In this week’s episode of the PolicyViz Podcast, I interview Rahul Bhargava from Northeastern University on the topic of data physicalization. We discuss the role of community engagement and societal impact in communicating data and including different people and communities. Our conversation touches upon teaching combined majors at Northeastern and expanding data engagement through Rahul’s participatory art methods. We explore the limitations of visual learning and advocate for including diverse voices via data sculptures and embodied experiences.

Topics Discussed

  • Inclusivity in Data-Driven Society. The episode opens with a discussion on the necessity of inclusivity in our increasingly data-centric world. Rahul shares his insights into how data physicalization can bridge the gap between complex data and diverse community members.
  • Teaching Combined Majors at Northeastern. Rahul gives us a glimpse into Northeastern’s approach to education, emphasizing the value of combined majors that integrate data science with other disciplines.
  • Participatory Art Methods in Data Engagement. Rahul describes his use of art tomake data more accessible and engaging. We talk about the potential of data sculptures and embodied experiences to include those who may not be reached through traditional visual data presentations.
  • Culturally Sensitive Approaches to Data. We discuss the importance ofunderstanding and respecting cultural differences, particularly when working with youth from lower socioeconomic backgrounds.
  • Community Empowerment through Data. Rahul shares his strategies for adapting data collection and dissemination to empower communities, and his use of everyday materials like craft items to make data physicalization more inclusive.
  • Data Literacy and Design Principles. Finally, we discuss on how to build data literacy by employing engaging and thoughtful design principles.


Rahul’s website

Data Culture Group at Northeastern University

Data Theatre as an Entry Point to Data Literacy, Bhargava et al.

Interaction Group for Social Change

Data Physicalization at Urban

Data Cuisine

Guest Bio

Rahul Bhargava is an professor, researcher, designer, and facilitator who works on data storytelling and technology design in support of social justice and community empowerment. He has created big data research tools to investigate media attention, built hands-on interactive museum exhibits that delight learners of all ages, and run over 100 workshops to build data culture in newsrooms, non-profits, and libraries. Rahul has collaborated with a wide range of groups, from the state of Minas Gerais in Brazil to the St. Paul library system and the World Food Program. His academic work on data literacy, technology, and civic media has been published in journals such as the International Journal of Communication, the Journal of Community Informatics, and Digital Humanities Quarterly. His museum installations have appeared at the Boston Museum of Science, Eyebeam in New York City, and the Tech Interactive in San Jose. Rahul has led workshops and made presentations at meetings such as Data for Black Lives, the FHNW Academy of Art and Design, IIIT New Delhi, the United Nations World Data Forum, TICTeC and the Designing Interactive Systems conference.

Rahul is a Principal at Connection Lab, and also an Assistant Professor of Journalism and Art + Design at Northeastern University. Rahul leads Data Culture Group there. The Data Culture Group builds collaborative projects to interrogate our datafied society with a focus on rethinking participation and power in data processes.

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00:13 – 00:18

Welcome back to the PolicyViz Podcast. I’m your host, Jon Schwabish. On this week’s episode

00:18 – 00:23

of the show, we talk about something I’ve been keenly interested in over the last 6 or 7 months,

00:23 – 00:29

which is data physicalization, which is a big jumble of consonants and vowels. But, basically,

00:29 – 00:34

it’s thinking about creating and working with data beyond the screen in the analog world. So

00:34 – 00:41

working with popsicle sticks or Legos or blocks or post it notes. And to help me better understand

00:42 – 00:47

this work and what it means to work with communities and people and groups beyond the computer

00:48 – 00:54

is Rahul Bhargava from Northeastern University who runs his own lab at Northeastern and works

00:54 – 01:00

in this physical world with different people and communities to help them own their own data

01:00 – 01:05

and to help them understand the world around them. And so I think what you’re going to hear

01:05 – 01:11

in this conversation is not only how to think about bringing data visualization into the physical

01:11 – 01:16

space, which I think is interesting on its own. But also, I think what you’re going to hear

01:16 – 01:21

and what you’re gonna learn about is how to engage with communities and how to engage with communities

01:22 – 01:27

in this space of working with collecting and analyzing data. So I think you’re really going

01:27 – 01:35

to enjoy this week’s episode of the podcast. It is sort of pulling together a string of conversations

01:35 – 01:40

I’ve done over the last 6 months. 1st, with the editorial team from the book Making with Data,

01:40 – 01:46

a couple of weeks ago from Dietmar Offenhuber, who wrote the book Autographic Design, and now

01:46 – 01:52

with Rahul, Bhargava from Northeastern. So I think there’s a nice thread here about this type

01:52 – 01:58

of way of thinking about and working with data. So no more talk for me. Let’s head over to the

01:58 – 02:04

conversation between me and Rahul. Hey, Rahul. How are you? Good to see you again. It’s been

02:05 – 02:06

months since I saw you in the summertime.

02:07 – 02:10

Yeah. Yeah. Yeah. It’s been a it’s a different year. So

02:11 – 02:11

It’s a different year.

02:12 – 02:13

Longer than it is.

