend conveyed by a chart. Anyway, back to social impact. Your book celebrates visual practices from different cultures and disciplines around the world. How does a diversity of backgrounds among visualization designers enrich the field?
Alberto Cairo: 00:52:17
Well, in many different ways, I think, I mean, some people whom I interviewed for the book come from a background in architecture. Some of them come from backgrounds in these biological sciences, some of them are academics, some of them are scholars. And that permeates their work a little bit and that transforms the field into a very, very interesting creature because the conversation, and I talk a lot about conversations in the book, the conversation among people who have different types of backgrounds, not only data scientists, but humanists, for example. We use data visualization to display cultural data, historical data or whatever. It’s like conversations of these type of people with people who come from fields that are more STEM-oriented from mathematics, or that makes for fascinating conversations and we can learn from each other a lot. I think that that is another point that I’m making in the book that different approaches to data visualization are not mutually exclusive.
00:53:24
They are, and they should keep being in constant conversation with each other and try to cross-pollinate in some sense. It’s like we can borrow ideas from… That was another purpose of the book, to expose a reader to this broad variety of people so everybody can get something useful from some of the chapters. Some of the chapters you would like more, some of them you would like less, but I feel that at least from let’s say one quarter or one-third of the chapters, you may get some interesting insights as you can end up applying to your own work.
Jon Krohn: 00:53:59
Related to these topics of social impact. You yourself, when you were working at El Mundo, which is one of the largest printed newspapers in Spain, you covered challenging events like September 11th attacks in the US, the 2004 Madrid train bombings. How do you approach conveying complex and sensitive information in these high-profile news events?
Alberto Cairo: 00:54:25
So in the case of the bombings of March, 2004 in Madrid, the way that we did it in terms of data visualization was to try to design visualizations that were not very let’s say eye-catching. We designed visualizations that are very bare, very subtle in terms of color because the events themselves were dramatic enough and shocking enough, we didn’t need to beautify or make them more striking than they already were. So we try to just represent the information as we gather it, be it through maps, through very simple diagrams, showing where the bombs have been placed by the terrorists. And then the other thing, which is something that I also encourage people to do to this day when we want to communicate visually, we didn’t rely just on the visualizations. The visualizations were just one of the many elements of a broader package, of a broader story that included a textual narrative, that included videos, that included photographs, all of them organically combined into a package with the visualizations being just one of the elements in it.
Jon Krohn: 00:55:39
Yeah. While to have been covering those kinds of events, and more recently, we’ve had the war in Ukraine. And in chapter 22 of your book, you talk about Anatoly Bondarenko and data visualizations that he has. Yeah, I don’t know if you want to highlight some of this work and how-
Alberto Cairo: 00:56:04
Yep. So Anatoly is a friend of mine. We have been friends for many, many years, and I begin the chapter with a sudden realization one day, which I say, I told myself I remember him, I said, “My friend is at war because he’s at war. He’s part of the Ukrainian army at the moment as a captain”. And I talk about that in the chapter. I keep in touch with him, I send him an email to see how everything is going, if he’s doing well. But I talk to many extraordinary people for the book so I should really not highlight one person over the others. But there’s a reason why I put Anatoly at the very end of the book because in my opinion, his work and only his work, the work of the organization that he works for, Texty, their website is texty.org.ua, the work that they make over there is they are essentially like the gold standard.
00:57:09
I mentioned ProPublica before as being the gold standard when it comes to data journalism in the United States. Well, Texty to me is like the gold standard for data journalism when I think about Eastern Europe or Europe in general. It’s essentially a nonprofit, a small nonprofit that appeared in Ukraine years ago. And I tell the story of how it appeared in The Art of Insight in the book, and they produced [inaudible 00:57:37] this incredible data visualization work and they are so creative and so thorough. It’s so precise in the way that they present stories to their readers. And the visualizations that they create, they are comparable in quality to what you can see in The New York Times, for example, or The Washington Post.
