Summary
You know Enrico Bertini, right? Writer, teacher, co-host of the Data Stories podcast, Enrico does it all. Now at Northeastern University, I invited Enrico to the show to talk about his research, great Substack newsletter, and for views on the evolving landscape of data visualization on social media. In our discussion, Enrico emphasized the significance of interdisciplinary collaboration at Northeastern University. He has some concerns about the current state of visualization theory and tools and talks about his ideas around “critical data thinking” as a crucial way of thinking about data visualization, highlighting the challenges of data accuracy and interpretation. We also talk about Enrico’s teaching methods to help students improve their data interpretation and data visualization skills. Enrico and I share some of the same feelings about the shifts in social media use in the dataviz community, and how it has led to a loss in diverse intellectual exchanges, underscoring the importance of finding new ways to foster community engagement and creativity, including through writing platforms like Substack and LinkedIn.
Topics Discussed
- The Current State of Visualization Theories and Tools: Enrico critiques the prevalent theories and tools in data visualization, calling for a more systematic and thoughtful approach to both creating and interpreting visual data.
- Challenges of Presenting Accurate Data: Our conversation delves into the difficulties faced in presenting precise and accurate data, especially highlighted during the COVID-19 pandemic, and how these challenges have impacted the field.
- Impact of Social Media Platform Shifts: A significant focus of our conversation is on the changing landscape of social media platforms, particularly the decline of Twitter as a crucial space for professional exchanges within the data visualization community.
- Reflections Prompted by the Pandemic: Enrico reflects on the pandemic’s role in helping him reevaluate his work and teaching practices, which is a helpful insight into how his creativity has changed and adapted over the last few years.
- The Role of Newsletters in Idea Refinement and Audience Connection: Enrico shares insights into how newsletters have become a pivotal tool for refining ideas and connecting with a diverse audience, including students from various disciplines, fostering a richer, more engaged community.
- Interdisciplinary Collaboration for Innovation: Highlighting the value of interdisciplinary collaboration, especially at Northeastern, this week’s episode showcases how interactions between computer science engineering students and design peers, as well as varied problem-solving approaches from faculty members, can lead to fresh insights and propel the field forward.
Resources
Guest Bio
Enrico Bertini is an Associate Professor at Northeastern University with a double appointment between the Khoury College of Computing Sciences and the School of Art and Design. Dr. Bertini holds a PhD in Computer Engineering from University of Rome, La Sapienza and he has been active in visualization pedagogy, research, and dissemination for almost 20 years, publishing research papers, delivering university courses, and building bridges between academic research and practitioners on social media and podcasts. He is the co-founder of the Data Stories podcast and, more recently of the FILWD newsletter on Substack, where he writes about how to think effectively with data and data visualization.
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Transcript
00:12 – 00:14
Welcome back to the PolicyViz Podcast.
00:14 – 00:17
I’m your host, as always, Jon Schwabish.
00:17 – 00:22
And I am excited to have a special friend with me this week on the show, Enrico Bertini, now
00:22 – 00:29
at Northeastern University, comes by to talk about his work, his research, his newsletter, and
00:29 – 00:33
the quickly shifting landscape of data visualization social media.
00:34 – 00:38
Now if you don’t know Enrico Bertini, well, I don’t know what to say.
00:38 – 00:41
Maybe you just got started in the field and you don’t know him.
00:41 – 00:47
But you should because Enrico was one of the first people that I discovered when I got started in data visualization.
00:47 – 00:54
At the time, he was at NYU, and now he’s at Northeastern, which is growing by leaps and bounds
00:54 – 00:57
in their information visualization, media journalism fields.
00:57 – 01:02
It’s really interesting about how they’re sort of bringing all these different fields together.
01:02 – 01:04
You’ll hear more about that in the conversation.
01:05 – 01:08
But Enrico is the host of the data stories podcast.
01:08 – 01:17
He has a new Substack newsletter, really thoughtful guy about how we communicate our data, how
01:17 – 01:23
we think about reading other people’s data visualizations, and just a whole, bevy of research,
01:23 – 01:27
in the field that if you’re a practitioner, maybe you haven’t really thought about a lot, but
01:27 – 01:33
Enrico is doing a lot of work of trying to bring some of that data visualization research to
01:33 – 01:36
those of us who are not really, you know, neck deep in the field.
01:36 – 01:39
And so it’s great to have someone who can kind of be that translator.
01:39 – 01:45
So I think this conversation will be useful for those of you who may not be as familiar with,
01:45 – 01:48
especially, the data visualization research field.
01:48 – 01:54
And also I think will be useful for thinking about how the changing landscape of social media
01:54 – 02:01
impacts where the data visualization field is today and where it’s going to head over the next few years.
02:01 – 02:06
So and plus, you get to hear Enrico’s Italian accent, which is always just enjoyable.
02:06 – 02:08
So Enrico and I go way back.
02:08 – 02:09
We have a great conversation.
02:09 – 02:13
It’s a lot of fun, and I hope you’ll enjoy this week’s episode of the podcast.
02:13 – 02:23
So without further ado, here is my conversation with Enrico Bertini. Holy moly. It’s Enrico Bertini.
02:23 – 02:29
I wish I could’ve gotten a rhyme there real quick, but I what’s up, friend? Long time no see.
02:30 – 02:33
It’s been way too long. Way too long.