02:13 – 02:17

Yeah. Right. Yeah. Well, really good to see you again. I’m excited to chat with you about your

02:17 – 02:23

work. I actually got inspired by seeing your talk at the conference we were at in in Maine,

02:24 – 02:29

in in the summertime. And then you actually did, like, a kinda real time data physicalization

02:29 – 02:33

project, like, just just kinda like spur of the moment, which was very I I thought it was very

02:33 – 02:38

cool to sort of like you’re just like, hey. We’re gonna have this break, and, yeah, we’re gonna

02:38 – 02:43

do this thing. So so I thought maybe you could tell folks about your background, the work that

02:43 – 02:47

you do, and then and then the work that you do at your lab at at Northeastern to get us started?

02:47 – 02:53

Sure. Yeah. So happy to share my I actually am what I call a recovering computer scientist.

02:53 – 02:59

So I trained in technology, but then have spent a lot of years trying to unlearn some of those

02:59 – 03:06

lessons, particularly around like ethics and impact in society and those sorts of things. So

03:06 – 03:12

since then I ventured over into, into education and robotics and things like that. And I ended

03:12 – 03:17

up, I think, like a lot of people of my generation who sort of cut their teeth in in the industry

03:17 – 03:25

in the early 2000, sort of stumbled on data science accidentally in a in a time where there

03:25 – 03:29

weren’t really programs that would help you learn how to work with data.

03:29 – 03:29


03:29 – 03:33

And that’s totally different now. But I think a lot of people of my generation, that’s the origin

03:33 – 03:39

story of how they came into working with data in a very computational quantitative kind of way.

03:39 – 03:44

So I didn’t come at it from the designer side. I came at it from that computation side. Now

03:44 – 03:50

I am a professor at Northeastern in journalism and art and design, which is totally different

03:50 – 03:57

than when I started. Yeah. And, and speaks, I think, to how I like to think about the intersections

03:57 – 04:03

between things being where the fun is. Mhmm. So I am a professor here at Northeastern. I get

04:03 – 04:08

to teach students. I do a bunch of research, and I run a lab called the Data Culture Group,

04:08 – 04:14

which really is just a a home to ask the kind of questions I’m interested in, that are about

04:15 – 04:23

how we can, as we shift more to a data society with data impacting civic decision making, company

04:23 – 04:29

organizational stuff, all sorts of pieces of our lives are now touched by information and build

04:29 – 04:35

around it. How can we change who gets to see with the table when that happens? When it happens

04:35 – 04:40

with a spreadsheet and a computer, a certain set of people show up and feel comfortable. And

04:41 – 04:46

that is fine, but that leaves out a whole set of other people. And I think that’s problematic,

04:46 – 04:51

and we can talk some more about why. So I try to create a bigger toolbox for bringing people

04:51 – 04:57

together around data in new ways. And that includes lots of art space, participatory, things

04:57 – 05:02

we can talk about, like data murals and sculpture and theater and things like that.

05:02 – 05:09

Yeah. Are you teaching journalism students and design students and computer science students?

05:10 – 05:15

Exactly. One of the funny things about Northeastern is that they have these combined majors

05:15 – 05:22

where it’s not a double major, but it’s actually a student that is getting a degree in computer

05:22 – 05:27

science and journalism. And that’s what their degree says. So like I mentioned, they’re at the

05:27 – 05:32

intersection. So I Right. In my classes, I get a lot of students that might be, data science

05:32 – 05:40

and journalism or, interaction design and data science or, you know, mechanical engineering

05:40 – 05:43

and graphic design, things like that.

05:43 – 05:49

Right. Right. So the cross cutting of all those different areas. Well, let’s talk about the

05:49 – 05:58

data physicalization stuff you do. From my reading of your work, it really spans a huge breadth

05:58 – 06:05

of types of projects from the big, I have in mind the the table you welded together, I think,

06:05 – 06:10

with your wife, right, of the the silverware, all the way to more of these participatory projects.

06:10 – 06:15

So I don’t know where you’d like to like to start, but maybe maybe you could help, you know,

06:15 – 06:17

give people a sense of the types of things that you do.

06:17 – 06:24

Yeah. Sure. So I think maybe starting with what people know, data visualization, right, uses

06:24 – 06:32

the, like, what we can see to represent data. And I think we tend to privilege sight as a sense,

06:33 – 06:38

just in society in general. We we focus more on what we can see than sort of what we can taste,

06:38 – 06:44

what we can hear, what we can smell, what we can touch. And I just think that’s limiting, not

06:44 – 06:50

just to everyone, but also to people that learn different ways. Mhmm. So one of my approaches

06:50 – 06:56

has been to say, hey. What if we just broaden that spectrum? And, yeah, I still work in the

06:56 – 07:04

visual, but, with the the physicalization as a word you used. And I tend to think about that

07:04 – 07:12

meaning encoding data onto the physical attributes of some object in 3 d. And, I find that word

07:12 – 07:14

hard to say, so I tend to split it up.

07:14 – 07:15

Just Yeah.

07:15 – 07:18

Physicalization. It’s just a lot of syllables.

07:18 – 07:19

Yeah. Yeah.

07:19 – 07:23

And I get the parallel to visualization, but I tend to split that into different buckets Mhmm.

