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.

Topics Discussed

  • The Quest for Reliable Data: Alberto and I discuss the critical importance of acquiring accurate and reliable data. We talk about the the complexities involved when dealing with cross-country datasets and how cultural and systemic differences can impact data collection and representation.
  • The Shifting Platforms of DataViz Communities: Once a thriving hub for sharing insights and innovations, Twitter’s popularity in the dataviz community is on the decline. We explore the implications of this shift and what platforms are emerging as new gathering spots for professionals and enthusiasts alike.
  • The Post-Pandemic Outlook: With the world slowly adjusting to the new normal, we consider the future of data-related conferences and community gatherings. How will these events evolve, and what can we expect from virtual and in-person interactions in the coming years?
  • Spotlight on Alberto: No conversation about data visualization would be complete without mentioning Alberto’s influential work in the field. We talk about his contributions, including his most recent book, The Art of Insight, and how his teachings have shaped the way we approach data storytelling.


Alberto: Website | Functional Art website | Amazon author page

Books mentioned:

Functional Aesthetics for Data Visualization by Vidya Setlur and Bridget Cogley

Building Science Graphics: An Illustrated Guide to Communicating Science through Diagrams and Visualizations by Jen Christiansen

Making with Data: Physical Design and Craft in a Data-Driven World by Samuel Huron, Till Nagel, Lora Oehlberg, and Wesley Willet

Autographic Design: The Matter of Data in a Self-Inscribing World by Dietmar Offenhuber

Practical Charts: The Essential Guide to Creating Clear, Compelling Charts for Reports and Presentations by Nicholas Desbarats

The Visual Display of Quantitative Information by Edward Tufte

Guest Bio

Alberto Cairo is a journalist and designer with many years of experience leading graphics and visualization teams in several countries. He joined the School of Communication in January 2012. He teaches courses on infographics and data visualization. He is also director of the Center for Visualization, Data Communication & Information Design at UM’s Institute for Data Science and Computing, and a Faculty Fellow at the Abess Center for Ecosystem Science and Policy.

Cairo has been described by Microsoft as always “in the vanguard of visual journalism”. He is author of the books How Charts Lie: Getting Smarter About Visual Information (W.W. Norton, 2019), The Truthful Art: Data, Charts, and Maps for Communication (Peachpit Press, 2016), and The Functional Art: an Introduction to Information Graphics and Visualization (Peachpit Press 2012). His most recent book, The Art of Insight: How Great Visualization Designers Think, was published by Wiley in 2023.

Alberto Cairo led the creation of the Interactive Infographics Department at El Mundo (, Spain), in 2000. His department is widely considered a pioneer in online news. Cairo’s team won more Malofiej and Society for News Design (SND) infographics international awards than any other news organization worldwide between 2001 and 2005. Cairo’s most recent award is a 2019 Sigma data journalism award ( for a large investigative project in collaboration with Google and El Universal, one of the largest national newspapers in Mexico. Cairo’s students have also been recognized in international visualization and infographics competitions such as Information is Beautiful and Computation+Journalism.

Background of featured image from Nadieh Bremer.

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01:14 – 01:33
fall semesters. Welcome back to the Policy Biz Podcast. I’m your host, Jon Schwabisch. On this

01:33 – 01:39
week’s episode of the show, I chat with the one and only Alberto Cairo, author of the new book,

01:39 – 01:44
The Art of Insight, How Great Visualization Designers Think. And so Alberto and I talk about

01:45 – 01:50
his process of writing the book and how he went about identifying and thinking about who he

01:50 – 01:56
wanted to talk to and why. We talk about whether he believes in rigid rules to data visualization

01:56 – 02:03
and we talk about my two favorite things about the book. The first is on qualitative data visualization,

02:03 – 02:08
and the challenge with getting good qualitative data and how if you’re not used to collecting

02:08 – 02:13
qualitative data, how you might actually go about do that. As you probably know, Alberto is

02:13 – 02:18
a former journalist, so he has a lot of things to say about actually talking to people. And

02:18 – 02:21
the other thing that we spend a lot of time talking about and what I found really interesting

02:21 – 02:28
in the book was the possible I’d say possible lack of data visualization outside the US and

02:28 – 02:33
outside Europe. And we talked about why that might be and how different areas of the world might

02:34 – 02:39
increase or improve their data visualizations. So if you’re working in the data visualization

02:39 – 02:45
field outside the US, outside Europe, I’d be curious to hear what your challenges are and how

02:45 – 02:50
you are creating better data visualizations. And so you can of course reach out to me on

02:51 – 02:57
or Twitter, LinkedIn, or Instagram. So having said that, let’s take a listen to this week’s

02:57 – 03:00
episode of the PolicyViz podcast with Alberto

03:00 – 03:01

03:03 – 03:09
Oh my goodness. Alberto Cairo. What a pleasure. Good to see you again, friend. It’s been a

03:09 – 03:12
while. Hey, John. Long time no see. Thank you for having me.

