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Alberto Cairo Unveils 'The Art of Insight': Evolution, Experiences, and Challenges in Data Visualization
931 Plays9 months ago
In this week’s episode of the podcast, I welcome author, speaker, and professor Alberto Cairo to the show. We discuss his most recent book, The Art of Insight, and our conversation extends to acquiring reliable data and challenges people across the world face in creating useful and accessible data visualizations. We also discuss the current state of social media as it relates to the data visualization community and Alberto contemplates the future landscape for both the community and data-related conferences in a post-pandemic world.
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Transcript
Introduction to MICA's Online Master's in Data Analytics
00:00:00
Speaker
This episode of the PolicyViz podcast is brought to you by the Maryland Institute College of Art.
00:00:05
Speaker
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Speaker
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Speaker
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Interview with Alberto Cairo on 'The Art of Insight'
00:01:28
Speaker
Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. On this week's episode of the show, I chat with the one and only Alberto Cairo, author of the new book, The Art of Insight, how great visualization designers think. And so Alberto and I talk about his process of writing the book and how he went about
00:01:48
Speaker
identifying and thinking about who he wanted to talk to and why. We talk about whether he believes in rigid rules to data visualization and we talk about my two favorite things about
Challenges in Qualitative Data Visualization
00:02:00
Speaker
the book. The first is on qualitative data visualization and the challenge with getting good qualitative data and how if you're not used to collecting qualitative data, how you might actually go about do that. As you probably know, Alberto is a former journalist so he has a lot of things to say about actually talking to people.
00:02:18
Speaker
And the other thing that we spend a lot of time talking about and what I found really interesting in the book was the possible I'd say possible lack of data visualization outside the US and outside Europe and we talk about why that might be and how different areas of the world might
00:02:33
Speaker
increase or improve their data visualizations.
Global Perspectives and Regional Challenges in Data Visualization
00:02:37
Speaker
So if you're working in the data visualization field outside the U.S., outside Europe, I'd be curious to hear what your challenges are and how you are creating better data visualizations. And so you can, of course, reach out to me on policyvis.com or Twitter, LinkedIn or Instagram. So having said that, let's take a listen to this week's episode of the Policy Viz podcast with Alberto Chaira.
Alberto Cairo's Literary Journey and Inspirations
00:03:03
Speaker
Oh my goodness, Alberto Cairo. What a pleasure. Good to see you again, friend. It's been a while. Hey John, long time no see. Thank you for having me. Of course. A book number, English book number four. English book number four, yes. But total books number five or six.
00:03:22
Speaker
number six, because we would need to know number seven. Well, yeah, we will need to count the first book that I ever wrote, which has nothing to do with visualization. That was in my early 20s.
00:03:37
Speaker
So is the Alberto Cairo box set? The collected series. The collected series with a special cover on it. It's got a special booklet that you get. In a steel box. That's right. That's right. That's right.
00:03:58
Speaker
Well, it's great to have you on again. I love the new book, Art of Insight. So this book is, I'd say, quite different than your other books. It absolutely is, yes. And I want to dive into all the kind of different pieces of it.
Personal Insights and Delays in Writing 'The Art of Insight'
00:04:12
Speaker
I think my first question is, was this book more fun for you to write than the other books?
00:04:18
Speaker
It was more fun in the sense that it's the most personal book that I've written to date, I would say. So in that sense, yes, it was a lot of fun. It was also a lot of fun because I got to learn a lot and talk to people whose work I admire, which is always great to bring some inspiration and re-energize yourself. So in that sense, yeah, it was a lot of fun. But
00:04:42
Speaker
It was a bit of a struggle to write because due to personal reasons and life changes, I've been pushing this book. I mean, the book has been delayed for more than two years. So Wiley, my publisher, was extremely generous in giving me
00:05:01
Speaker
sort of like flexible deadlines and accepting my constant delays. And so it was a little bit of a struggle to get it done. But once I was able to sit down and actually get it done,
00:05:12
Speaker
It was a, it was a huge pleasure. Yeah, for sure. Yeah. What was your process
Informal Interview Processes in Book Research
00:05:17
Speaker
like? So, so you interviewed around two dozen or so plus I would guess many others that maybe aren't in the book, but designers and developers and however we want to call whatever we call data visualization folks these days. Did you transcribe all the recordings and go back through them? Like, I mean, you're a former journalist, so this is probably kind of second nature for you, but what's your process like?