07:23 – 07:29

Partially so it’s easier to say, partially so it’s easier to think about. Right. One bucket

07:29 – 07:35

is what I call data sculptures, and that’s where the data is encoded into some physical object

07:35 – 07:42

that is outside of yourself. So the naive or the simple version is like, say, a 3 d bar chart

07:42 – 07:47

made out of boxes. Right? And I could speak to there’s more creative versions like, you know,

07:47 – 07:52

the table that, that the welded table made out of 1700 pieces of cutlery, which,

07:53 – 07:53


07:53 – 07:57

the way, my wife did the majority of because my welding sucks. All the pieces that fell off, those

07:58 – 07:59

All yours.

07:59 – 08:00

Just to be clear.

08:00 – 08:01

Yeah. Good enough. Alright. Yeah.

08:01 – 08:08

So, so sculptures is one bucket. Another bucket is embodiment. So that’s where you’re using

08:08 – 08:14

your body to represent data in some way in cooperation or collaboration with other bodies. And

08:14 – 08:19

that’s where I’m doing a bunch of work on data theater and using participatory practices from

08:19 – 08:26

the long rich history of theater as a way to bring people together to talk about civic issues

08:26 – 08:31

and imagine alternatives. So I like to split that bucket up, but that’s just kinda like how

08:31 – 08:34

I start to think about that longer term physicalization.

08:34 – 08:35

Yeah. Yeah.

08:36 – 08:41

And then the goals, of course, are for me are really focused on who gets that seat at the table

08:41 – 08:43

and how can we build new ways to offer it to them.

08:44 – 08:48

Right. I’m curious about the the embodiment. So is that can you give an example? Is this like

08:48 – 08:53

taking a group of people and, you know, having them stand in certain areas? Is there are they

08:53 – 08:55

raising signs? What is Yeah. What does that look like?

08:55 – 08:59

Well, the the simplest version I mean, people were doing this stuff in the seventies. They were

08:59 – 09:04

doing, like, living histograms, they call them. So imagine you’re standing up on a ladder and

09:04 – 09:07

you ask a group of people to line up based on height.

09:07 – 09:08

And you

09:08 – 09:12

say use, like, use, like, 3 inch buckets or something. And you can just imagine them lining

09:12 – 09:19

up in rows based on height. And, they they called that a living histogram. Mhmm. And, again,

09:19 – 09:23

that’s, like, a very simple way to imagine people using their bodies. Because if you imagine

09:23 – 09:30

doing that, you’re gonna remember where you are and the people around you far more strongly

09:30 – 09:35

than if you saw a picture of it. Right? Because you there’s all this research into how we learn

09:35 – 09:40

with our bodies. There’s words like embodied learning, somatic learning, and there’s lots of

09:40 – 09:45

different research that gets into why and how that works. But the simplest is just to think

09:45 – 09:52

about any sport you play, like muscle memory, it just works differently than the rationalized

09:52 – 09:54

or abstracted way of knowing things.

09:55 – 09:55


09:55 – 09:57

So it builds on that kind of embodied understanding.

09:58 – 10:05

Right. And so you’re doing this in, in civic meetings with with community groups and and nonprofit CTOs?

10:06 – 10:10

That’s where we’re getting to. I’m partnering with a team. For about 3 years now, I’ve been

10:10 – 10:15

working on the idea of what would it mean to use the lever of theatrical performance. Right.

10:15 – 10:20

So pushing beyond that idea of just lining people up to say, hey, what if we actually give a

10:20 – 10:28

set of people in this case, actors or dramaturgs, people who work with source material and bring

10:28 – 10:29

it into some scene?

10:30 – 10:30


10:30 – 10:36

Give them information about some issue, some civic issue. In the current case, it’s about green

10:36 – 10:41

space planning and displacement in Boston. Mhmm. Mhmm. So very, like, relevant civic issue for

10:41 – 10:48

lots of places. How do we give them something and then have them synthesize and come up with

10:48 – 10:55

a performance of that data, which they use all of their dramatic skills for. Mhmm. And then

10:55 – 11:00

have a debrief conversation afterwards that responds to that. So just take a step back from

11:00 – 11:05

that description. Yeah. Usually in the US, in my area, certainly, the way civic meetings happen

11:05 – 11:11

is there’s like an evening meeting that you get invited to that, you know, it’s probably hard

11:11 – 11:16

to attend if you have 2 jobs or kids. But then there’s a if you go, there’s, like, a big room

11:16 – 11:21

with chairs and there’s a there’s a projector and there’s, like, you know, usually, like, some

11:21 – 11:26

city official and wearing nice clothes. That’s, like, sharing things. And maybe there’s, like,

11:26 – 11:31

1 or 2 things where you can, like, put up sticky notes or, like, they ask some questions. That’s

11:31 – 11:36

and then there’s, like, people then there’s, like, the 3 people who always ask tons of questions.

11:36 – 11:38

Right. Right. Yeah. Companies. Yeah.

11:38 – 11:42

Like, that’s what a civic meeting looks like in our area. Yeah. Now like I already mentioned,

11:42 – 11:46

there’s lots of people left out of that, not just because of the logistics, but also the format.

11:47 – 11:55

Mhmm. So we’re saying, alright. Contrast that to the idea of a 20 minute theatrical performance

11:55 – 12:02

Mhmm. That maybe you know the people in it, and then breakout conversations that react to the

12:02 – 12:07

performance for 20 minutes afterwards. Mhmm. It’s just a very different invitation.

12:07 – 12:09

Yeah. The only way of thinking about it.