02:33 – 02:44
I mean, I think since the last time I saw you in person, we were in New York eating at a restaurant outside around other people.
02:44 – 02:45
It was the good life.
02:50 – 02:54
The world has changed dramatically. You moved. Yep.
02:55 – 03:00
We each got 74 years older. Yeah.
03:00 – 03:02
It’s good to see you.
03:02 – 03:03
We don’t look like
03:03 – 03:04
No. No. No. No. No.
03:04 – 03:10
We our our hair is just as dark and silky as before and have just as much of it.
03:10 – 03:13
I mean, there’s no extra poundage anywhere. Yeah. It’s
03:13 – 03:14
Not at all.
03:14 – 03:16
We look the same. We haven’t aged a day.
03:17 – 03:18
Not at all.
03:18 – 03:19
Not at all.
03:20 – 03:20
Great
03:20 – 03:25
to see you again. You were at Northeastern doing a whole bunch of stuff.
03:25 – 03:27
Why don’t we start with your move?
03:27 – 03:29
Tell me about the move up to Northeastern.
03:29 – 03:31
You’ve been there, like, 2 years or so. Yeah.
03:31 – 03:36
Like, it and it’s it’s like the growth there is is incredible, the people that that have been hired there.
03:36 – 03:38
So tell me about tell me about that experience.
03:39 – 03:41
Yeah. So I’ve been here for 2 years now.
03:41 – 03:44
It feels like time passed, like, in a second.
03:45 – 03:52
After spending basically 10 years at NYU in New York City, and, it’s been interesting.
03:52 – 03:58
I think it’s, I had a really good time at NYU. Nothing really to complain.
03:59 – 04:04
I think what is interesting here is that the school is really investing in, in visualization
04:04 – 04:06
in a really big way.
04:06 – 04:12
So it’s becoming a really interesting place to be if you work in that space. Right.
04:12 – 04:20
And one of the things that is really special here for me is that there are data visualization
04:21 – 04:24
people in at least 2 different schools.
04:24 – 04:30
So we have people in the, let’s say, in the computer science space. Mhmm.
04:30 – 04:32
And we have people from the art and design space.
04:32 – 04:37
In fact, my position here is across the 2 colleges, which is really interesting.
04:38 – 04:38
Yeah.
04:38 – 04:41
And, so I think this is really unique.
04:41 – 04:48
And this also means that even just with the faculty that we had even before I joined, there
04:48 – 04:53
was a really interesting group of people coming from different a different background.
04:53 – 04:53
Right.
04:53 – 04:56
And now the school is investing in a big way in this area.
04:56 – 05:01
So since when I joined, other people from visualization Jon, and it’s growing.
05:01 – 05:07
So it’s it’s it’s kind of ridiculous, honestly, but in a good way.
05:07 – 05:12
Yeah. And what have you been doing research wise?
05:12 – 05:16
I mean, I I know there’s this paper you have with, Steve Franconeri and I think your graduate
05:16 – 05:19
student Raquel that we can talk about. But, like, what Yeah.
05:19 – 05:22
How has what are you doing research wise?
05:23 – 05:30
Okay. So research wise, I would say, you know, when when you move, you you start thinking about
05:30 – 05:31
your work, you know, in a much deeper way.
05:31 – 05:33
You have some time to think.
05:35 – 05:40
It’s always hard to summarize, but I would say so for for for many years before moving here,
05:40 – 05:44
I’ve been doing research across machine learning and data visualization.
05:45 – 05:52
So that’s more the the more core computer science type of thing, and the focus there was like, hey.
05:52 – 05:58
How we have these really complex models, and visualization seems to be the the really interesting
05:58 – 06:05
tool to help people understand what these models do, how they behave, and what the elements
06:05 – 06:07
of the models, what roles they have.
06:07 – 06:10
So really look inside or look outside the model.
06:10 – 06:15
We spent quite a number of years trying to do research in that space.
06:16 – 06:23
And now that I moved here, I’m still interested, but, in in a in a different way.
06:23 – 06:29
I think in the meantime, we have large language models, okay, came to be seen, and a lot is
06:29 – 06:31
happening in in general in AI.
06:32 – 06:35
So I’m kind of, like, a little disoriented that I’m in a phase where I want to understand what
06:35 – 06:37
I wanna do next in that
06:37 – 06:38
space. Yeah.
06:38 – 06:46
And then I would say the the other area of research is more like, this is a very general term,
06:46 – 06:51
but I would say data visualization theory in some sense. Yeah.
06:51 – 06:56
I’m really interested in trying to make really basic progress in data visualization, trying
06:56 – 07:00
to ask the questions that we are not asking.
07:00 – 07:01
Looking around is there, what are
07:01 – 07:05
the basic things that we are not looking at? Right? Trying to
07:05 – 07:10
get at the low hanging fruits that nobody seems to find somehow. Right?
07:10 – 07:15
I don’t know if this metaphor works. And, I don’t know.
07:15 – 07:17
I spent quite a lot of time thinking.
07:18 – 07:22
I developed over the years a dissatisfaction with the theory that we have. Right?
07:23 – 07:28
When you go back to the basic question of how do you visualize this? Right?
07:28 – 07:32
And, we don’t have a lot of tools to answer that question.
07:32 – 07:37
So if you go to a lot of practice, you become proficient. Right?