00:57:57
And this is even more amazing if you consider that The Washington Post or The New York Times, they have dozens of people in their graphics teams, and Texty is like seven people, eight people, I don’t remember. But still, I mean, their work is incredible. So it really shows you that all that we need to create excellent work in data journalism and data visualization is the will to create it. Some resources, obviously we need to have some resources to pay salaries and stuff, but it’s like the, I don’t know, it’s like creativity and then the energy and the passion to communicate ideas to others in a truthful manner. Ideas that matter and ideas that you think may be useful to the public that you’re trying to inform, that’s all that matters in data journalism in particular.
Jon Krohn: 00:58:55
Nice. Yeah, a powerful story there. And very cool how that small team can create thorough, creative and powerful visualizations of the Ukraine war on par with those huge organizations like The Times. So looking ahead, hopefully into an even more peaceful time in the future, let’s talk about the future of visualization. So in chapter 21, you introduce Simon Ducroquet, who’s a versatile graphics reporter for The Washington Post that you just happened to mention. And he’s experimented with virtual and augmented reality in his visualizations. So as AI and extended reality, XR technologies like virtual and augmented realities become more powerful, do you think visualization will transcend the static and the observational and become increasingly exploratory and interactive?
Alberto Cairo: 00:59:53
I don’t know. I guess that everything in visualization, it will greatly depend on what we want to achieve with it. I mean, sometimes a non-interactive, completely static graphic that is very well-polished or very well-designed tells the story, and then you don’t need any interaction or virtually a reality gimmick in order to make the visualization more effective, you just don’t need it. You just tell the story with [inaudible 01:00:18]. But in some cases, applying immersive technologies may be helpful. I mean, I’ve seen examples, for example, of scientific visualizations of let’s say the human heart, a high resolution 3D depiction of a human heart represented in virtual reality that lets you walk inside a living human heart. How amazing is that? It’s incredible, right? But in that case, the technology serves a purpose so you can explore the heart, the living heart from the inside.
01:00:48
That’s the purpose, and it really achieves it. You cannot create the same experience with a static visualization. So yeah, if it serves a purpose, yes, by all means, I mean these technologies will serve a purpose. When it comes to artificial intelligence, that’s way above my pay grade. I honestly don’t know where we are going. I don’t know. I fear, I am a little bit worried about any prediction. The way that I’m using for now, I’m using GPTs to generate code just because I’m super lazy and I don’t want to write code, and I prefer to use natural language, right? Speed up the code to create this type of graphic with this type of variables, whatever. And then that generates the R code that I can copy, paste and then edit because it always needs editing to generate my own visualizations. But I’m very aware that this is a very limited and probably short-sighted way of using all these technologies. I don’t know, time will tell. I don’t know.
Jon Krohn: 01:01:47
Yeah. In my experience for sure, and I’ve had an episode on this before in the ChatGPT interface provided by OpenAI, they have a compiler so that you can be running code right there. So you can provide say a CSV file of your data, and you can just provide natural language instructions. In fact, you could make it extremely vague to start. You don’t even need to have-
Alberto Cairo: 01:02:13
And I absolutely love that because I don’t like to code, I really don’t like to code. I mean, I use R quite a bit and I love R, don’t get me wrong. R is a great technology, but it doesn’t feel human. That’s not how humans communicate with each other. So all these technologies are great for someone like me who prefers to have a conversation, in this case with a machine, trying to describe to the machine what is it that I want to achieve? And then fine-tune all the details, “Oh, I don’t really like this axis. Can you change the color of that to these?” And then you see the results immediately, how wonderful is that? I mean, that’s great. So I see all these technologies, at least for now, as an extension and a less abstract way of interacting with the technology.