12:09 – 12:13

Probably in a different place, a place maybe you’re more familiar with, like the library or

12:13 – 12:19

something like that, or a a theater, it’s probably easier to get people to show up because,

12:19 – 12:25

like, they’re Jon see a play, not to, like, a meeting. Right. This is the hypothesis. So we’re

12:25 – 12:31

working towards that, and this is we just did actually, just this week, we did our 3rd round

12:31 – 12:37

with a wonderful group of youth here at the Heights Square Task Force. Mhmm. So a group of youth,

12:37 – 12:44

in a lower socioeconomic status part of the city We do a ton of activism, and they brought their

12:44 – 12:49

theater experience and wisdom to working on some data about a local park that is a green space

12:49 – 12:54

in that part of the city, and how and why it’s used and what it’s for. It was mostly youth of

12:54 – 13:00

color, and they were responding and working with survey data, which had been mostly filled out

13:00 – 13:04

by people that were white and affluent in the area. Mhmm. So it’s a really interesting mix,

13:04 – 13:10

and we’re working towards creating that alternate model to then do a comparison. That’s a long

13:10 – 13:11

way of saying it.

13:11 – 13:15

Yeah. Well well, it’s interesting too because there’s kinda there are 2 different approaches

13:15 – 13:19

in some ways, right, or or 2 different means. Because on the on the theater one, it’s if I’m

13:19 – 13:27

the attendee, I view this, performance, and then I get to interact with people. But in the second

13:27 – 13:34

one, it’s, I’m really part of the data visualization for lack of a better word. Right? There’s

13:34 – 13:39

more participation in this. So how do you think about the difference between these 2 of the

13:39 – 13:41

sort of more the exhibit versus the participation?

13:43 – 13:47

Yeah. That’s a great question. I tend to argue that if you think about a data pipeline, right,

13:47 – 13:53

people draw them in different ways, triangles, hexagons, lines, circles, whatever. Yeah. They

13:53 – 13:59

often move from, like, asking some questions to producing or gathering some data to finding

13:59 – 14:05

a story, to, like, creating that story and then sharing it with with an audience. I think each

14:05 – 14:11

of those is a point for participation. Whether it’s participatory data collection or participatory

14:11 – 14:18

analysis, brainstorming on stories, doing collective creation of an artifact, or having, like,

14:18 – 14:24

a hosting, like, a launch party. And that isn’t to say that every project needs to engage each of those.

14:24 – 14:25


14:25 – 14:30

But to do what there was a there was a training I took years ago from a local group called the

14:30 – 14:36

Interaction Institute For Social Change, and they had this wonderful concept of a maximal appropriate

14:36 – 14:42

level of participation. So just to pull that apart, like, as much engagement with the group

14:42 – 14:45

of people you’re working with as is right for the project.

14:46 – 14:46


14:46 – 14:50

What’s a simple way to think about that? Well, not every decision needs to be made with consensus,

14:50 – 14:55

but some should. Right? So that’s a simple way to think about that idea. I think about data

14:55 – 15:02

projects very similarly. What’s the right level of participation at which point for this particular

15:02 – 15:06

project with these particular partners? And it looks very different in different ways. So some,

15:07 – 15:13

it’s about getting them to be at the data sharing events, and that’s where you engage them.

15:13 – 15:18

For others, the whole output, the whole desired outcomes are about the process, not what you

15:18 – 15:23

make at the end. So it’s about engaging people in the process. Those are some first thoughts

15:23 – 15:24

about, like, that difference.

15:24 – 15:30

Yeah. That difference. Contextual. Yeah. And when you are working with your students in the

15:30 – 15:36

lab, what what what does that process look like? What are the different, I mean, you’re working

15:36 – 15:40

with students, so you’re not necessarily hiring people. But if, you know, what are the sort

15:40 – 15:46

of different skill sets that you’re looking for, when you are trying to build some of these

15:46 – 15:48

things or pull it together? You know?

15:49 – 15:54

The main thing I pull from is from my my experience after I was sort of ran away from computer

15:54 – 15:59

science a little bit. I did, well, I I graduated basically with robotics experience. And at

15:59 – 16:05

the time, I think if you remember back 25 years, the only robotics jobs you could get were robots

16:05 – 16:10

that blew things up. And I was like, okay. I don’t really wanna blow things up. So what can

16:10 – 16:11

I do with these skills?

16:11 – 16:12


16:12 – 16:18

And so so I went into robotics in education. So I lean on my education experience, and the I

16:18 – 16:25

would say the most useful skill set is around activity design. So if you have a set of goals,

16:25 – 16:30

how do you create an experience, an activity that invites people in with appropriate media,

16:30 – 16:37

like a thing to work with to help achieve that goal with that group of people? Mhmm. So that’s

16:37 – 16:42

a skill set I look for really like, that’s a really it takes a thoughtful person that can be

16:42 – 16:47

in an experience and step out of it and think about the settings and the context. Those are

16:47 – 16:52

the skills that I look for and that I try to practice with myself to get better at the most.

16:54 – 16:58

So that’s one thing that I think is different with this kind of work, because usually, you don’t

16:58 – 17:02

you know, it’s not like like, yes, you need to be nimble and facile with data and information.