07:38 – 07:43
But if you want to unpack what is it that you learn when you go through a lot of practice, we
07:43 – 07:50
don’t have a lot of pedagogical tools to transfer that knowledge in a in a better way to to students. Right?
07:51 – 07:54
Or ways to think about visualization in a more systematic way.
07:54 – 07:58
So we have a few models here and there, but I’m not too satisfied about that.
08:00 – 08:02
And, yeah, I could go on forever.
08:02 – 08:06
But I would say these are the two main strands of research that I’m
08:06 – 08:08
that I’m working on here. And I can give
08:08 – 08:09
you more details, of course. Yeah.
08:09 – 08:14
Yeah. What I think is interesting on the so we can talk about the the pedagogy in in a second
08:14 – 08:16
because I I think that’s it is interesting.
08:16 – 08:18
I get asked once in a while to, like, hey.
08:18 – 08:22
Could you write a book or something, some sort of tool where it would be like a flowchart that
08:22 – 08:26
would help you, like, pick the graph for the data that you have.
08:26 – 08:29
But, like, I don’t know if that I don’t even think that exists. But Yeah.
08:30 – 08:34
What I found interesting about your Substack newsletter, which we can we can talk about is Yeah.
08:34 – 08:41
You’ve been focusing a lot on kind of core principles of working with data.
08:41 – 08:45
Like, it hasn’t even really been about the visualization piece.
08:45 – 08:45
Oh, yeah. That’s
08:45 – 08:50
data piece. And, like, you feel like that’s a missing part of the whole field?
08:50 – 08:53
You put it much better than I. Right? Yes.
08:54 – 08:57
Mostly because I’m frustrated that.
08:58 – 08:59
Let me take a step back.
08:59 – 09:09
So when we talk about visualization, we tend to talk about the problem of how do you visually represent information. Right?
09:09 – 09:17
So that step going from data to something that is graphical somehow is what we focus on. Right?
09:17 – 09:24
But most of the problems that exist in visualization don’t depend exclusively from that step.
09:24 – 09:24
Yeah.
09:24 – 09:27
Right? And I always felt that well, always.
09:27 – 09:33
I started feeling that we could go on discussing whether a pie chart is better than a bar chart.
09:33 – 09:39
At some point, it’s not that useful because there are many other problems before you even get to that step. Right?
09:40 – 09:45
I started thinking about visualization as one component of a much larger problem.
09:46 – 09:52
And the much larger problem, I start thinking about it as how do you think with data. Right? Yeah.
09:52 – 09:57
I think the problem of how do you think with data is the larger problem. Right?
09:57 – 10:01
How do you think with data, and how do you then communicate data? Right?
10:01 – 10:08
But the general whether you are the reader of a data visualization or or the producer of a data
10:08 – 10:10
visualization, you still have the same problem.
10:10 – 10:14
How do you think effectively with data?
10:14 – 10:22
And, of course, visualization plays a major role there because it’s the one of the best tools to help you think. Right?
10:23 – 10:27
But what happens when you are thinking with data and with data visualizations?
10:27 – 10:31
I don’t think we have a really good understanding of what happens there. Right? Right.
10:31 – 10:34
And we don’t tend to talk about thinking.
10:34 – 10:37
We tend to talk about how do you design this thing right?
10:37 – 10:40
And there’s nothing wrong in that. It’s super important.
10:41 – 10:41
Right.
10:41 – 10:46
But there’s there’s a lot of focus on how do you design this thing right. Right?
10:46 – 10:50
Whereas I think it’s really important to start thinking, how do you think with data?
10:50 – 10:56
Because both designers and readers need to know how to think with data.
10:56 – 11:02
And, yeah, I think that’s one of the main ideas that I try to explore in the newsletter.
11:02 – 11:09
Yeah. And do you think that’s something I struggle with the thinking with data and also, like,
11:09 – 11:16
the uncertainty literature to these other pieces because there’s, like Yeah. Statistical literacy, numeracy.
11:16 – 11:21
Like, is it all is it restricted just for the people who are, you know, neck deep in data, or
11:21 – 11:22
does that apply to everybody?
11:22 – 11:27
And how do we get more people to understand and think think with data in that way?
11:27 – 11:31
No. I think look. Maybe maybe. I’m now thinking.
11:31 – 11:40
I unconsciously, I’ve been I’ve been traumatized by by the COVID pandemic. Right? Because we’ve been flooded.
11:40 – 11:44
I mean, of course, everyone has been traumatized one one way or another, but now I’m I’m referring
11:44 – 11:46
to to data the Yeah. To data. Yeah. Right?
11:47 – 11:49
It’s like we’ve been flooded.
11:49 – 11:54
All in a sudden, it’s been not only one of the biggest events in in in human history, but one
11:54 – 11:58
of the biggest events in data history. Yeah. Right.
11:58 – 12:03
Because we’ve been flooded with data statistics, and data visualizations. Right?
12:03 – 12:14
And the point is that a lot of that flooding has been so poorly done, so poorly communicated, and so poorly interpreted. Right?
12:14 – 12:24
And even a person like me who, I I think I can reason pre pretty well with with data visualizations.
12:25 – 12:27
I’ve been If your if your students are listening, he can
12:27 – 12:30
he can read it with data. Yeah. Yeah. Right.
12:30 – 12:35
So I’ve been I I think I’ve been influenced by that.
12:36 – 12:43
And so to answer your question, I think it’s crucial for people to be able to reason with data
12:43 – 12:50
and also to evaluate whatever they receive critically. Right?