03:12 – 03:17
Of course. Book number for English book number

03:17 – 03:19

  1. English book number 4. Yes.

03:19 – 03:22
But total books number 5 or 6?

03:23 – 03:30
Number 6. Yes. Right. Because we we we would need to no. Number 7. Woah. Yeah. We would need

03:30 – 03:35
to count the first book that I ever wrote, which has nothing to do with visualization. That

03:35 – 03:37
was when in my early twenties.

03:37 – 03:41
So is the Alberto Cairo box set, that’s a that’s a 7.

03:42 – 03:44
The Collected Series.

03:44 – 03:51
The Collected Series with a special cover on it. It’s got a special, it’s a special booklet

03:51 – 03:52
that you get that Yeah. I know.

03:52 – 03:55
Write them. In a steel box. Yeah. That’s right.

03:55 – 04:01
That’s right. That’s right. Well, it’s great to have you on again. I love the new book, Art

04:01 – 04:06
of Insight. So this is this book is, I’d say, quite different than your other books.

04:06 – 04:08
Absolutely. Yes.

04:08 – 04:13
Yeah. And wanna and I wanna dive into all the kind of different pieces of it. I think my first

04:13 – 04:17
question is, was this book more fun for you to write than than the other books?

04:18 – 04:24
It was more fun in the sense that it’s the most personal book that I’ve written to to data,

04:24 – 04:30
I I would say. So in that sense, yes. It was a lot of fun. It was also a lot of fun because

04:30 – 04:35
I got to learn a lot and talk to people whose work I admire, which is always great to bring,

04:35 – 04:41
you know, some inspiration and reenergize yourself. So in that sense, yeah, it was a lot of

04:41 – 04:47
fun. But it was a bit of a struggle to to write because of, due to personal reasons and life

04:47 – 04:55
changes. I’ve, been, pushing this book. I mean, the book has been delayed for more than 2 years.

04:55 – 05:03
So Wiley, my publisher, was extremely generous in giving me sort of, like, flexible deadlines

05:03 – 05:08
and accepting my constant delays. And so it was a little bit of a struggle to get it done.

05:08 – 05:08

05:08 – 05:14
But once I was able to sit down and actually get it done, it was a it was a huge pleasure. Yeah.

05:14 – 05:15
For sure. Yeah.

05:16 – 05:22
What was your process like? So so you interviewed around 2 dozen or so, plus I would guess many

05:22 – 05:27
others that maybe aren’t in the book, but designers and and developers and however we wanna

05:28 – 05:35
whatever we call data visualization folks these data. Did you transcribe all the recordings

05:35 – 05:38
and go back through them? Like, I mean, you’re a former journalist, so this is probably gonna

05:38 – 05:40
be kinda second nature for you. But what’s your process?

05:41 – 05:45
Yeah. Yeah. So well, first of all, I needed to come up with a list of people I wanted to talk

05:45 – 05:52
to. And Mhmm. As I mentioned in the in the introduction to the book, that has no any system

05:52 – 05:57
to it. It was like I I just wrote down tons of names of people whose work I like I like, and

05:57 – 06:04
I was interested in talking to about visualization and in no particular order. And I came up

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with a with a very long list, like 50 or 60 people. And I talked to more people than appear

06:11 – 06:15
in the book. I wouldn’t I will need to think about what to do with those conversations. Yeah.

06:15 – 06:21
I even thought about doing a follow-up volume, like a second part of the the other side and

06:21 – 06:25
then make more conversations and stuff just because those conversations were equally enjoyable.

06:26 – 06:31
But I had a limited limited number of pages, so I needed to choose at the end what to include.

06:32 – 06:36
But, again, it’s there is no it’s not a as I said in the introduction, it’s not a systematic

06:37 – 06:43
it’s not a representative sample of anything other than my own thinking and my my own preferences.

06:43 – 06:49
So I first of all came up with the list, and I contacted people. And then all conversations,

06:50 – 06:54
I would not call them interviews because they were not in really interviews. I didn’t have a

06:54 – 07:00
particular set of questions that I had that I had for people. I just wanted people to talk to

07:00 – 07:05
me about their work and what Right. They felt passionate about and about their thinking process.