Jon Krohn: 01:03:02
Yeah. So for listeners back in episode number 708, I talked about the ChatGPT. At that time it was called their code interpreter, and now it’s called advanced data analytics. But I guess they gave it that name because most people were using it for data analytics. But in fact, that undersells the huge breadth of what you can do with this. Being able to upload files and ask it to generate Python code to run on whatever you upload, or you wouldn’t even need to upload anything, but it runs right there in the browser, produces results. And so you could upload a CSV of some data and you could say, visualize all these columns for me, and it will figure out an appropriate visualization. It’ll take a guess, and often quite a good guess at what kinds of visualizations to create for each of the individual data points.
01:03:55
It might even automatically, without you asking, start doing plots, comparing multiple of your columns at the same time. And yeah, so that kind of thing, definitely exciting. And I haven’t played around with this myself. In that episode number 708, I do a hands-on code demo so that if watch the YouTube version, you can actually see the code running. And I provide five hacks for data scientists, but something that it didn’t occur to me to do in there was to try to be creative with the visualization. So to try to see, I mean, you’re basically you’re piggybacking on the intelligence of tons of designers, the kinds of people that you interviewed in your book, people who’ve the bigger the impact that an individual has had, the more likely their kinds of principles or ideas will make its way into the training data for GPT-4 in this case. And yeah, we’ll have an impact on the kinds of creativity that you could have with these visualizations. So that’s-
Alberto Cairo: 01:04:55
It speeds up the work that I think that all, again, these are augmentation technologies. They just make the process of designing graphics and the analysis perhaps faster, more effective, but you still need the human being in there to intervene and edit and make choices with the technology.
Jon Krohn: 01:05:12
For sure. Yeah. At least at this time, the human being still has the best idea of the context and the message that we’re trying to convey to a particular audience, for sure. You have a lot of history pushing the boundaries of what we do with technology. In the early 2000s at El Mundo, that same Spanish newspaper we were talking about earlier, you experimented with emerging tools like Macromedia Flash co-producing-
Alberto Cairo: 01:05:36
Those were the times.
Jon Krohn: 01:05:39
… yeah. So yeah, you’ve been experimenting with interactive graphics 20 years ago. And then 10 years ago in 2012, you created the first MOOC, Massive Open Online Course in Journalism, so that was called Introduction to Infographics and Data Visualization. Yeah. We’re talking about the future and how things are changing, do you think that online platforms for journalism, data visualization education will make a bigger and bigger impact as years gone?
Alberto Cairo: 01:06:12
Yeah, sure. And one of my interests these days actually spreading the world about data science and data literacy and scientific reasoning, and we need to take advantage of the technologies at hand, the technologies that are available or perhaps not even technologies. I mean, how would you bring all these skills to the classroom? And when I say the classroom, I’m not referring to the university’s classroom, but to K-12 education. It’s like why do we teach our kids arithmetic and algebra and calculus, but we don’t teach them statistics? And when I say statistics, I’m not referring to the statistical calculations of averages, measures of central tendency, whatever, but the reasoning behind all that.
01:07:02
Why are those useful? What they are useful for and how to apply them to their lives and how we can show children that all these calculations and all these methodologies may be applicable not only to traditional types of statistics like life expectancy and poverty and things like that, but to things that they really care about, such as cultural data is like data related to music, data related to movies and so on and so forth. So how do we get a level playing field when it comes to data science? How we bring not only the experts up to speed, but the rest of the world, the rest of people up to speed with all these technologies to provide these elementary but still workable understanding of what data science and data visualization entail.
Jon Krohn: 01:07:55
Yeah. Digital education around the globe, particularly paired with some generative AI tool for answering questions in the flow.
Alberto Cairo: 01:08:04
Yeah, these types of technologies will lower the barrier of entry if you will learn how to use them well because again, if you want to create a bit, again, this is wonderful for people like me. It’s like I don’t like to code, and most people don’t like to code. We prefer to use natural language so we can teach people how to reason better with the data with the help of all these new technologies. I think that that’s wonderful.