12:50 – 12:50
Mhmm.
12:50 – 12:58
So in the back of my head, I I always have this idea of coming up with something called critical data thinking. Right?
12:58 – 12:58
Yeah.
12:58 – 13:02
In the sense that it’s critical thinking with data. Right?
13:02 – 13:02
Yeah.
13:02 – 13:11
Yeah. And, we are. So point 1, it’s very hard to do even if you have been working with data for many, many years.
13:11 – 13:18
You are constantly humbled by how easy it is to get fooled with data. Yeah. Right? Yeah.
13:18 – 13:25
And in general, I think that if you look even at advanced societies that have a very high level
13:25 – 13:29
of literacy, most people don’t really know how to think with data.
13:29 – 13:30
I I think that’s a fact.
13:30 – 13:32
I don’t have good numbers to show. I think You
13:32 – 13:33
don’t have data on that.
13:33 – 13:36
I don’t have the data to to show them.
13:36 – 13:41
I I suspect suspect I suspect that there’s a very large segment of people, even highly educated
13:42 – 13:48
people, that are not that good at interpreting things with data. Right?
13:48 – 13:54
And we are not even talking about doing their own analysis. Right? That that’s way beyond.
13:55 – 13:55
Right.
13:55 – 13:59
Right? Right. So I think that’s my main point there.
13:59 – 14:00
Yeah.
14:00 – 14:03
So I think answering your question is not only for experts.
14:03 – 14:10
In fact, I think that what is really interesting about this this world is that, there’s a lot
14:10 – 14:16
to do for for the for the population at large. Right. Right? For the people. Yeah.
14:16 – 14:22
I mean, for me, I I I go even I I think a lot of my thinking has evolved over the last, you
14:22 – 14:28
know, 2, 3 years on just is the data that I tend to work with, right, those socioeconomic demographic data.
14:29 – 14:29
Yeah.
14:29 – 14:31
Is it measuring what we think it’s measuring?
14:31 – 14:33
Is it is it accurate?
14:33 – 14:36
Is it capturing the right the bright people in the right groups? Like, I just Exactly.
14:36 – 14:44
My faith in, like, the data that we use all the time is shaken, and that I I just I’m not sure
14:44 – 14:49
it tells us what we think it tells us, and that is a problem.
14:50 – 14:54
And then to the to the DataViz side, I struggle with this a lot.
14:54 – 14:59
I was actually I gave a talk a couple weeks ago at the Data DC meetup, and I had this little
14:59 – 15:04
physical DataViz thing where people could make little pie charts out of their average beverage
15:04 – 15:06
consumption over the course of the day. Right?
15:06 – 15:12
So but but while people are sort of hanging out, there was this conversation about how bad pie charts were. Right? It’s this whole, like
15:13 – 15:13
Here we go. This whole
15:14 – 15:17
right. This this whole, like, ufty thing that, like, we gotta get over it.
15:17 – 15:22
And and I before I did my talk, I asked people why they think it’s a bad pie chart.
15:22 – 15:28
And, like, a couple of responses were, oh, well, because you can’t really figure out the slices accurately.
15:28 – 15:32
And my response to that wasn’t even about any discussion about the chart itself.
15:32 – 15:38
It was, do you think the data in those charts are even accurate? Right?
15:38 – 15:42
Like like, if I asked you what percent of your beverage consumption in the course of the day
15:42 – 15:44
is coffee, you would give me a number.
15:44 – 15:45
But, like, is that number accurate?
15:45 – 15:50
Like, you know, it’s like so there’s there’s just kinda maybe a false precision that we that
15:50 – 15:56
we often think about that it comes all the way back to, I think, the data that we have at the beginning of the day.
15:57 – 16:03
Totally. Look. There there so last semester, I started teaching a new course.
16:04 – 16:12
And in the context of this new course, I prepared the whole module on on basically this this specific problem. Right? Mhmm.
16:12 – 16:18
And, and it was inspired by something I read in one of Ben Jones’ books.
16:19 – 16:25
I think it’s called data pitfalls. Something with data pitfalls. I’m sorry.
16:25 – 16:26
I don’t remember exactly the title.
16:27 – 16:29
And he has this really nice chapter
16:38 – 16:44
between what’s in the data and what what’s the reality described by the data.
16:44 – 16:49
And, again, this is one of those areas where I think there’s so much more to do, so much more. Right?
16:49 – 16:58
Because if the numbers don’t represent the thing you think they represent, all the rest is completely useless.
16:58 – 17:00
Matter. Right. It doesn’t matter. Doesn’t matter. Yeah.
17:02 – 17:05
And this is this goes back to what I was saying before.
17:05 – 17:11
You can decouple data visualization from these specific notions. Right?
17:11 – 17:16
It’s it’s all these things are glued together, which I guess is basically what you were trying
17:16 – 17:19
to say a moment ago. Right? Yeah.
17:19 – 17:25
And so when you teach visualization from this point of view, from the interpretation point of
17:25 – 17:28
view, right, you have to go through all these steps.
17:28 – 17:28
Right.
17:28 – 17:35
And, again, I feel like we haven’t been talking about these steps much in the past, and this
17:35 – 17:37
is why I’m so excited about it.
17:37 – 17:41
Yeah. So I mentioned your your newsletter a little while ago.