07:06 – 07:11
I I mean, you read the book, so you know this already, but the book is not really about the

07:11 – 07:18
work. It’s not a it’s really not a process book. Here’s my projects. Here’s how I make it. It’s

07:18 – 07:23
more about the people who are behind the work. That was what what I what I was really interested

07:23 – 07:30
in. It’s like, who are these people? What gets them excited? What motivated them to get into

07:30 – 07:36
visualization? What motivates them today? What are their ideologies, worldviews, philosophies,

07:36 – 07:41
you know, passions or fears? And I was interested in all that because that that I think that

07:41 – 07:47
life permeates our work as much as work permeates our life. And that is that is the focus of

07:47 – 07:52
the of the book. So, yeah, I had all these conversations. I recorded them all. Then I have them

07:53 – 07:58
transcribed. I didn’t transcribe them myself. That’s a lot of work. So I hired a professional,

07:59 – 08:04
to transcribe the conversations. Then I I I wrote the conversation. So, obviously, there’s a

08:04 – 08:10
lot of editing involved in that. I mean, because in in in some cases, conversations can be a

08:10 – 08:15
little bit rumbling, and so you need to give them a proper shape. And then I gave everyone the

08:15 – 08:20
opportunity, which is not common practice in journalism. In journalism, you usually don’t do

08:20 – 08:25
this, but this is not a journalistic work. I wanted Right. The people in the book to be happy

08:25 – 08:31
with their own words. So I gave everybody the opportunity to read, my take over their words

08:31 – 08:37
and then help me with the editing so people could be properly represented in there by by the

08:37 – 08:42
words that appear in the you will never do that if you’re working in a newspaper. But, you know,

08:42 – 08:45
this is a book, this is my book, and I do whatever the hell I want with it.

08:45 – 08:51
That’s right. That’s right. So you mentioned that this book is not really about the process.

08:51 – 08:56
It’s not about how did person go from step a to step b to step c to get this thing, online.

08:56 – 09:03
In my view of the Data library, right, the books are kind of moving from the how to books, the

09:03 – 09:07
best practices book. You’ve written you’ve written a couple of those. I’ve written a couple

09:07 – 09:13
of those. They’re they seem now we’ve got more books on process, like, Settler’s book and Jen

09:13 – 09:18
Christensen’s book. We’ve got different types of data coming out. There’s the making with data

09:18 – 09:26
book and and, Demer Offenhuber’s book is out on, data physicalization. Do you see that evolution

09:26 – 09:30
in the, I guess, the library of data visualization books? And is that do you think that I mean,

09:30 – 09:34
you talk a lot about philosophy in the book. So, like, do you see that as a natural evolution

09:34 – 09:35
of a of a field as it matures?

09:36 – 09:42
I I think that the evolution is not so much a linear process. It’s it’s more a it’s more a a

09:42 – 09:47
diversification process. It’s like we instead of having sort of like a linear sequence of types

09:47 – 09:53
of books, In the eighties, we had Data. In the nineties, we have these. In the 2000, we have

09:53 – 09:59
that. It’s more that there’s a broader spectrum of types of books that we have today. We have,

09:59 – 10:04
obviously, we still have the we have still have the basic principle type of book. Right? It’s

10:04 – 10:09
like we have practical charts by Nick Desperats recently. Right? Yep. Which is an excellent

10:09 – 10:14
it’s an excellent basics book. And there is always going to be a need for books that remind

10:14 – 10:20
the community about some basics of how we do things and why we do things the way we do them.

10:21 – 10:26
But there’s also books about history. There’s also books about, you know, sort of like a meta

10:26 – 10:33
data reasoning about visualization, philosophy visualization, and then books about the people

10:33 – 10:38
who create visualization, like the like the art of insights. Again, not focus on the process,

10:38 – 10:42
but more focus on the people. So I think I think that this, that that’s the evolution. It’s

10:42 – 10:46
like these sort of, like, more diverse spectrum of of types of books.

10:46 – 10:52
Yeah. You also and anyone who knows your work isn’t gonna be surprised by this. But you also

10:52 – 10:57
spend time throughout the book refuting this idea that there are rigid rules. You should do

10:57 – 11:01
this. You should do that. You should, you know, do this or not do that. Are there any rules

11:01 – 11:06
you think that practitioners should follow, or is it all bend them and then break them sort of things?

11:07 – 11:13
So, I mean, the way that I the way that I explain this in the book is not that there are no

11:13 – 11:18
there are no rules. What I say is that there are no rules that are universal. It’s like rules

11:18 – 11:25
or ways or heuristics or ways to behave and ways to act are greatly dependent on the context,

11:25 – 11:30
are greatly depending on the goals, are greatly depending on what you want to do. There are

11:30 – 11:38
certain, I would say, very general principles if you want to call them, I would say. For example,

11:38 – 11:44
I want visualization to be a truthful endeavor. Right? We should always strive to represent

11:44 – 11:49
our best understanding of what the truth is, which may differ. I mean, people may approach the

11:49 – 11:55
same data in different ways, and that’s perfectly fine, or interpret a data set and represent

11:55 – 12:00
the data set consequently in different ways is because we interpret it differently. So there

12:00 – 12:05
really there’s even not a rule in there other than Yeah. Try to do try to do your best. But

12:05 – 12:09
what I explained in the book is that what really matters is that is is perhaps we should we

12:09 – 12:15
should do is to stop thinking of visualization in terms of principles and rules and start thinking

12:15 – 12:21
about visualization or more broadly, the data analysis process as more as sort of like a reasoning

12:21 – 12:28
process. It’s like you need to give yourself and give others reasons that are sound and that

12:28 – 12:35
can be understood by others. And you need to be able to rationally justif logically justify

12:35 – 12:40
the reasons that took you to to make a particular thing or to do a particular thing in a particular

12:40 – 12:44
context. And you should be able to have that conversation. So that I would say that that’s the

12:44 – 12:48
general principles. Like, base your decisions on that type of reasoning.