Jon Krohn: 01:08:26
Yeah. In episode 730, I had the CTO of GitHub on the show and he was talking about how GitHub Copilot is so useful for coders because it’s right in the flow and it didn’t occur to me at that time, this idea of flow, like Mihaly’s idea of flow-
Alberto Cairo: 01:08:46
Csikszenmihalyi.
Jon Krohn: 01:08:46
… yeah, and then takes it to a new level of depth when he talked about these tools allowing you to stay in the flow. Now I’ll really be thinking about it, is that deep state, that meditative state that these tools facilitate making those kinds of things more enjoyable. So at the University of Miami, you are the director of the Center for Computational Science Visualization Program and so you’re really at the forefront of how computing can be changing data journalism and information design. Is there anything else you’d like to add for our audience on how emerging practices or technologies could shape this field in years to come?
Alberto Cairo: 01:09:30
Yeah. Paradoxically, I would ask people to think a little bit less about the new thing and a little bit more about how we can use more effectively the technologies and knowledge that we already have. So yeah, I’m super excited about the possibilities of artificial intelligence for what we do. Absolutely. I want to embrace that, I want to experiment with that, but there’s still some base knowledge that we need to convey to people. It’s like people out there still don’t know. And when I say people, I refer to a substantial portion of the population, still don’t know how to interpret a scatterplot correctly. So that’s what we need to teach. We need to focus a little bit more on that, not so much on developing new high-end technologies, but again, on bringing everybody else up to speed with elementary knowledge about all these matters. I think that I would ask people to focus a little bit more on that.
Jon Krohn: 01:10:26
Very nice, great answer. That is certainly not what I was anticipating, but I love it. It makes a lot of sense. So very exciting. I can’t recommend this book enough. The Art of Insight, it came out just a few weeks ago. And yeah, people should check it out. Published by Wiley and certainly available in the US. A few other countries I checked, it was available there as well. In fact, that also, that reminds me, I should have said this right at the beginning of the episode. I always forget to do that, but we’ll of course be happy to give away copies of the book. So I will be on my personal LinkedIn account on the day that this episode is released. I will of course make a post about this episode and then anybody who would like to provide some feedback on the episode, positive or negative, any comments or re-shares of my post, the top 10 responses or comments that people provide, I will be happy to ship you a copy of Alberto’s new book, The Art of Insight.
01:11:41
And so yeah, so that should be fun. And I should say that you’ve got to be somewhere where I can easily ship it to you. So basically, if there’s an Amazon store in your country where the book isn’t going to have to go over a border, that creates lots of headaches for us. But we’d love to get you a physical copy of Alberto’s book. So Alberto, beyond The Art of Insight or your own books, people who are watching the YouTube version of this will see that you have hundreds of books surrounding you. Are you able to pick just one to recommend to our audience today?
Alberto Cairo: 01:12:15
Yes. I mean, I think that I would pick, actually, if you give me permission to do that, two or three maybe.
Jon Krohn: 01:12:22
Of course. Of course, yeah.
Alberto Cairo: 01:12:22
Because it’s hard for me to just pick one. So very recent books related to data visualization. I am a big fan of a book that was published on the very same day as The Art of Insight. The title is Practical Charts and the author is Nick Desbarats. So Practical Charts is the title. The book says what the book is about. It’s an extremely practical book on elementary data visualizations, how to make decisions on visualization design, particularly specifically focused on traditional types of visualizations. There’s nothing super creative in the type of charts that Nick showcases in the book. It’s all about bar graphs, line graphs, scatterplots, and so on and so forth. But the book provides a very systematic approach to decision-making, and that’s something that I really like because I am not that systematic in my approach.