00:05:40
Speaker
Yeah, yeah. So well, first of all, I needed to come up with a list of people I wanted to talk to. And as I mentioned in the in the introduction to the book, that has no any system to it. It was like, I just wrote down tons of names of people whose work I like, I like, and I was interested in talking to about visualization. And in no particular order,
00:06:03
Speaker
And I came up with a very long list, like 50 or 60 people. I talked to more people than appear in the book. I would need to think about what to do with those conversations. I even thought about doing a follow-up volume, like a second part of the out of inside and then more conversations and stuff, just because those conversations were equally enjoyable. But I had a limited number of pages, so I needed to choose at the end what to include
00:06:32
Speaker
But again, it's not, as I said in the introduction, it's not a systematic, it's not a representative sample of anything other than my own thinking and my own preferences. So I first of all came up with a list and I contacted people and then all conversations, I would not call them interviews because they were not really interviews. I didn't have a particular set of questions that I had for people. I just wanted people to talk to me about their work and what
00:07:02
Speaker
They felt passionate about and about their thinking process. I mean, you've read the book, so you know this already, but the book is not really about the work. It's really not a process book. My project is how I make it. It's more about the
Evolution of Data Visualization Philosophy
00:07:19
Speaker
people who are behind the work. That was what I was really interested in. It's like, who are these people? What gets them excited?
00:07:28
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What motivated them to get into visualization? What motivates them today? What are their ideologies, worldviews, philosophies, passions, or fears? And I was interested in all that because I think that life permeates our work as much as work permeates our life. And that is the focus of the book. So yeah, I had all these conversations. I recorded them all. Then I had them transcribed. I didn't transcribe them myself.
00:07:55
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That's a lot of work. So I hired a professional to transcribe the conversations.
00:08:01
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Then I wrote the conversation, so obviously there's a lot of editing involved in that, because in some cases, conversations can be a little bit rumbling, and so you need to give them a proper shape. And then I gave everyone the opportunity, which is not common practice in journalism. In journalism, you usually don't do this, but this is not a journalistic work. I wanted the people in the book to be happy with their own words. So I gave everybody the opportunity to read
00:08:29
Speaker
my take over their words and then help me with the editing so people properly represented in there by the words that appear in the you will never do that if you're working in a newspaper but you know this is a book this is my book and I do whatever the hell I want with it that's right that's right
00:08:48
Speaker
So you mentioned that this book is not really about the process. It's not about how did a person go from step A to step B to step C to get this thing online. In my view of the DataViz library, the books are kind of moving from the how-to books, the best practices book. You've written a couple of those. I've written a couple of those. They seem now we've got more books on process, like Vidget Settler's book and Jenny Christensen's book. We've got different types of data coming out.
00:09:17
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there's the Making with Data book and Deamer Offenhuber's book is out on data fiscalization. Do you see that evolution in the, I guess the library of data visualization books? And is that, do you think that, I mean, you talk a lot about philosophy in the book. So like, do you see that as a natural evolution of a field as it matures?
00:09:36
Speaker
I think that the evolution is not so much a linear process. It's more a diversification process. It's like we, instead of having sort of like a linear sequence of types of books, in the 80s, we had Tufti, in the 90s, we have these, in the 2000s, we have that. It's more that there's a broader spectrum of types of books that we have today. Obviously, we still have the basic principle type of book.