17:03 – 17:07

Yeah. And you need to have, like, cultural knowledge about how to move in different settings

17:07 – 17:12

or be aware of your own limitations as a person. But when you’re doing participatory work with

17:12 – 17:18

communities, all of the work I do is with community groups Mhmm. Then that’s, I think, the main

17:18 – 17:20

one that I focus Jon, actually.

17:20 – 17:24

Yeah. So when you are working on a project, you’re working, let’s just say, with a community

17:24 – 17:29

group and you were gonna create, let’s just say, you know, let’s just take this let’s just take

17:29 – 17:34

this community meeting. I think it’s a good good way for for me to think about it. At this community

17:34 – 17:39

meeting, you’re gonna have them work with some data. We’ll just keep it simple. They’re gonna

17:39 – 17:45

work with some data to create something. In your mind, how do you think about that? Do you like,

17:45 – 17:49

what are your steps? Are you thinking about, like, here’s this community. Here’s what we need

17:49 – 17:54

to know about the people in the room. Do you think about what graph we wanna end up with? What

17:55 – 18:01

data? What what tactile pens or paper or Legos they’re gonna have? Like, what is walk us through

18:01 – 18:03

your thought process. Yeah.

18:04 – 18:09

All of those. So almost always, I’m invited in or, like, I have a collaboration with some key

18:09 – 18:16

stakeholder. Right? So there’s a set of there’s a small set of people that are of or have trust

18:16 – 18:21

in the community that I’m working with. And with them, we come up with a plan about what are

18:21 – 18:25

your goals? Like, what is the point of this thing we’re doing? And they might say, like, hey,

18:25 – 18:29

We got funded to do this data thing, and now we need to, like, disseminate it. And we wanna

18:29 – 18:33

do that in a way that matches our nonprofit way of working that’s about empowerment and justice.

18:34 – 18:41

So help us catch up. And I’m like, okay. Cool. Or it might be to say, oh, you know, we’re, we’re

18:41 – 18:45

engaging a group of people that we wanna survey to produce some data about the community. How

18:45 – 18:47

can we do that in a way that doesn’t feel extractive?

18:47 – 18:48


18:48 – 18:54

So that’s when we start to say, alright, What can make people feel comfortable? Is it about

18:54 – 19:02

having, you know, the let me get some visuals. Is it about having, you know, the craft materials?

19:02 – 19:03

Yeah. Yeah.

19:03 – 19:09

That make them feel comfortable. Right? Is it about having, you know, is it about having someone

19:09 – 19:14

that looks like them be the one leading the session? Mhmm. Is it what do you so then that’s

19:14 – 19:20

when you match the toolbox with the goals, and then we come up with a plan. And that might look

19:20 – 19:27

like, you know, using those craft materials or, you know, or Legos to make a data sculpture.

19:27 – 19:34

Who knows? You know? Or it might look like, it might look like brainstorming questions together

19:34 – 19:40

in reaction to, like, a one page data handout. You know, it has some some a couple of tiny tables

19:40 – 19:43

and a bar chart. Who knows?

19:43 – 19:43


19:43 – 19:47

So that’s some of the concreteness of what it can look like in a room. Yeah.

19:48 – 19:48


19:48 – 19:54

It very seldom. It might be a giant paper spreadsheet on the wall that they’re writing information

19:54 – 19:59

on. It very seldom looks like giving them, like, a tablet computer and, like, having them look

19:59 – 20:06

at data on a device. Right. That seldom aligns with the type of work I’m doing. I’m not saying

20:06 – 20:11

that’s necessarily bad. It’s just not where I point my energies. Other people are doing stuff

20:11 – 20:16

like that. There other people are working on technical skill development. Sometimes the goal

20:16 – 20:20

of my work is data literacy building, but it’s not literacy in that way. It’s not computational

20:21 – 20:26

literacy. Because computers don’t help with things like asking a good question. Right? Like,

20:26 – 20:31

that’s not something they’re good at. So that’s something that people are good at. So I tend

20:31 – 20:34

to focus on those things to complement other work.

20:34 – 20:41

Right. You mentioned, at at the very start how site is kind of limiting in in lots of different

20:41 – 20:50

ways and and excludes the broad swath of people. When you are, starting out your exploration

20:50 – 20:55

with a community group, what are your conversations like? You’ve already mentioned different

20:55 – 21:01

communities of color and and, levels of affluence. But when you start talking about sight versus

21:01 – 21:06

certain types of disabilities, what what are those conversations like? I I assume you can’t

21:06 – 21:11

have this big poster board on the wall if you’re working with, you know, people who, you know,

21:11 – 21:15

can’t move around as as nimbly as, you know, my 14 year old son.

21:16 – 21:21

Yeah. And I’ll say straight Jon of the bat, like, that’s actually not a focus of my work. So

21:21 – 21:25

I haven’t been leaning in those directions intentionally. It comes up because I work with regular

21:25 – 21:30

communities and communities are made up of people with different abilities. So I don’t proclaim

21:30 – 21:37

to have expertise there. The thing I will say is that, like, fun is a major design principle

21:37 – 21:43

for me. It’s like a very important one. And one way to get people to have fun is to do something

21:43 – 21:49

together. And maybe it’s cooking, like the wonderful data cuisine work. You know? Maybe it’s

21:49 – 21:55

drawing a picture, sketching. Maybe it’s building with craft materials. Maybe it’s taking a

21:55 – 22:01

walk. Those are all ways to have fun, and each of those can engage with data. And, like, why?