12:49 – 12:54
I’m guessing that’s how you approach your teaching. That the that the theme or the thread through

12:54 – 12:59
your classes is it’s about reasoning. It’s not about step a, step b, or or even if it’s not

12:59 – 13:00
enough linear way.

13:00 – 13:09
Yeah. Even when I explain basic stuff such as, for example, why is it advisable that bar graphs

13:10 – 13:17
start at 0? I I can explain why. I mean, I can explain the reasons behind that. It’s like and

13:17 – 13:25
and I I I try to walk students through that reasoning. And, and whenever I I need to grade students,

13:25 – 13:30
I I don’t like grading. Whenever I need to give feedback to students, what I ask what I can

13:30 – 13:37
ask students to do is to be able to provide reasons for every choice that they make. Why do

13:37 – 13:41
you use this particular typography? Or why do you use this particular color? Or why are graphics

13:41 – 13:48
arranged in this particular way? I may disagree with the reasons given for that particular choice,

13:48 – 13:53
but at least we can establish a conversation. And they can give me those reasons, then I can

13:53 – 13:59
give them back my reasons, and then we may be able to reach a consensus or not. They may see

13:59 – 14:05
the whether my my my reasons have any merit to them or not, and then they may follow my reasons

14:05 – 14:10
so they may stick to the reasons. Right? So it’s all about the conversation at the end. And

14:10 – 14:14
this conversation can be based on, as I explained, the art of insight. It can be based obviously

14:14 – 14:19
on experience. It can be based on what you have observed that works or not throughout the years.

14:19 – 14:26
It can be based on a growing body of empirical evidence that that we can all use and draw from.

14:27 – 14:31
But in some cases, it can be just based on taste. And and many decisions in visualizations are

14:31 – 14:36
based on taste, and that’s perfectly fine as long as you make that clear and straightforward.

14:37 – 14:43
Back to the the point about the the evolution of of books, do you think that’s part of the evolution

14:43 – 14:48
of the field? I mean, in, like, the modern the modern sort of work on data visualization, we

14:48 – 14:53
have, you know, kind of the tough camp of rigid rules that, you’ve written about in the past

14:53 – 14:59
about how they are are are not based on anything more than preference. And now it seems we’ve

14:59 – 15:04
moved towards this more of a reasoning, a logic. There’s some aesthetic decisions that are kind

15:04 – 15:09
of, you know, more embraced by the field rather than these rigid rules. Is that is that, do

15:09 – 15:12
you think, part of the evolution of the field or the diversity?

15:12 – 15:16
Yeah. Yeah. It’s a it’s a it’s a sign of maturation. So that we are we are reaching a point

15:16 – 15:21
of, higher or deeper maturity, which is a good thing.

15:22 – 15:28
Yeah. The other thing that I found really interesting so there there are 2, for me, 2 threads

15:28 – 15:34
in the book that I found really, really interesting. So first is on qualitative data vis. You

15:34 – 15:39
discussed it several places in the book. Many of the people you talk to do a lot of really incredible

15:39 – 15:45
stuff with with qualitative data, data vis. I’m I’m curious whether you think qualitative Databiz

15:45 – 15:52
has kinda gotten a short straw in the field in terms of instruction and and practices and approaches?

15:52 – 15:55
And if so, like, how do we how do we deal with that?

15:56 – 16:03
Well, we deal I mean, it has not been as as covered or as deeply covered as quantitative visualization

16:04 – 16:10
simply because it is harder to teach it systematically. Right? Yeah. When when we teach quantitative

16:10 – 16:14
data visualization, it’s easy to teach it systematically because you can you can talk about

16:14 – 16:19
the grammar of graphics. Right? You have you have a set of numbers, but beta is not always numbers,

16:19 – 16:23
obviously. But, you know, let let’s let’s speak loosely here. So we are you have a set of numbers

16:23 – 16:31
or quantities, then you have a set of objects, and and there are certain grammatical principles

16:31 – 16:36
that teach you how to map those numbers onto those objects and then vary certain properties

16:36 – 16:39
of those objects in proportion to the numbers that you’re representing Mhmm. Which is the core

16:39 – 16:44
of the grammar of graphics. So that is very easy to systematize. But how do you systematize

16:44 – 16:50
the teaching of, let’s say, I don’t know, Jaime Serra’s work. Jaime is one of the designers

16:50 – 16:57
that I showcase in the book who is famous for producing this beautiful illustration driven visual

16:57 – 17:03
explanation. So how do you systematize that? You really can’t. Right? Yeah. You can talk about

17:03 – 17:08
vague heuristics and sort of, like, principles of composition and organization of information,

17:09 – 17:15
but you cannot systematize it as as deeply or as strictly as you can with the case of data visualization.