00:10:03
Speaker
It's like we have practical charts by Nick Desborat recently, which is an excellent, it's an excellent basics book. And there is always going to be a need for books that remind the community about some basics of how we do things and why we do things the way we do them. But there's also books about history. There's also books about sort of like a meta reasoning about visualization, philosophy visualization.
00:10:30
Speaker
And then books about the people who create visualization, like the art of insights. Again, not focused on the process, but more focused on the people. So I think that that's the evolution. It's like these sort of more diverse spectrum of types of books. Yeah. You also, and anyone who knows your work isn't going to be surprised by this, but you also spend time throughout the book
00:10:54
Speaker
refuting this idea that there are rigid rules. You should do this, you should do that, you should do this or not do that. Are there any rules you think that practitioners should follow or is it all bend them and then break them sort of things? The way that I explain this in the book is not that there are no rules. What I say is that there are no rules that are universal.
00:11:17
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It's like rules or ways or heuristics or ways to behave and ways to act are greatly dependent on the context, are greatly depending on the goals, are greatly depending on what you want to do. There are certain, I would say, very general principles, if you want to call them, I would say. For example, I want visualization to be a truthful endeavor, right? We should always strive to represent
00:11:44
Speaker
our best understanding of what the truth is, which may differ. I mean, people may approach the same data in different ways, and that's perfectly fine, or interpret a dataset and represent the dataset consequently in different ways just because we interpret it differently. So there's even not a ruling there other than try to do your best. But what I explained in the book is that what really matters is that perhaps what we should do is to stop thinking of visualization in terms of principles and rules,
00:12:14
Speaker
And so I was thinking about visualization or more broadly, the data analysis process as more of a sort of like a reasoning process. It's like you need to give yourself and give others reasons that are sound and that can be understood by others. And you need to be able to rationally and logically justify the reasons that took you to make a particular thing or to do a particular thing in a particular context.
00:12:41
Speaker
and you should be able to have that conversation. So I would say that that's the general principle. It's like base your decisions on that type of reasoning. I'm guessing that's how you approach your teaching, that the theme or the thread through your classes is it's about reasoning. It's not about step A, step B, or even if it's not enough linear work. Yeah, even when I explain basic stuff, such as, for example, why is it advisable
00:13:08
Speaker
that bar graphs start at zero, right? I can't explain why. I mean, I can't explain the reasons behind that. And I try to walk students through that reasoning. And
00:13:23
Speaker
And whenever I need to grade students, I don't like grading. Whenever I need to give feedback to students, when I ask, what I ask students to do is to be able to provide reasons for every choice that they make. Why do you use this particular typography or why do you use this particular color or why are graphics arranged in this particular way? I may disagree with the reasons given for that particular choice, but at least we can establish a conversation.
00:13:50
Speaker
and they can give me those reasons, then I can give them back my reasons, and then we may be able to reach a consensus or not. They may see whether my reasons have any merit to them or not, and then they may follow my reasons, or they may stick to the reasons. It's all about the conversation at the end, and this conversation can be based on
00:14:12
Speaker
As I explained the other inside, it can be based obviously on experience. It can be based on what you have observed that works or not throughout the years. It can be based on a growing body of empirical evidence that we can all use and draw from. But in some cases, it can be just based on taste and many decisions are based on taste and that's perfectly fine as long as you make that clear and straightforward.
00:14:37
Speaker
Back to the point about the evolution of books. Do you think that's part of the evolution of the field? I mean, in like the modern, the modern sort of work on data visualization, we have, you know, kind of the tufty few camp of rigid rules that you've written about in the past about.
00:14:54
Speaker
how they are not based on anything more than preference. And now it seems we've moved towards this more of a reasoning, a logic. There's some aesthetic decisions that are kind of, you know, more embraced by the field rather than these rigid rules. Is that, do you think part of the evolution of the field or the diversity? Yeah, it's a sign of maturation. We are reaching a point of higher or deeper maturity, which is a good thing. Yeah.