01:13:19
And I usually take very winding paths to designing my own visualizations, but Nick is much more organized, systematic in giving advice to people on how to design better graphics. So that will be one of my choices. Another choice, well, we mentioned Data Feminism before. That’s another book that I would recommend to people to make people think more deeply and more critically about what we do when we design these types of products. And then finally, I think that, and I may disclose this upfront that I get a small, tiny financial interest because I get a small portion of the royalties of the book that I’m trying to recommend. It’s not a lot of money, it’s just a very, very small amount of money and it’s a great book, honestly. I recently edited Joyful Infographics by Nigel Holmes, who is a long time infographics designer, has been in this field for many, many, many decades. And I was lucky enough to edit his most recent book, Joyful Infographics, which is all about humor in data visualization. The role of gentle humor in data visualization is truly delightful, truly delightful. I will strongly recommend it.
Jon Krohn: 01:14:36
Very nice. Great recommendations. Thank you so much for this amazing episode, Alberto. We knew you would be great. I’m glad that we’ve been creeping your career and finding the perfect moment to ask you to be on the Super Data Science Podcast. Thank you so much for coming on. How should listeners follow you after today’s episode?
Alberto Cairo: 01:14:57
Well, first of all, thank you so much for the kind words. This has been a delightful conversation. It’s very easy to find me. I am on LinkedIn, I am on Bluesky, I am on Mastodon. Those are the platforms that I’m currently using at the moment. I have abandoned Twitter. I used to be very active on Twitter, but for different reasons I stopped using it. So the best places to go are LinkedIn and Bluesky.
Jon Krohn: 01:15:24
Nice. We will be sure to include links.
Alberto Cairo: 01:15:26
And my website, I forgot to mention I also have that.
Jon Krohn: 01:15:29
Oh, yeah. That’s right.
Alberto Cairo: 01:15:30
Yeah, a personal website, which is my name and my last name, albertocairo.com. And then my web blog that I try to update every now and then, and it’s the title of my first book, thefunctionalart.com. Those are the places to go.
Jon Krohn: 01:15:45
Yeah, very nice. Yeah. We’ll be sure to include links to your LinkedIn, Bluesky, Mastodon and website URLs in the show notes. Thank you so much, Alberto.
Alberto Cairo: 01:15:57
Thank you so much.
Jon Krohn: 01:15:59
It was so great having you on the show, I learned so much. I really enjoyed it. It’s great to be able to have someone of your prestige and knowledge on the program to learn from things.
Alberto Cairo: 01:16:06
Likewise, Jon. Thank you so much.
Jon Krohn: 01:16:13
What a cool and deeply interesting individual. In today’s episode, Alberto filled us in on how like teaching data visualization forces you to understand. He talked about how designing data visualizations facilitates a meditation-like flow state as Mihaly Csikszentmihalyi describes it. He talked about how balancing the science of visualization with one’s personal style, removing any elements that don’t add value, and adding helpful elements like annotations are the keys to communicating data effectively. He talked about how featuring people as individuals can prevent statistics from becoming dehumanizing, how generative AI will continue to make coding of graphics easier and more natural, and how extended reality and AI could make visualizations more and more interactive in the coming years. But static graphics will often still be the most powerful option. As always, you can get all the show notes including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Alberto’s social media profiles, as well as my own at www.superdatascience.com/741.
01:17:12
Thanks to my colleagues at Nebula for supporting me while I create content like this Super Data Science episode for you. And thanks of course to Ivana, Mario, Natalie, Serg, Sylvia, Zara, and Kirill on the Super Data Science team for producing another enriching episode for us today. For enabling that super team to create this free podcast for you, we’re so very grateful to our sponsors. You can support the show by checking out our sponsor’s links, which are in the show notes. If you don’t want to do that, then you can support the show by sharing, by reviewing, or by subscribing, just by letting people know about the show. But if you don’t want to do any of that, just keep on listening. That’s the most important thing to us, for sure. We love having you tune in, I’m so grateful to have you listening, and I hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking it out there, and I’m looking forward to enjoying another round of the Super Data Science Podcast with you very soon.