17:15 – 17:20
So I think that in part, the reason why we have not paid so much attention to qualitative data

17:20 – 17:25
visualization is the fact that it’s it’s hard to write. Hard to write. Yeah. It’s it’s easier

17:25 – 17:30
to write about data visualization than it is to write about qualitative visualization or visual

17:30 – 17:36
explanations. Fortunately, I mean, we have more and more, we have books that deal with this.

17:36 – 17:42
Or did you mention Jon Chris Christensen’s book, about science infographics? So she does that

17:43 – 17:45
very thoroughly, but it’s hard work.

17:46 – 17:52
Yeah. And one of the arguments I’ve been making in other places is the idea that quantitative

17:52 – 17:55
researchers and that could be at any level

17:55 – 17:55

17:55 – 17:59
Need to be more qualitative in their work. They need to actually talk to people. Right? And

17:59 – 18:04
you as a journalist, like, that’s, like, second nature to you is, like, talking to people. Mhmm.

18:04 – 18:11
Do you think from a data viz practitioner perspective, people need to be more willing or able

18:11 – 18:13
to go out there and talk to people behind the data?

18:13 – 18:17
Yeah. Yeah. Yeah. Absolutely. Yeah. Yeah. Yeah. So that’s one of my actually, that that’s one

18:17 – 18:24
of the things that I may want to write about in the future. So I have several ideas already

18:24 – 18:29
bubbling in my brain about future projects that I may want to undertake and that is that is

18:29 – 18:33
one of them. And we have several examples in the, several examples in the book. For example,

18:34 – 18:40
one of the chapters is about a Federica Fraga Pane, the Italian visualization designer. And

18:40 – 18:47
she has this beautiful project about refugees who cross the Mediterranean Sea. Who first of

18:47 – 18:53
all, who cross Africa half of Africa to reach Mediterranean Sea, and then they cross the Mediterranean

18:53 – 18:59
Sea. So, obviously, you can you can represent that quantitatively, and and you can show their

18:59 – 19:03
paths, and you can show, you know, how many people cross through here or through there. And

19:03 – 19:08
she she did she has done that, but what she did in this particular project was to trace the

19:08 – 19:14
paths and tell the stories of, I don’t remember how many, like 7 or 8 Yeah. Specific migrants,

19:14 – 19:21
specific, refugees. I and it’s a wonderful project. It’s still a visualization, but it’s highly

19:21 – 19:27
qualitative in nature because it doesn’t just show you the hard facts. It also shows you, let’s

19:27 – 19:34
say, how the hard facts reflect and reflect back onto the lives that are being represented in

19:34 – 19:38
those hard facts. And I think that that is wonderful. That’s a that’s a wonderful trend. So

19:38 – 19:43
I think that we need to do we need to do much more about that. I mean, we need to sort of, like,

19:43 – 19:53
realize that a data often represents people. And in order to understand the data, when to understand

19:53 – 19:57
the people behind the data or being represented by the data. Because data sometimes doesn’t

19:57 – 20:03
capture the, the complexity the entire complexity of the lives of people being represented in the numbers.

20:05 – 20:09
It it’s interesting because when I in the past, when I’ve talked about this and I’ve interviewed

20:10 – 20:14
other journalists about this, journalists are always like, well, you you go and you you go talk

20:14 – 20:19
to people and you have this conversation and you ask questions, but but they don’t get to the

20:19 – 20:25
part of how do you actually talk you know, find the person to talk to. So if someone’s listening

20:25 – 20:29
to this show and they’re working on their data visualization about, you know, whatever it is,

20:29 – 20:36
what would your recommendation be to find the person or the people to actually talk to to get

20:36 – 20:43
that, you know, that that that insight into what the data actually mean for people’s lives and experience?

20:43 – 20:48
Well, I mean, as everything in visualization, we will greatly depend on the type of topic that

20:48 – 20:55
we are we’re talking about. But for example, let’s say that you’re doing a story about, you

20:55 – 21:01
know, the recent onslaught of legislation against trans people here in the United States. Right?

21:01 – 21:08
Which is a newsworthy story nowadays, sadly. So it is very easy to maintain the discussion at

21:08 – 21:13
the data level, as sort of like the objective level. Right? It’s like how many people are trans?

21:13 – 21:17
How many people are receiving gender affirming care? How many people this? How many people that?

21:18 – 21:24
Are there, let’s say, side effects to gender affirming, treatments? Whatever whatever. That’s

21:24 – 21:29
the objective part of that, But you don’t gain an understanding of how that reflects into the

21:29 – 21:32
world unless they talk to actual trans people. Right.

21:32 – 21:33

21:33 – 21:38
Because they will tell you all these legislation has absolutely nothing to do with our well-being.