00:15:22
Speaker
The other thing that I found really interesting, so there are two, for me, two threads in the book that I found really, really interesting. So first is on qualitative data viz. You discuss it several places in the book. Many of the people you talk to do a lot of really incredible stuff with qualitative data viz. I'm curious whether you think qualitative data viz has kind of gotten a short straw in the field in terms of instruction and practices and approaches.
00:15:52
Speaker
If so, like, how do we how do we deal with that? Well, we do. I mean, it has not been as as covered or as deeply covered as quantitative visualization simply because it is harder.
00:16:06
Speaker
to teach it systematically. When we teach quantitative data visualization, it's easy to teach it systematically because you can talk about the grammar of graphics. You have a set of numbers, but data is not always numbers, obviously, but let's speak loosely here. So you have a set of numbers or quantities.
00:16:24
Speaker
Then you have a set of objects and there are certain grammatical principles that teach you how to map those numbers onto those objects and then vary certain properties of those objects in proportion to the numbers that you're representing, which is the core of the grammar of graphics. So that is very easy to systematize. But how do you systematize the teaching of, let's say, I don't know, Jaime Serra's work. Jaime is one of the designers that I showcase.
00:16:51
Speaker
in the book who is famous for producing these beautiful illustration-driven visual explanations. So how do you systematize that? You really can't. You can talk about vague heuristics and principles of composition and organization of information, but you cannot systematize it as deeply or as strictly as you can
00:17:13
Speaker
with the case of data visualization. So I think that, in part, the reason why we have not paid so much attention to qualitative data visualization is the fact that it's hard to write. It's easier to write about data visualization than it is to write about qualitative visualization or visual explanations. Fortunately, I mean, we have more and more, we have books that deal with these. You mentioned Jen Christensen's book.
00:17:40
Speaker
about science infographics, so she does that very thoroughly, but it's hard work.
00:17:46
Speaker
Yeah. One of the arguments I've been making in other places is the idea that quantitative researchers, and that could be at any level, need to be more qualitative in their work.
Qualitative Storytelling in Data Visualization
00:17:58
Speaker
They need to actually talk to people, right? And you as a journalist, like that's like second nature to use, like talking to people. Do you think from a data vis practitioner perspective, people need to be more willing or able to go out there and talk to people behind the data? Yeah, yeah, yeah, absolutely. Yeah, yeah, yeah. So that's one of my
00:18:16
Speaker
Actually, that's one of the things that I may want to write about in the future. I have several ideas already bubbling in my brain about future projects that I may want to undertake, and that is one of them. And we have several examples in the book. For example, one of the chapters
00:18:35
Speaker
is about Federica Fragatame, the Italian visualization designer. And she has this beautiful project about refugees who cross the Mediterranean Sea, who, first of all, who cross Africa, half of Africa, to reach the Mediterranean Sea. And then they cross the Mediterranean Sea. So obviously, you can represent that quantitatively. And you can show their paths, and you can show
00:19:00
Speaker
how many people cross through here, through there. And she has done that. But what she did in this particular project was to trace the paths and tell the stories of, I don't remember how many, like seven or eight specific migrants, specific
00:19:16
Speaker
refugees. And it's a wonderful project. It's still a visualization, but it's highly qualitative in nature because it doesn't just show you the hard facts. It also shows you, let's say, how the hard facts reflect and reflect back onto the lives that are being represented in those hard facts. And I think that that is wonderful. That's a wonderful trend. So I think that we need to do much more about that. I mean, we need to sort of like realize
00:19:44
Speaker
that data often represents people. And in order to understand the data, we need to understand the people behind the data or being represented by the data because data sometimes doesn't capture the complexity, the entire complexity of the lives of people being represented in the numbers.
00:20:05
Speaker
It's interesting because in the past when I've talked about this and I've interviewed other journalists about this, journalists are always like, well, you go and you go talk to people and you have this conversation and you ask them questions,
Finding Voices for Data-Driven Narratives
00:20:18
Speaker
but they don't get to the part of how do you actually find the person to talk to.