22:02 – 22:07

Not because data is like this amazing thing. You know, data is useful, but it’s because data

22:07 – 22:13

is like a key to power and and impact. Right? In these sorts of settings where now data is being

22:13 – 22:18

used to bring people together to make decisions, if you don’t speak the language of data or

22:18 – 22:24

it’s not presented in a language you you understand, in a way you understand, then you are actively

22:24 – 22:30

disengaged and disempowered. And that’s troubling for me. So what I wanna do is help say, hey.

22:30 – 22:35

Look. There’s a language of power. Right? And you could make the argument about reading literacy

22:35 – 22:40

as the same thing. Right? You need this literacy to access the doorway to power, which are,

22:40 – 22:46

by the way, like reading literacy, typically closed on purpose. Right? Like, oh, no. You know,

22:46 – 22:53

women aren’t allowed to read. You know? Formerly enslaved people in the US cannot be taught

22:53 – 22:59

to read. Right? So there’s a history of things that we call literacies being used to shut the

22:59 – 23:07

door to access to power. So I see that and I say, oh, well, data is is like a new tool that

23:07 – 23:13

has carries a rhetorical power of truth. Right? It looks important and truthy, and there’s a

23:13 – 23:19

lot of history around that. But, so how do we help more people access that so that we can open

23:19 – 23:24

that doorway? That’s a really big deal to me. So that’s a long way of getting back to your question.

23:24 – 23:28

Right? Like, okay. I wanna have that that gets us back to questions of access and accessibility.

23:29 – 23:29


23:29 – 23:35

So so then, like, then we say, alright. Well, what are the capabilities and assets in this community?

23:35 – 23:40

Right? Not the not that deficit framework, but, like, what can we build on in this community?

23:40 – 23:45

And, man, if it’s empanadas, let’s go with empanadas. You know? Like, what can we do with this?

23:45 – 23:45


23:45 – 23:49

Like, you just gotta work with what you have. And I usually when I’m working internationally,

23:50 – 23:54

I usually I usually, like, I’ll get to the place a day early and, like, go around the market

23:54 – 24:00

with people and just try to learn what I can about the media that makes sense to people there.

24:00 – 24:07

And whether it’s smell or touch or materials, I try to sort of at least be cognizant that it exists.

24:07 – 24:08


24:08 – 24:12

Aware that I won’t become an expert in it in a data, but, like, I try to that’s something that’s

24:12 – 24:13

deeply important to me.

24:13 – 24:18

Yeah. Have you ever had an experience where you’ve gone internationally or or wherever to a

24:18 – 24:25

place outside, you know, New England with, an idea in your mind of what an activity might look

24:25 – 24:28

like, and then you get there and have this experience in a day, and you’re like, no. That’s

24:28 – 24:32

not gonna work at all. I have to totally redesign what you had in mind.

24:33 – 24:41

Dude, that’s, like, every single occasion ever. I mean, to a certain extent, that’s why, like,

24:41 – 24:46

I have I always make sure that I’m not parachuting somewhere. Right? That there is a local person

24:46 – 24:52

that I at least know we’re gonna introduce to that can, you know, pull me to the side, smack

24:52 – 24:55

me upside the head and say, hey. Look. This is what that means here.

24:56 – 24:56


24:56 – 25:03

So so you in those settings, I have to carry a deep, like, cultural humility Mhmm. To understand,

25:03 – 25:08

like, there’s there’s a certain set of cultures I feel comfortable in and know well. And there’s

25:08 – 25:14

a large that I do not. Mhmm. So how do you work in those environments? I think you I specifically

25:14 – 25:20

go in with a plan, and then I’m just I’m constantly thinking on my feet, which, again, training

25:20 – 25:22

as a teacher or facilitator helps you.

25:22 – 25:23

It helps. Right.

25:23 – 25:28

When you’re bringing people together around data, you can’t just roll in with your data science

25:28 – 25:32

muscle. You have to roll in with your Jon bringing people together muscle.

25:33 – 25:33


25:34 – 25:41

And and that to me is is is super critical, and reading the room is a big part of that.

25:41 – 25:47

Yeah. I wanna ask on the side, I was really fascinated by this idea of the participation at

25:47 – 25:54

at each stage of the data workflow, whatever, however we wanna call it. But I’m curious on the

25:54 – 26:00

on the data, I guess, collection or data sharing piece of that workflow. If you’re working with

26:00 – 26:05

a group and they are providing their own, data to participate in this group. How do you think

26:05 – 26:11

about, you know, data? I’m gonna put that in quotes because it’s kind of loose in that maybe

26:11 – 26:17

that sense. But, like, data privacy, data security when you’re in this in this group. Do you

26:17 – 26:22

think of it like, oh, we’re in this closed group. This is a closed room. This lives here only.

26:22 – 26:25

Yeah. So how do you how do you wrestle with that?

26:25 – 26:31

Yeah. That’s a really good question. So the first step is just to recognize the historical,

26:32 – 26:39

like, norms that, like, people are used to in the room. Right? Yeah. Data is usually extracted

26:39 – 26:44

from set of people by someone else who has more power. And by power, I mean, like, the ability

26:44 – 26:51

to make decisions about yourself and people around you. Like, it’s usually extracted from someone

26:51 – 26:59

that by someone else and then seldom looped back to engage them on it. So just by by bringing

26:59 – 27:05

data that was produced with the community back to the community, you’re already resetting a power balance.