21:39 – 21:45
It’s just politically motivated. Gender affirming care is perfectly safe. It has been tested.

21:45 – 21:51
It’s not anything particularly new. It’s just being present because you don’t know crap about

21:51 – 21:57
all these stuff. And we do know a lot about these, and we can teach you about it. So how do

21:57 – 22:01
you school the people to talk to? Well, I guess that again, every story is different, but you

22:01 – 22:09
will go to organizations, that can help you put yourself in touch with people who know much

22:09 – 22:15
more than you do about the data. You need to to strive to be, let’s say, representative with

22:15 – 22:20
the people that you choose. There’s an interplay between the objective level and the subjective

22:20 – 22:25
level. Right? You need to try to represent in the people that you choose, sort of like, try

22:25 – 22:29
to represent the samples that are being reflected in the in the data. But there are no really

22:29 – 22:35
clear cut rules for these. It’s all all very, you know yeah. It’s it’s a difficult process,

22:35 – 22:41
obviously. Any journalist can tell you that. And very often, we fail at at at choosing our choosing

22:41 – 22:47
our subjects is because we don’t we can really not do representative samples in the interviews. Right?

22:47 – 22:52
But but your point of of reaching out to organizations and other groups is is finally what I

22:52 – 22:58
realized after talking to, I mean, countless data journalists is, like, you don’t go to some

22:59 – 23:01
bar and randomly think you’re talking to people. Right?

23:01 – 23:04
A diner. You go to a diner and find people to talk about politics.

23:04 – 23:05

23:05 – 23:09
No. They’re gonna I mean, you can do that, but it’s, I mean, it will be obviously biased because

23:09 – 23:14
it is not the same thing to talk about people in a diner about politics in Miami than it is

23:14 – 23:18
to do it in Minnesota. Right? This is not the same thing, obviously.

23:18 – 23:22
Yeah. Yeah. So the first part that was particularly interesting to me was on the qualitative

23:23 – 23:29
data, this. The other part that was particularly interesting to me was on, I guess, the possible,

23:29 – 23:35
maybe the actual lack of Data Viz outside the US and Europe. I thought the chapter with Mohammed

23:35 – 23:40
Waqed was really, illuminating on on this point. And and, so I wanted to ask you given that

23:40 – 23:45
you’ve talked to so many different people. So first off, do you think there’s a there’s a lack

23:45 – 23:51
outside the US and Europe? And if so, what is holding people in those countries back? Is it

23:51 – 23:55
is it the technology? Is it the data? Is it just the training? Like, what is it?

23:55 – 24:02
It is a combination of factors. One of them is the lack of the lack of data, the lack of Mhmm.

24:02 – 24:08
Trustworthy and reliable data. It is hard. It’s hard to produce this type of work in countries

24:08 – 24:15
like, Egypt or or China. I I Yeah. For for the book, I talked to one of my former students,

24:15 – 24:20
Catherine Ma. Her chapter didn’t make it to the book, but eventually, we’ll do something about

24:20 – 24:27
about it. And she talks about that challenge in China. So how did you get proper reliable data?

24:27 – 24:32
Right? It’s like in in different places. Right? Or in the book, you can read about a Atila Batorffy

24:33 – 24:40
from Hungary. And he he talks about a COVID tracker that he developed in Hungary, at a time

24:40 – 24:46
when the Hungarian government was not provided with reliable data. So they essentially, they

24:46 – 24:52
needed to create their own data, gather data from different sources, talk to experts, handle

24:52 – 24:59
the data. So that’s a common challenge. I mean, if you talk to people, for example, a newspaper

24:59 – 25:05
called La Nacion in Argentina, they had, you know, long experience creating their own datasets

25:05 – 25:09
to visualize just because the Argentinian government is not very proud of putting out, you know,

25:09 – 25:14
good good reliable data. So that that’s one of the that’s one of the challenges. The other challenge

25:14 – 25:22
is also is is related, I think, to, let’s say, networks of support. So re self reinforcing networks

25:22 – 25:29
of support. In many cases, the people I talk to in in other countries other than the US or the

25:29 – 25:34
United Kingdom, etcetera, they’re they feel a little bit lonely. It’s like I am the only one

25:34 – 25:40
doing this type of thing here. It’s just a small group of people. There’s not a mass of people

25:40 – 25:45
who are producing this type of work. And that is the reason why some of them, you mentioned

25:45 – 25:50
Mohammed, but but he’s not the only one, They are trying to work as ambassadors, as educators,

25:50 – 25:55
trying to spread the word, trying to bring more people in, trying to persuade people that visualization

25:55 – 26:00
is not magic. It’s something that anybody can learn, and you should embrace it and start practicing

26:00 – 26:07
it. Once you have that a critical mass of people, naturally, networks of mutual support will

26:07 – 26:14
start growing as it happens, for example, in the US and the UK. Obviously, I mean, social media