00:20:23
Speaker
So if someone's listening to this show and they're working on their data visualization about, you know, whatever it is, what would your recommendation be to find the person or the people to actually talk to to get that, you know, that, that, that insight into what the data actually mean for people's lives and experience.
00:20:44
Speaker
Well, I mean, as everything in visualization, we will greatly depend on the type of topic that we are talking about. But for example, let's say that you're doing a story about the recent onslaught of legislation against trans people here in the United States, right? Which is a newsworthy story nowadays, sadly. So it is very easy to maintain the discussion at the data level, sort of like the objective level, right?
00:21:11
Speaker
How many people are trans? How many people are receiving gender affirming care? How many people this? How many people that? Are there, let's say, side effects to gender affirming?
00:21:22
Speaker
treatments, whatever, whatever. That's the objective part of that. But you don't gain an understanding of how that reflects into the world message up to actual trans people. Right, right. Because they will tell you all this legislation has absolutely nothing to do with our well-being. It's just politically motivated. Gender affirming care is perfectly safe. It has been tested. It's not anything particularly new. It's just being presented
00:21:49
Speaker
because you don't know crap about all these stuff. We do know a lot about these, and we can teach you about it. So how do you see the people to talk to? Well, I guess that, again, every story is different, but you will go to organizations that can help you put yourself in touch with people who know much more than you do about the data.
00:22:11
Speaker
you need to strive to be, let's say, representative with the people that you choose. So there's an interplay between the objective level and the subjective level, right? You need to try to represent in the people that you choose, sort of like to represent the samples that are being reflected in the data. But there are no really clear-cut rules for this. It's all very, you know, yeah, it's a difficult process, obviously. Any journalist can tell you that. And very often we fail.
00:22:39
Speaker
at choosing our subjects is because we can really not do representative samples in interviews, right? But your point of reaching out to organizations and other groups is finally what I realized after talking to, I mean, countless data journalists is like, you don't go to some bar and randomly talk to people, right? No, you go to a diner and find people to talk about politics.
00:23:04
Speaker
No, I mean, you can do that, but it's, I mean, it will be obviously biased because it is not the same thing to talk about people in a diner about politics in Miami and that it is to do it in Minnesota, right? This is not the same thing. So the first part that was particularly interesting to me was on the qualitative data vis.
Challenges Outside Western Data Visualization Development
00:23:24
Speaker
The other part that was particularly interesting to me was on, I guess the possible, maybe the actual lack of data vis outside the US and Europe. I thought,
00:23:33
Speaker
the chapter with Mohammed Wakad was really illuminating on this point. And so I wanted to ask you, given that you've talked to so many different people, so first off, do you think there's a lack outside the US and Europe? And if so, what is holding people in those countries back? Is it the technology? Is it the data? Is it just the training? Like, what is it?
00:23:55
Speaker
It is a combination of factors. One of them is the lack of data, the lack of trustworthy and reliable data. It is hard. It's hard to produce this type of work in countries like Egypt or China. For the book, I talked to one of my former students.
00:24:15
Speaker
Catherine Ma, her chapter didn't make it to the book, but eventually it will do something about it. And she talks about that challenge in China. It's like, how did you get proper, reliable data? It's like in different places. Or in the book, you can read about Attila Batorfi from Hungary. And he talks about a COVID tracker that he developed in Hungary at a time when the Hungarian government was not provided.
00:24:43
Speaker
Reliable data. So essentially they needed to create their own data, gather data from different sources, talk to experts, handle the data. So that's a common challenge. I mean, if you talk to people, for example, a newspaper called La Nación in Argentina, they had long experience creating their own data sets to visualize just because the Argentina government is not very prone to putting out good reliable data. So that's one of the challenges.