17:41 – 17:47
I’m curious what you think about and you and you mentioned your evolving thinking over time
17:47 – 17:56
and moving to a new, university, but I’m curious what you think of the Data sort of community writ large now.
17:56 – 18:04
I mean, things have changed pretty dramatically, pandemic, kinda destruction of Twitter. Yeah.
18:04 – 18:08
Where are you right now in this whole changing world?
18:10 – 18:18
I think the the Twitter thing has been a really major event from for our community.
18:19 – 18:20
Yeah.
18:22 – 18:27
I don’t know. Well, it’s always hard to say if our community is the visualization community.
18:27 – 18:30
Maybe there are there are multiple communities. Right?
18:30 – 18:30
Right.
18:30 – 18:34
But I’m Yeah. I’m gonna assume that there’s a thing like our community.
18:35 – 18:40
And our community felt, I don’t know, Twitter just kind of crumbled.
18:40 – 18:47
And looking back, I invested, what, 10, 15 years on that platform. Yeah.
18:47 – 18:54
And I got to know most of the great data visualization people there from very early on. Right? Right.
18:54 – 18:59
And I never realized how important this platform was for me.
18:59 – 19:06
But when it crumbles, it’s like, holy can I swear on your podcast? Holy
19:10 – 19:11
shit. It’s it’s You’re like, wait.
19:11 – 19:13
This isn’t the data stories podcast.
19:13 – 19:15
I used to swear all the fucking time on my
19:18 – 19:23
podcast. Right? So, holy shit. It’s gone.
19:23 – 19:28
And when it’s gone, now there are there are a number of connections that are gone. Right?
19:29 – 19:34
But there’s also all that beautiful conversation that used to happen in the back channel somehow.
19:34 – 19:35
Yeah.
19:35 – 19:39
And and it was so rich, so rich. Right?
19:40 – 19:45
Especially now that it’s no longer there, I realized that one of the big two values.
19:45 – 19:51
One was that where people from so many different backgrounds that once this thing crumbled,
19:51 – 19:56
you go back to connecting to people that are more similar to you, and you no longer have access
19:56 – 20:03
to all these intellectual, interesting intellectual contributions that you get from people that
20:03 – 20:06
think in a very different way from the way you think.
20:06 – 20:06
Yeah.
20:06 – 20:12
Right? That’s one thing. And the other I forgot the other one that I wanted to say.
20:14 – 20:18
I think, yeah, there are some of these connections you only have on social media. Right?
20:18 – 20:22
I mean, me and you, we connect since 10 years. Yeah. Yeah. Right?
20:22 – 20:26
We I I could literally just, call you on the phone if I want to. Yeah. Yeah. Yeah.
20:26 – 20:32
But I don’t have the same level of connection with with with many people with whom maybe have
20:32 – 20:34
been interacting even for many years on Twitter.
20:34 – 20:36
And once they’re gone, they’re gone.
20:36 – 20:40
So I think that was a major event for our for our community.
20:41 – 20:45
And now I’ve been discovering LinkedIn somehow.
20:46 – 20:51
And it seems like for whatever reason, many people are there, and it feels different.
20:51 – 20:56
And at the beginning, I was put off by the fact that it feels different.
20:56 – 21:01
But I’m like, wait a minute. That’s different. Let’s explore it.
21:01 – 21:06
And it’s more corporate, of course. Mhmm.
21:07 – 21:13
But it also it seems like it gives us access to a group of people that otherwise wouldn’t have
21:13 – 21:18
seen our work if we were stuck on Twitter. Right?
21:18 – 21:28
And, I also feel like that on Twitter, the personal and the professionals seem to mix a lot. Yeah. Yeah.
21:28 – 21:32
And for many years, I thought that it was a really good thing because it makes it fun.
21:32 – 21:35
It makes it more more true in a way.
21:35 – 21:38
You know, people know more who you are. Right? Who you are. Yeah.
21:38 – 21:42
LinkedIn is a LinkedIn is a little bit more uptight. Right?
21:42 – 21:44
My name is on LinkedIn.
21:44 – 21:46
Yeah. Yeah. Yeah. That’s right.
21:46 – 21:50
It’s way more my boss. It’s more professional. Right?
21:50 – 21:51
100%. Yeah.
21:51 – 21:55
And I thought it was a limitation, but maybe it’s not a limit.
21:55 – 22:01
It is a limitation, but I also like that people feel a little bit more cautious because Twitter
22:01 – 22:06
used to go we used to go a little bit overboard with the personal. Right?
22:06 – 22:06
Right.
22:06 – 22:08
And I miss that aspect.
22:08 – 22:16
But on the other hand, I also like the fact that we don’t we don’t get to discuss crazy things all the time. Yeah. Right?
22:16 – 22:18
It’s it’s it’s way more balanced.
22:19 – 22:24
Right? I also say the the back and forth, I think, seems less on LinkedIn.
22:24 – 22:29
LinkedIn feels more one way to me. I post the thing.
22:29 – 22:32
There’s not the same sort of conversation that happens.
22:32 – 22:33
And I don’t know why that is.
22:33 – 22:36
It might just be the way the the platform works.
22:36 – 22:41
The algorithm sort of moves things up and down. Yeah. Yeah.
22:41 – 22:46
Yeah. And I think another aspect going back to your original question because I’ve only been
22:46 – 22:47
talking about the social media aspect.