26:14 – 26:20
can help a lot with that, in finding people who are like you and but it’s not the same as having

26:20 – 26:25
a sort of like a local network or Yeah. Sure. People you can meet with. So that’s another that’s

26:25 – 26:30
another challenge. Also related to NERVOX support is like the support of companies. It’s like

26:30 – 26:36
the fact that companies in other countries or or governmental organizations or nongovernmental

26:36 – 26:43
organizations in other countries may not be so inclined to invest money and resources in creating

26:43 – 26:48
data visualizations for different reasons. First of all, lack of funding could be a huge problem,

26:48 – 26:53
obviously. They and they have other priorities to invest in. But in other cases, it could be

26:53 – 26:58
just lack of knowledge. They don’t know what visualization is for. They see it as something

26:58 – 27:03
whimsical and something secondary in comparison to other goals. They have not been shown or

27:03 – 27:09
they have not understood the value of a visually presenting data to themselves or to other people.

27:09 – 27:13
So it’s a it’s a huge number of factors, I think, and they are all interrelated.

27:14 – 27:19
Yeah. Absolutely. I wanted to to finish up. You just mentioned social media. I wanted

27:19 – 27:19
to follow-up

27:19 – 27:27
with your view of what’s happening in the field, particularly with respect to to social media.

27:27 – 27:32
I mean, I I think for for many of us in the field, well, I’ll just speak for myself, I guess.

27:32 – 27:36
It’s the Twitter space has sort of fallen apart and and, you know, I made a lot of you know,

27:36 – 27:40
you and I met through Twitter. I’ve made lots of friends through Twitter in the field. And and

27:40 – 27:48
I’m wondering where you see it now and how you see at least data the DataViz community sort

27:48 – 27:51
of evolving over the next couple of years, I guess.

27:52 – 28:01
I honestly don’t know. I I feel myself a little bit, intellectually impoverished by the, by

28:01 – 28:08
the demise of Twitter Yeah. Of the Twitter space just because I am not exposed to as much visualization

28:08 – 28:13
as I used to. Yeah. Just because of that. It’s also because I must admit to the fact that I

28:13 – 28:19
have I have essentially removed myself from social media spaces other than I mean, I’m still

28:19 – 28:24
in Blue sky. I still post, you know, every now and then on Linkedin. But there has been an a

28:24 – 28:31
conscious effort on my part to remove myself from social media spaces, because I Jon to focus

28:31 – 28:35
much more deeply on several things that I’m working Jon. And that requires a lot of time in

28:35 – 28:41
terms of reading, studying. And social media is, is very time consuming. It’s a lot of fun,

28:41 – 28:46
but it’s very time consuming. So I honestly don’t know. I hope that, for example, a platform

28:46 – 28:54
such as Blue Sky will pick up. I try to be active in that platform on Blue Sky, LinkedIn also.

28:54 – 28:59
But I don’t know what will substitute Twitter as a sort of like a platform for conversation,

29:00 – 29:06
finding new voices, finding great projects. I honestly don’t know. At the moment, I have no

29:06 – 29:08
idea. But what what do you think?

29:08 – 29:13
I don’t know. I mean, I I’ve I’ve been playing around with different platforms and ideas. I

29:13 – 29:19
mean, I I think, you know, the data visualization society is is one place where you see still Yeah.

29:19 – 29:20
Yeah. Absolutely.

29:20 – 29:27
Mhmm. But even even there but even there, it’s on Slack. I mean, Slack is you know, it’s really

29:27 – 29:28
hard, I think. You know?

29:28 – 29:30
Yeah. It is hard.

29:30 – 29:34
I I’m wondering I was gonna ask you, but I’m wondering how the conference, the Data conference

29:34 – 29:40
space will evolve now that we’re maybe moving past the pandemic? Is that gonna be a place that

29:40 – 29:45
will you know, we used to have a a lot of great conferences and a lot of them, either because

29:45 – 29:48
of the pandemic or other reasons have have sort of stopped. So

29:48 – 29:48

29:48 – 29:53
I’m curious about how that will change over over time. So I don’t know. I I I will say that

29:53 – 29:58
I I agree with everything you said, but I I do miss it and I miss those conversations and not

29:58 – 30:05
always about, you know, a chart or, you know, a visualization. But, hey. You know, it it have

30:05 – 30:09
a conversation about something else that, like, kinda turns into, like, how would you get the

30:09 – 30:10
data to do this thing?

30:10 – 30:14
Yeah. Yeah. Yeah. Yeah. Yeah. I miss those 2. Yeah. I don’t know whether I don’t know whether

30:14 – 30:20
conferences will will go back to being what what they were. Some of them are coming back quite

30:20 – 30:27
strongly. So for instance, in March in March, I’m planning to attend and speak at the, NICAR

30:27 – 30:32
conference, which is the investigative reporters conference, which is a huge data journalism

30:33 – 30:37
and data visualization component, and that seems to be pretty healthy.