27:05 – 27:06

Yeah. Right.

27:06 – 27:10

And I think that’s really important because then they’re put in control of decisions about privacy

27:10 – 27:16

and impact. Mhmm. And that I think there’s great examples of that in, you know, in the development

27:16 – 27:20

world where people are rethinking that, like, colonialist extraction for, like, working in,

27:20 – 27:24

you know, places that you would imagine, like, Sub Saharan Africa and things like that. We have

27:24 – 27:30

nice examples of switching that script Mhmm. But it’s still the norm. And it’s not always bad,

27:30 – 27:35

but, like, survey someone and then analyze analyze it. But, like, there’s just a a reduct there’s

27:35 – 27:37

like a there’s a disempowering history there. So

27:37 – 27:38


27:39 – 27:44

Coming back to your question, I try to engage that with the room because culturally, people

27:44 – 27:49

have very different expectations around privacy. And I come at it from, like, a western context

27:50 – 27:57

that is sometimes totally different than than and I run to that in the US as well. Right? When

27:57 – 28:03

I go into different cultures in the US, whether it be like a a like an organization that works

28:03 – 28:12

with, with Latino populations across, you know, some city or, like, a a room of that’s all,

28:12 – 28:14

like, black organizations. The norms are different.

28:15 – 28:15


28:15 – 28:18

So it’s a similar thing about, like, how do we negotiate this together?

28:19 – 28:19


28:19 – 28:25

And, like, working with communities and people that aren’t computers takes longer.

28:26 – 28:27

Yeah. Yeah. Like, it

28:27 – 28:34

just takes longer. So you have to accommodate that and we we tend to focus on speed as a metric

28:34 – 28:42

of success. And that is a very poor metric of success when you’re doing participatory work.

28:42 – 28:43


28:43 – 28:47

It’s actually often a metric of failure. Like, if you did something too fast, you probably didn’t

28:47 – 28:53

build the relationship that you need. So, I know that’s just where sometimes, like, I veer away

28:53 – 28:58

from talking about data in the, like, the sense that data visualization thinks about it.

28:58 – 28:58


28:58 – 29:03

These are the things that matter when you’re doing working with information in real settings.

29:03 – 29:08

Right. So you can’t just it’s easier to put the blinders on and just stare at the computer.

29:08 – 29:13

But once you take them off, you gotta be like, oh, I have to wrestle with all these things.

29:13 – 29:20

Right. Right. I wanna just finish up with, asking you about some of your favorite projects.

29:21 – 29:25

If you have a few that sit in your head, either because they were meaningful to you or they’re

29:25 – 29:31

meaningful to the community or they’re just hell a lot of fun to make. What are what are some

29:31 – 29:34

of the ones that that for you are, like, your most fun ones?

29:34 – 29:40

Well, some well, I’ll I’ll use one that has brought me a lot of Jon in different ways. So the

29:40 – 29:45

table that we discussed a little bit, just to briefly describe, my wife and I coming out or

29:45 – 29:49

or in the pandemic decided to we work a lot on food security like you do.

29:49 – 29:50


29:50 – 29:55

And we decided to make a piece that spoke to the number of people that were fighting against

29:55 – 30:03

it in Massachusetts. So we found this number that almost it was 6, 1659, almost 1700 people,

30:04 – 30:10

Households every day were applying for SNAP benefits in Massachusetts during the peak of the

30:10 – 30:14

pandemic. So they were saying, hey. I need help with food. My household needs help with food.

30:14 – 30:21

Every day, 1700 blew me away because it’s a conceivable number, but it’s still too big.

30:22 – 30:22


30:22 – 30:29

So we collected from our friends and community 1700 almost pieces of cutlery and welded them

30:29 – 30:31

into this full sized table.

30:32 – 30:32


30:32 – 30:39

And the reason I wanna talk about is because then we took it places. We took it to a farmer’s

30:39 – 30:44

market where, you know, a guy we would talk to somebody like, what’s up with this table? And

30:44 – 30:48

we’d be like, well, this is what it is. And he would say, oh, can I apply for that? And then

30:48 – 30:52

we would send them over to the table where he could apply. We took it to an art gallery where

30:52 – 30:56

they said, oh, this is a real problem in other places. And we said, no. No. It’s a problem here.

30:57 – 31:03

Here’s a QR code that links you to organizations where you can help. Those experiences to me

31:04 – 31:11

speak to the power of alternate forms of data. Yeah. Because the arts sort of helps you ask

31:11 – 31:17

questions. And this this big physical table that you can touch and feel, and as a form of a

31:17 – 31:22

table, it uses all these metaphors, material metaphors, right, about the things that they’re

31:22 – 31:28

made out of to get you into what is really a data story. Right? It’s about numbers. And then

31:28 – 31:33

we have a videos that that can engage with it and, like, a data report that looks more traditional.

31:33 – 31:42

All those things are linked to so, that piece, I think, is a for me, it has has had more legs

31:42 – 31:45

than I would have thought. We were it was touring for, like, 2 years.