22:48 – 22:52
I think what happened is that during the pandemic, many people just went back to reflecting
22:52 – 22:55
about what they’re doing and how they’re doing it.
22:55 – 23:01
And I feel like there has been some kind of visualization fatigue where people didn’t really
23:01 – 23:09
feel like going straight back to the same conversations, the same kind of work, the say I I’ve
23:09 – 23:15
been hearing from many people like, do I really want to do more of that? Right?
23:15 – 23:16
Right.
23:16 – 23:21
And I I felt it for a while, and luckily, I am back to finding a new angle, right, which is
23:21 – 23:23
basically what we just discussed.
23:23 – 23:30
But I can imagine I can understand why some people doing visualization for 10 plus years, then
23:30 – 23:35
the pandemic hits, then they all in a sudden, they have a way to reflect about what they’re doing.
23:35 – 23:38
And they they don’t feel like there there’s more to say in that space.
23:38 – 23:40
So I think that’s another element that
23:40 – 23:40
Yeah.
23:41 – 23:49
I feel there’s a sense that there’s not much more to say in this space unless you stumble into something new. Because, again, Yeah.
23:49 – 23:55
We started with, do do we want to talk about pie charts for the next 10 years? Probably no. Right?
23:55 – 23:56
Yeah. Yeah.
23:56 – 23:58
So I think that’s another element in the air.
23:58 – 24:02
Oh, that’s right. I think I think all the things you said, I totally agree with.
24:02 – 24:07
I I think the other thing that I find distressing about the kind of collapse of the Twitter
24:07 – 24:17
community is finding, peep individual people, individual freelancers, new people to the field doing neat, cool stuff.
24:18 – 24:18
Yeah.
24:18 – 24:25
And I I don’t know what it is, but I don’t I I feel like my feeds now are like, my my my Twitter
24:25 – 24:28
feed is all just like the news organizations I follow. There’s no way
24:28 – 24:30
to It’s horrible. Feeding. It’s horrible.
24:30 – 24:35
And so Yeah. So I feel like all the visualizations, like, that I sort of collect and curate
24:35 – 24:41
for teaching or for writing are all from, like, the major news organizations that I read every day. Right?
24:41 – 24:42
The Times and The Post. And
24:43 – 24:43
Yeah.
24:43 – 24:49
And I I feel like I’ve lost that, that maybe that fresh eye or something like that. I don’t know.
24:49 – 24:55
And that and and that makes me sad too because, like, that was a lot of the excitement. Right? Someone tries something new.
24:56 – 24:59
Not that not that the folks at least major news organizations aren’t, but, you know, someone
24:59 – 25:02
new to the field try something new.
25:02 – 25:06
And like you said, sometimes they get bashed on they would get bashed on Twitter.
25:06 – 25:10
But but, oftentimes, it was like a celebration of, like, look at this new thing that someone’s doing.
25:12 – 25:19
I totally agree. I I I do think that we lost something really, really valuable. Yeah. I’m still processing it.
25:19 – 25:20
I don’t know what to do
25:20 – 25:21
about it.
25:21 – 25:22
Yeah. No. Yeah.
25:22 – 25:23
I don’t either.
25:24 – 25:26
It seems like a big loss, honestly.
25:26 – 25:27
Yeah. No. I I agree.
25:28 – 25:33
And I know lots of people complain about, you know you know, Twitter being a cesspool and and
25:33 – 25:34
and bad stuff about it.
25:34 – 25:36
And, you know, obviously, there are parts of it.
25:36 – 25:40
But but for me, you know, staying in within the commute the data community, like
25:40 – 25:43
Look. I didn’t want I didn’t wanna leave. I’m still there.
25:43 – 25:45
I’m just not that active.
25:45 – 25:49
If if only that’s sad that everyone comes back, I’m I’m ready to go back to the party.
25:51 – 25:55
I checked it a few days ago, and someone had posted, like, a picture from the Tableau conference
25:55 – 25:57
last year, and I had, like, 40 notifications.
25:58 – 26:00
I was like, oh, yeah. It’s great. Yeah. Yeah. Yeah.
26:00 – 26:04
But then there was, like, thrown in there was, like, the NFT stuff and, you know Yeah.
26:04 – 26:07
I wanna wake up from the bed bed dream.
26:07 – 26:13
Right. Right. So so tell me about the the newsletter. So Yes.
26:14 – 26:20
Your your newsletter is really interesting because a lot of it is your it’s not necessarily
26:20 – 26:25
like, stream of consciousness, but it’s your kind of ruminations on things.
26:25 – 26:30
And some of it feels unfinished in not in the writing, but in the thought because you’re clearly
26:30 – 26:36
developing the thought, and and those posts sort of end with, like, well, what do you think? So Yes.
26:37 – 26:39
So I guess two questions there.
26:39 – 26:44
How are you thinking about approaching the newsletter, and are you having people write back to you?
26:44 – 26:45
Are you having any conversation there?
26:45 – 26:51
Or is that I have not found that to be the case on Substack, but I’m curious if you’re having that that experience.
26:51 – 26:56
But I think the more important question is, you know, how are you how are you thinking of writing,
26:56 – 26:59
and what’s your thread and and all that? Yeah. You know?
26:59 – 27:04
Yeah. I well, I find that the best way to think is to write.
27:05 – 27:06
So that’s that’s the main thing.
27:06 – 27:11
It forces me to to to go deeper into my thinking.