30:37 – 30:42
Yeah. That one’s pretty healthy. Yeah. For sure. Yeah. Yeah. I mean, you know, the Data Viz

30:42 – 30:45
Society will have their, outlier conference in

30:45 – 30:46
Outlier. Yeah. Yeah.

30:46 – 30:49
Yeah. In Jon, maybe May or June.

30:49 – 30:54
Yeah. Eventually, I will start I will start organizing conferences again myself here in Miami

30:54 – 30:55
or hosting Right.

30:56 – 30:59
Because you were doing the the Miami, communication.

30:59 – 31:07
Yeah. VISUAM, which is a a small visualization conference. I hosted data tape tapestry at some

31:07 – 31:14
point. I hosted computation on journalism, which I will host again. I want to help, bring back

31:14 – 31:21
Malo Fiege eventually. Mhmm. The infographics conference, but that may not happen until 2025

31:21 – 31:25
or something like that. So I don’t know. I mean, I guess that it will all depend on how much

31:25 – 31:33
energy people are willing to devote to to to bring in this or or to or to ideate new ways of

31:33 – 31:40
making making connections. Honestly, I mean, I I do want to keep organizing conferences here

31:40 – 31:45
in Miami, but more and more, I feel that what I really want to do in the next few years is to,

31:47 – 31:53
perhaps not being so visible. Perhaps staying in the backstage a little bit, doing editing,

31:53 – 31:59
book editing, then reading, thinking, etcetera. Eventually, write another book at some point.

32:00 – 32:05
And and and helping bring other voices to I mean, younger people who can Yeah. You know, bring

32:05 – 32:10
fresh ideas and try to think what are the next steps. Maybe conferences are not the answer.

32:10 – 32:16
Maybe social media is not the way. So what are the ways? I don’t know. I’m old. Yeah. And people

32:16 – 32:21
who are younger might know much more than I do. Perhaps TikTok will be the next platform. Who knows?

32:21 – 32:25
Perhaps. Yeah. Perhaps. Things things that you and I aren’t gonna even be able to follow.

32:26 – 32:30
Yeah. And that’s fine. We can just, you know, cultivate our gardens or something.

32:30 – 32:31
That’s right. We’ll just

32:31 – 32:32
And we’ll

32:32 – 32:36
just yell at the kids from our from the front lawn. We’ll just we’ll just we’ll just yell and just shake

32:36 – 32:38
our face. Yeah.

32:40 – 32:45
Alberto, it’s always good to see you. Good to chat with you. Congrats on the book. So for those

32:45 – 32:50
who don’t know, how can they find you? Where where should they look for you? I mean, obviously

32:50 – 32:52
the book, they can get anywhere, all the major booksellers.

32:53 – 32:53

32:53 – 32:56
But if they wanna see your regular are you gonna keep blogging?

32:57 – 33:01
Yeah. I will keep yeah. I wish I should update my blog at some point, but, you know, I’m still

33:01 – 33:06
on Twitter. So I still visit Twitter every now and then. I don’t post much on it. I’m on Blue

33:06 – 33:13
Sky. I’m I’m on LinkedIn. Obviously, my blog is still active, My own

33:13 – 33:20
website, I I’m planning to add, a a calendar of talks at some point also to

33:20 – 33:25
the to the website. So, yeah, I’m still active online. Even if I am not as crazily active as

33:25 – 33:31
I was 2 or 3 years ago, I’m still around. I still check Twitter. I still check Blue Sky, LinkedIn.

33:31 – 33:33
So again, you can find me there. Yep.

33:33 – 33:38
Alright. So that’s where you can find Alberto. Thanks again. Art of insight. How great visualization

33:38 – 33:39
designers think. I’m loving it.

33:39 – 33:40
Thank you. And,

33:40 – 33:41
have a good

33:43 – 33:45
start to the year. Thank you. You too. Thanks to

33:45 – 33:49
everyone for tuning into this week’s episode of the show. I hope you enjoyed that interview

33:49 – 33:54
with Alberto, and I hope you’ll check out Alberto’s book, The Art of Insight. I’ve also put

33:54 – 33:59
an entire list of books and people that we talked about in the interview Jon the full show notes

33:59 – 34:06
page at policybiz, you can check out a more curated list of notes in your podcast provider

34:06 – 34:11
app. But if you want the full list, you can go over to And if you would be so

34:11 – 34:16
kind as to rate and review the show on your favorite podcast provider, I would really appreciate

34:16 – 34:22
it. That enables me to find better and more guests to bring to you so you can learn more about

34:22 – 34:27
data and data visualization. So until next time, this has been the PolicyViz this podcast. Thanks

34:27 – 34:32
so much for listening. A number of people helped bring you the Policy of this podcast. Music

34:32 – 34:37
is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created

34:37 – 34:42
with assistance from Sharon Satsuki Ramirez, and each episode is transcribed by Jon Transcription

34:42 – 34:46
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34:46 – 34:52
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