00:25:13
Speaker
The other challenge is also related, I think, to, let's say, networks of support, self-reinforcing networks of support. In many cases, the people I talk to in other countries other than the US or the United Kingdom, et cetera, they feel a little bit lonely. It's like, I am the only one doing this type of thing here. It's just a small group of people. There is not a mass of people who are producing this type of work. And that is the reason why
00:25:43
Speaker
Some of them you mentioned Mohammed, but but he's not the only one They are trying to work as ambassadors as educators trying to spread the word try to bring more people in trying to persuade people that Visualization is not magic. It's something that anybody can learn and you should embrace it and start practicing it once you have that a critical mass of people naturally networks of mutual support
00:26:07
Speaker
will start growing as it happens, for example, in the US and the UK. Obviously, I mean, social media can help a lot with that in finding people who are like you, but it's not the same as having sort of like a local network of people you can meet with. So that's another challenge. Also related to network support is like the support of companies. It's like the fact that companies in other countries or
00:26:34
Speaker
governmental organizations or non-governmental organizations in other countries.
00:26:38
Speaker
may not be so inclined to invest money and resources in creating data visualizations for different reasons. First of all, lack of funding could be a huge problem, obviously, and they have other priorities to invest in. But in other cases, it could be just lack of knowledge. They don't know what visualization is for. They see it as something whimsical and something secondary in comparison to other goals. They have not been shown or they have not understood
00:27:05
Speaker
the value of a visually presenting data to themselves or to other people. So it's a huge number of factors, I think. And they are all interrelated. Yeah, absolutely. I wanted to finish up, you just mentioned social media. I wanted to follow up with your view of what's happening in the fields.
00:27:25
Speaker
uh, particularly with respect to, to social media. I mean, I think for, for many of us in the field, uh, well, I'll just speak for myself, I guess it's the Twitter space has sort of fallen apart. And, and, you know, I made a lot of, you know, you and I met through Twitter. I've made lots of friends through Twitter in the field. And, and I'm wondering where you see it now and how you see at least date, the date of his community sort of evolving over the next.
00:27:50
Speaker
couple of years, I guess. I honestly don't know. I feel myself a little bit intellectually impoverished by the demise of Twitter.
Twitter's Decline in the Data Visualization Community
00:28:02
Speaker
of the tutoring space just because I am not exposed to as much visualization as I used to. Just because of that. It's also because I must admit to the fact that I have essentially removed myself from social media spaces. I mean, I'm still in blue sky. I still post every now and then on LinkedIn.
00:28:23
Speaker
But there has been a conscious effort on my part to remove myself from social media spaces, because I want to focus much more deeply on several things that I'm working on. And that requires a lot of time in terms of reading, studying. And social media is very time consuming. It's a lot of fun, but it's very time consuming. So I honestly don't know. I hope that, for example, a platform such as BlueSky will pick up. I try to be active in that platform on BlueSky.
00:28:52
Speaker
also, but I don't know what will substitute a Twitter as sort of like a platform for conversation, finding new voices, finding great projects. I honestly don't know. At the moment, I have no idea. What do you think? I don't know. I mean, I've been playing around with different platforms and ideas. I mean, I think
00:29:15
Speaker
You know, the data visualization society is, is one place where you see still. Yeah, yeah, absolutely. But even there, but even there it's on Slack. I mean, Slack is, you know, it's really hard. I think, you know, yeah.
00:29:30
Speaker
I'm wondering, I was going to ask you, but I'm wondering how the conference, the date of this conference space will evolve now that we're maybe moving past the pandemic. Is that going to be a place that will, you know, we used to have a lot of great conferences and a lot of them, uh, either because of the pandemic or other reasons have, have sort of stopped. So I'm curious about how that will change over, over time. So I don't know. I, I will say that, uh, I agree with everything you said, but I do miss it and I miss those conversations and not always about.