27:11 – 27:19
So in a way, one of the reasons why I started the newsletter is because I felt that if I could
27:19 – 27:25
write and the ideas that I have in mind, I could become more precise. Mhmm.
27:25 – 27:30
And I also accepted, very early on, I accepted the idea that these are initial thoughts, and
27:30 – 27:36
nothing prevents me to go back to it and write another post in the newsletter that is either
27:36 – 27:39
a refinement of what I wrote or a follow-up.
27:39 – 27:45
And in fact, many of the posts that I have are like, oh, I’m going back to it from a different angle.
27:46 – 27:48
Like, this happens quite quite often.
27:48 – 27:51
And, so that’s one thing.
27:51 – 27:57
The other thing I’m I really felt that I wanted to build a community around the ideas that I
27:57 – 28:02
have and try to serve people somehow. Right? Mhmm.
28:02 – 28:05
And this is part of the discovery process.
28:05 – 28:09
I don’t think that one starts a newsletter knowing, oh, I wanna target these kind of people.
28:09 – 28:13
It’s for me, it’s more like, I’m gonna start writing.
28:13 – 28:14
I wanna see who shows up.
28:14 – 28:22
And little by little, that’s gonna be a way for me to discover what people want to learn, basically, and what they think.
28:22 – 28:22
Yeah.
28:23 – 28:27
And, also, if they can help me think about these ideas better. Right?
28:28 – 28:36
So when I when I write about these half baked ideas, one of the intents is to see if anyone has some brilliant ideas. Right?
28:36 – 28:42
Or even not brilliant, honestly, just adding some interesting elements in there, and it’s kind of working.
28:43 – 28:50
One thing that I discovered to answer your second question is that almost by chance, I stumbled
28:51 – 28:55
on this synergy between Substack and LinkedIn. Right?
28:55 – 28:58
So in a way, the two things work together.
28:59 – 29:01
And it’s not like I did it on purpose.
29:01 – 29:05
I almost, like, discovered that the two things can go well together. Mhmm.
29:05 – 29:13
So there are some ideas that I shared on LinkedIn that are even more proto can you say prototypical? I guess so. Mhmm.
29:13 – 29:19
Prototypical prototypical then the posts. Right? Mhmm.
29:19 – 29:21
So I post something in there.
29:21 – 29:25
I start getting some comments, and this helps me think about that idea.
29:25 – 29:29
And then I’ll post that idea on the newsletter.
29:29 – 29:33
But then when I post something in the newsletter, I put it in LinkedIn.
29:33 – 29:35
There’s more activity in there.
29:35 – 29:41
So there there’s a little bit of back and forth between these two platforms, and it seems to work. It seems to work.
29:41 – 29:44
I’m still experimenting, but it it seems to work really well.
29:45 – 29:47
Yeah. And what about your your students?
29:47 – 29:52
I mean, I would imagine given you know, you have this kinda sounds like like a joint appointment,
29:52 – 29:58
and the and the department has a mix of design students and computer science students and probably
29:58 – 30:03
journalism students I know and, like, I’m sure all all in between, like, interacting with your students.
30:03 – 30:08
How has that helped you sort of re because I know you early on in the substack, you were writing
30:08 – 30:12
about you were publishing, like, your Jon syllabi and your and your lectures and all that.
30:12 – 30:15
Like, so so how has that helped you sort of refine?
30:15 – 30:19
I know you’ve always done that, but but given that now that you’re in a more of a more kind
30:19 – 30:25
of diverse student body, how’s that helped or maybe not?
30:25 – 30:28
You mean specifically for the newsletter or or in general?
30:28 – 30:30
I guess, kinda just more I mean, this In general.
30:30 – 30:34
Newsletter, but really just general of your your thinking around all these different topics
30:34 – 30:37
because I can see how thinking about data is different than
30:38 – 30:38
Yeah. How do
30:38 – 30:40
I build a, you know, a data vis tool?
30:41 – 30:47
Well, I think, as I said at the beginning, joining Northeastern and giving me an opportunity
30:47 – 30:53
to be kind of, like, 50% in a different school, in the school of art and design, that that was
30:53 – 30:56
a it is a new completely new element.
30:56 – 30:59
By the way, I’m I’m training engineering in computer science.
30:59 – 30:59
Right.
30:59 – 31:04
So I I’ve been always around computer scientists and engineers.
31:04 – 31:14
So coming here is is a discovery process, and this includes discovering design students. And they’re completely different.
31:14 – 31:18
It’s really interesting, the way they think, the kind of skills that they have.
31:19 – 31:27
It’s it’s been my interaction with design students has been extremely positive so far. Extremely positive.
31:28 – 31:36
So first of all, you might think that, I don’t know, somehow design students are not technically savvy. Right?
31:36 – 31:38
They are, At least here.
31:38 – 31:41
I don’t know if this is true in in in all the other schools.
31:41 – 31:42
Yeah.
31:42 – 31:44
Many of them know how to code.
31:44 – 31:49
If they don’t know how to code, they they learn pretty advanced tools.
31:49 – 31:54
So it’s not like they come and they don’t know anything about Right. Technical. Right? Yeah.
31:55 – 32:01
But at the same time, what is interesting is that they if you ask them to this is funny. Right?
32:01 – 32:07
So if you if you stop for a moment in a class and you say, now sketch this. Right?
32:07 – 32:07
Yeah.
32:07 – 32:10
For for a design student, it’s completely normal.