31:45 – 31:45


31:45 – 31:50

And I did not think it had that many legs, and that to me is interesting. It it pushed me to

31:50 – 31:54

think it continues to push me to think harder. Plus, it was fun to make, fun to show,

31:55 – 31:55

and it, like,

31:55 – 31:59

brings me a lot of joy to feel like, hey. We’re engaging people on something that’s hard to

31:59 – 32:04

talk about because you talk to people and it’s hard to talk about some of their backgrounds.

32:04 – 32:08

They’re like, oh, yeah. We had times when my mother where we didn’t have enough food.

32:08 – 32:09


32:09 – 32:14

You know? And, like, they get into it. So, anyway, that’s one piece. Another example is a lot

32:14 – 32:21

of the work that my students do in class. Mhmm. So in various settings, when I when I’m in,

32:21 – 32:25

like, a data storytelling class that I’m teaching, they expect to be producing charts and graphs

32:25 – 32:32

and maps. Yeah. And then I say things like, oh, you need to make something edible. And they’re

32:32 – 32:38

like, what? And then they go and produce this group of students to this amazing piece where

32:38 – 32:45

they made smoothies. And they made a video of themselves with these smoothies, where the it

32:45 – 32:49

was like a breakfast smoothie. Right? And the amount of kale they started with c o two emissions.

32:49 – 32:57

Mhmm. The amount of kale in each smoothie was based on the amount of carbon dioxide emissions

32:57 – 33:03

in each country. So they had 4 smoothies and they had like, you know, each one had increasing

33:03 – 33:08

amount of kale. Okay. Yeah. So that’s really good. Kale smoothie. It’s good for you. Yeah. Like,

33:08 – 33:11

first one, I don’t remember what it was. It was something like European country, and they’re

33:11 – 33:16

like, oh, this is a good smoothie. And they get to the last one. And they’re jerking. It’s like,

33:16 – 33:17

oh, my God.

33:18 – 33:19

This is terrible.

33:20 – 33:22

Yeah. And like, you can you can feel

33:22 – 33:23

feel it. Yeah.

33:23 – 33:24

Just just watch You

33:24 – 33:26

experience it. Right? Yeah.

33:26 – 33:30

Right. And that’s exactly it. That way of know that’s a different way of knowing the data. You

33:30 – 33:35

watch someone perform it, and you viewed that as a viewer of it.

33:35 – 33:35

And Right.

33:35 – 33:40

I think I love those examples where, like, they come up with something I never would have, and

33:40 – 33:44

they ever would have just because I pushed them a little bit, then they come up with this other

33:44 – 33:49

way to experience the data. And I think we just need more examples of that. So I’m trying to,

33:49 – 33:54

like, you know, this this year, the data storytelling class I’m I’m teaching, they’re gonna

33:54 – 33:58

produce a video piece at the end that’s like a TV news bit that’s about bringing people together

33:58 – 34:04

Jon data. I found 1 or 2 examples from, like, morning shows in the US. I I think we need more

34:04 – 34:08

examples that, like, just show that there’s a bigger toolbox.

34:09 – 34:15

Yeah. Yeah. Than just your line charts, bar charts, whatever’s on the screen Jon your phone

34:15 – 34:16

all the time. Yeah.

34:16 – 34:21

Yeah. And especially because there’s all this great research in psychology that starts to say

34:21 – 34:26

that when we use those forms, people think we’re right. Right? When we show people a bar chart,

34:26 – 34:30

people are like, oh, yeah. Okay. That makes a lot of sense. Mhmm. But they don’t ask as many

34:30 – 34:36

questions as when even if you just draw that same bar chart by hand. Mhmm. Then they ask more

34:36 – 34:41

questions. And in so many settings, you want people to ask questions and engage with you, not

34:41 – 34:44

have them nod your head. If you’re in front of your board meeting and you want more money, go

34:44 – 34:47

ahead. Show them a bar chart. You want you want them nod their head.

34:47 – 34:47


34:47 – 34:53

But if you’re in any other community setting, you want people to, like, ask questions and engage.

34:53 – 34:58

So we have to use media forms and processes that are appropriate.

34:59 – 35:05

Yeah. Well, it’s great work. I’m super excited about it. I just enjoy the, the website for your

35:05 – 35:11

lab. And, yeah. Thanks so much for coming on the show. I hope you have a great semester and,

35:11 – 35:14

I’ll look forward to watching those projects. Come on.

35:14 – 35:16

Thank you for having me. It’s so fun to talk about this stuff.

35:17 – 35:21

Thanks for tuning in to this week’s episode of the show. I hope you enjoyed that. I hope you’ll

35:21 – 35:25

check out the website for Rahul’s lab. I hope you’ll check out the policy vis site where I have

35:25 – 35:31

lots of other resources and a couple of blog posts on my own about this process of working with

35:31 – 35:37

data in the physical world. While you’re at it, take a moment if you wouldn’t mind to leave

35:37 – 35:43

a rating or a review for this podcast on your favorite podcast provider, be it iTunes, Spotify,

35:43 – 35:48

Google Music, wherever you listen to this podcast, if you could leave a rating, leave a review,

35:48 – 35:53

I’d really appreciate it. It helps me reach out to more guests and helps more people learn about

35:53 – 35:58

the exciting world of data visualization. So until next time, this has been the Policy Viz Podcast.

35:58 – 36:04

Thanks so much for listening. A number of people help bring you the Policy Viz Podcast. Music

36:04 – 36:09

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