00:29:59
Speaker
you know a chart or you know of visualization but hey you know and have a conversation about something else that like kind of turns into like how would you get the data to do this thing yeah yeah yeah yeah i miss those too yeah i don't know whether i don't know whether conferences will will go back to being what what they wear um some of them are coming back quite strongly so for instance in march in march i'm planning to attend and speak at the um
00:30:26
Speaker
a NICAR conference, which is the Investigative Reporters Conference, which is a huge data journalism and data visualization component. And that seems to be pretty healthy. Yeah, that one's pretty healthy. Yeah, for sure. I mean, you know, the Data Vis Society will have their Outlier conference in June maybe, May or June. Yeah, eventually I will start organizing conferences again myself here in Miami or hosting them. Right.
00:30:56
Speaker
Because you were doing the Miami communication. Yeah, VZUM, which is a small visualization conference. I hosted a tapestry at some point. I hosted computation and journalism, which I will host again. I want to help
00:31:13
Speaker
bring back Malofiej eventually, the Infographics Conference, but that may not happen until 2025 or something like that. So I don't know. I mean, I guess that it will all depend on how much energy people are willing to devote to bringing this or to ideate new ways of making connections. Honestly, I mean, I do want to
00:31:38
Speaker
keep organizing conferences here in Miami. But more and more, I feel that what I really want to do in the next few years is to perhaps not being so visible, perhaps staying in the backstage a little bit, doing editing, book editing.
00:31:55
Speaker
then reading, thinking, et cetera, eventually write another book at some point and helping bring other voices to, I mean, younger people who can, you know, bring fresh ideas and try to think, what are the next steps? Maybe conferences are not the answer. Maybe social media is not the way. So what are the ways? I don't know. I'm old.
00:32:15
Speaker
People who are younger might know much more than I do. Perhaps TikTok will be the next platform. Who knows? Perhaps. Yeah, perhaps. Things that you and I aren't going to even be able to follow.
00:32:25
Speaker
Yeah, and that's fine. We can just, you know, cultivate our gardens or something. That's right. We'll just yell at the kids from the front lawn. We'll just yell. Exactly. Alberto, it's always good to see you, good to chat with you. Congrats on the book. So for those who don't know,
00:32:46
Speaker
how can they find you? Where should they look for you? I mean, obviously, the book, they can get anywhere, all the major booksellers. But if they want to see your regular, are you going to keep blogging? Yeah, I should update my blog at some point. But I'm still on Twitter, so I still visit Twitter every now and then. I don't post much on it.
00:33:06
Speaker
I am on BlueSky, I'm on LinkedIn. Obviously, my blog is still active, they function on iro.com. My own website, albert.io.com, I am planning to add a calendar of talks at some point also to the website. So yeah, I'm still active online, even if I am not as crazily active as it was two or three years ago, I'm still around. I still check Twitter, I still check BlueSky, LinkedIn.
00:33:31
Speaker
So get ready to come find me there. All right. So that's where you find Alberto. Thanks again, Art of Insight. How great visualization designers think. I'm loving it. Thank you. Have a good start to the year. Thank you. You too.
00:33:45
Speaker
Thanks to everyone for tuning into this week's episode of the show. I hope you enjoyed that interview with Alberto, and I hope you'll check out Alberto's book, The Art of Insight. I've also put an entire list of books and people that we talked about in the interview on the full show notes page at policyvis.com. You can check out a more curated list of notes in your podcast provider app, but if you want the full list, you can go over to policyvis.com.
00:34:10
Speaker
And if you would be so kind as to rate and review the show on your favorite podcast provider, I would really appreciate it. That enables me to find better and more guests to bring to you so you can learn more about data and data visualization. So until next time, this has been the policy of this podcast. Thanks so much for listening.
00:34:29
Speaker
A number of people help bring you the Policyviz podcast. Music is provided by the NRIs, audio editing is provided by Ken Skaggs, design and promotion is created with assistance from Sharon Satsuki-Ramirez, and each episode is transcribed by Jenny Transcription Services. If you'd like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts.
00:34:50
Speaker
The Policy Vis podcast is ad-free and supported by listeners. If you'd like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com slash policyvis.