32:10 – 32:11
Right. Right.
32:11 – 32:14
Or you come in class and you don’t have slides. Yeah.
32:14 – 32:18
For them, it’s just it’s it’s completely normal. Right?
32:18 – 32:19
Yeah.
32:19 – 32:23
And when I used to teach only to engineering students, when I tried to do these these things
32:23 – 32:26
in class, they always looked at me like if I was an alien.
32:27 – 32:29
So it takes a little bit say, no.
32:29 – 32:33
I don’t have slides in this class. I’m sorry. Right. Right. No.
32:33 – 32:36
I’m not I’m not teaching. I’m not lecturing today.
32:36 – 32:38
You are gonna do some activities. Yeah.
32:38 – 32:41
You have to sketch with your pen. Right?
32:42 – 32:44
Well, what’s a pen? What’s paper? Yeah.
32:44 – 32:47
Right? But let let me say something else.
32:48 – 32:54
So even beyond students, I think what is interesting here is that now you talk with some of the faculty here
32:55 – 32:55
Mhmm.
32:56 – 32:59
And they think in a very different way. Right?
33:00 – 33:04
And it takes some translation. Right?
33:04 – 33:07
Because we use a different language somehow.
33:07 – 33:11
And that’s really, really interesting because we we need to understand each other, and they
33:11 – 33:14
come to the problem from a very different angle, I have to say. Right.
33:15 – 33:16
From a very different angle.
33:16 – 33:16
Yeah.
33:18 – 33:23
And, it’s challenging, but also intriguing and and reaching somehow because
33:23 – 33:24
Right.
33:24 – 33:29
They they will talk about completely different problems, completely different ways of thinking about data.
33:29 – 33:32
Yeah. Yeah. Yeah. And trying to solve them in totally different ways.
33:32 – 33:33
Completely different.
33:33 – 33:39
Yeah. Yeah. Now have you, embraced the Boston lifestyle?
33:39 – 33:45
Are you, like, you go to Dunkin’ every morning and you’re a big red sox fan and all that, or
33:45 – 33:46
you still got that New York?
33:46 – 33:49
I’m I’m not at that level yet.
33:50 – 33:52
I met a new person.
33:52 – 33:58
I think I was at dinner the other day, and we are talking about I I mentioned the fact that
33:58 – 34:03
it took me a while to to adapt to Boston, kinda like a couple of years.
34:03 – 34:06
And he was like, Boston is an acquired taste.
34:11 – 34:13
Yeah. That’s, like, that matches up. Yeah.
34:14 – 34:20
You know, honestly so when I moved from New York to Boston, my New York friends, they were like, Boston?
34:23 – 34:28
Yeah. Yeah. Seriously? Yeah. You go to the enemy city.
34:28 – 34:30
Like, how can I go to Boston?
34:30 – 34:32
Come on, dude. What are you doing?
34:35 – 34:37
Like, the core New Yorkers. Yeah.
34:38 – 34:39
But, no, I actually like it.
34:39 – 34:44
I I agree that it’s an acquired taste, but I really, really enjoy it now.
34:44 – 34:46
That’s great. That’s great.
34:46 – 34:47
Yeah.
34:47 – 34:49
Well, it’s always great to touch base.
34:49 – 34:51
It’s been far too long.
34:51 – 34:56
I’m glad to get one of these Euro voices back on the show. You know? Yes.
34:56 – 35:02
What did you guys used to call them on the podcast? Exotic. Exotic Euro voices. Right.
35:02 – 35:04
It’s like you and Boris and Robert Casara.
35:05 – 35:05
Yes.
35:05 – 35:07
And, you know, you could just pick out
35:07 – 35:10
the comment from someone. I don’t remember who.
35:10 – 35:10
Yeah.
35:10 – 35:12
Who wrote probably on Twitter.
35:12 – 35:16
I like something like I I love the exotic Euro voices.
35:18 – 35:19
We’d have to find that.
35:19 – 35:21
I bet I bet that’s alive.
35:21 – 35:24
That that’s probably alive somewhere. So Yes. Alright, buddy.
35:24 – 35:25
It was great to see you.
35:25 – 35:27
Thanks so much coming on the show.
35:27 – 35:31
Thanks so much, John. That that’s been a lot of fun, and thanks for having me.
35:32 – 35:34
Thanks, everyone, for listening to this week’s episode of the show.
35:34 – 35:36
Hope you learned a lot from that conversation.
35:36 – 35:42
Before you go, make sure you check out all the good stuff happening on the PolicyViz website
35:42 – 35:46
with the blog and other podcast episodes and lots of other stuff.
35:46 – 35:51
You can also subscribe to my substack newsletter that comes out every other week just before
35:51 – 35:56
this podcast comes out with a draft blog post or some other things I’m thinking about, key insights
35:56 – 36:02
on the podcast, and some other things that I’m thinking about, that I’m reading, that I’m watching, that I’m listening to.
36:02 – 36:07
So just sort of a grab bag of stuff that I think is relevant to those of us working in this
36:07 – 36:10
field of data communication and data visualization.
36:10 – 36:13
So until next time, this has been the PolicyViz Podcast.
36:13 – 36:15
Thanks so much for listening.
36:15 – 36:18
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36:18 – 36:20
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36:20 – 36:22
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36:23 – 36:27
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36:27 – 36:31
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36:37 – 36:40
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