Welcome to the Season 10 finale of the PolicyViz Podcast. I can’t believe I’ve been doing this podcast for 10 years! I’m truly grateful to all my guests and all my listeners who have been tuning in and, hopefully, have learned a lot about data, data visualization, presentation skills, and more. As I sign off for the summer, I hope you are able to take a break and get a bit of rest.

In this final episode of the season, I welcome Nancy Organ to the show to discuss her new book Data Visualization for People of All Ages. Nancy’s book aims to make dataviz accessible to everyday readers. Our conversation highlights the importance of not altering data simply for aesthetics but to facilitate understanding. We also explore balancing creativity with informed design choices, and suggest alternatives to traditional graphs, such as infographics, timelines, flowcharts, and diagrams.

Our discussion extends to the challenges of creating visually appealing infographics and the significance of design in effective communication. Because Nancy’s book could work well to help kids better understand data and data visualization, we talk about how she might better integrate data visualization into educational curriculums. We also talk about non-traditional learning environments like science camps and homeschooling, and we also discuss inclusivity and diverse thinking approaches.

Topics Discussed

  • Importance of Data Visualization. Nancy underscores the necessity of data visualization skills in the modern world and how these skills can be nurtured from a young age. Nancy’s book aims to demystify complex data concepts, starting with basic data units and building up to more sophisticated visual representations.
  • Educational Approach. Her book includes self-assessment tools and classroom exercises to facilitate learning with an emphasis on making the content relatable and straightforward for a broad audience.
  • Ethics in Data Visualization. There is a strong focus in Nancy’s book and in our conversation on maintaining data integrity and making ethical choices in visual storytelling.
  • Techniques and Tools. We also discuss various data visualization formats such as timelines and flowcharts and how to understand different data encodings to enhance perceptibility and engagement.
  • Incorporating Visualization into Education. Finally, Nancy and I talk about integrating data visualization and data science into the K-12 curriculum and how to promote visual thinking across multiple disciplines and learning environments.


Guest Bio

Nancy Organ is a Seattle-based data visualization practitioner and author of Data Visualization for People of All Ages. An emphatic response to the gap in visual literacy education for people young and old, the book uses lighthearted examples to illustrate graduate level theory so that everyone can learn what it really means to visualize data. Her background is in research, education, and tech, including contributions at Microsoft, the University of Washington. She is currently the Data Visualization Lead at Trilliant Health, a growth stage startup working to make the health economy more efficient and performant for providers and patients alike. 

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00:12 – 00:17

Welcome back to the PolicyViz Podcast. I’m your host, Jon Schwabish.

00:18 – 00:23

This is the final episode of season 10 of the Policy Viz Podcast.

00:23 – 00:27

Thank you so much for tuning in. I hope you’ve enjoyed this season.

00:27 – 00:34

I have talked to so many great data visualization and presentation practitioners and researchers and analysts.

00:34 – 00:36

I hope you’ve learned a lot over the last few years.

00:36 – 00:40

Over the next couple months, take some time, catch up, relax.

00:40 – 00:47

You’re on the beach, you’re hiking, you’re hanging out with friends, throw Jon your headphones, throw on your AirPods, listen

00:47 – 00:57

to a couple of episodes, catch up so that you can be ready for the fall where I’ve already got folks lined up for another great season of the show.

00:57 – 01:05

But on this year’s season finale of the show I love saying there’s a season finale, by the way, because I get a break from all the work.

01:05 – 01:14

But on this final episode of the season, I have a special guest, Nancy Organ, author of the new book, Data Visualization for People of All Ages.

01:14 – 01:23

We talk about what that means, what it means for kids to learn about data and data visualization, and how we can engage them

01:23 – 01:29

and the different challenges of teaching data visualization to 5 year olds and 65 year olds.

01:29 – 01:32

We talk about Nancy’s process for writing the book.

01:32 – 01:41

We talk about the challenges about teaching data visualization, and we talk about how we think schools and education, programs

01:41 – 01:46

might integrate data visualization into those curricula.

01:46 – 01:50

So there’s a long good conversation here about the new book. I hope you’ll check it out.

01:50 – 01:55

It’s really, I think, a great book to help you learn the fundamentals of data and data visualization.

01:55 – 02:01

And so I hope you will check it out at the CRC press site at Amazon or wherever you get your books.

02:01 – 02:04

Now just a couple of things before we go for this season.

02:05 – 02:12

First off, I wanna thank everybody who has listened to the show, all of my guests that have come on over the last 9 or 10

02:12 – 02:21

months, and, of course, everyone who’s helped me build the show from sound editing to the YouTube channel to creating the website and all the content.

02:21 – 02:24

This is not an easy show to put together. It sounds easy.

02:24 – 02:26

I’m sure you’re listening to it like, oh, this is just a breeze.

02:26 – 02:33

There’s a lot that goes into this show, all the way from scheduling to recording, to editing, to all the materials that go

02:33 – 02:42

out, when the show is produced along with the newsletter where I write up a little bit about each and every episode. That’s first thing.

02:42 – 02:50

Second thing, if you wouldn’t mind, please take a moment to rate or review the podcast on your favorite podcast provider. That might be iTunes.

02:50 – 02:55

It might be Spotify, or it might even be on the Zencastr website where I do a lot of the recording.

02:56 – 03:01

The podcasting market is kind of interesting how it’s evolving, but I think iTunes and Spotify are the primary places.

03:01 – 03:05

So if you wouldn’t mind, please take a moment, leave a rating, leave a review.

03:05 – 03:15

If you don’t wanna do that, head over maybe to Amazon and leave a rating or review for one of my, data visualization or presentation books. There’s better data visualizations. There’s better presentations.

03:15 – 03:21

There’s elevate the debate and, of course, my new book, data visualization in Excel. Okay.

03:21 – 03:28

With that out of the way, I’m gonna head over to my interview with Nancy Oregon. I hope you will enjoy it. I hope you’ll learn a lot.

03:28 – 03:31

Again, thanks so much for tuning in to the show. Have a great summer.

03:32 – 03:47

And here’s the final episode of this 10th season of the show, my interview with Nancy Oregon, author of the new book, Data Visualization for People of All Ages. Hi, Nancy. Great to see you. Great to meet you. I guess, like

03:47 – 03:48

Great to meet you. Person.

03:48 – 03:50

Right? For real

03:50 – 03:51

at the same time. Yeah.

03:53 – 03:58

As opposed to, like, endless emails, and I’m sure, like, attending the same thing simultaneously that we

03:58 – 03:59

do for,

03:59 – 03:59

like yeah.

03:59 – 04:01

Totally. The smallest world.

04:02 – 04:05

Smallest world. Yeah. Especially the data world, right? Like the smallest world. Absolutely.

04:06 – 04:09

So thanks for coming on the show. Great new book. Thank you.

04:09 – 04:12

Data Visualization for People of All Ages.

04:13 – 04:15

But, yeah, we have our matching our matching copies.

04:15 – 04:17

I saw my poster in the background.

04:17 – 04:18

And your poster in the back. Right?

04:18 – 04:20

You’ve got, like, the Dataviz theme in the back.

04:20 – 04:20

Mine is a little bit more hectic than that.

04:20 – 04:21

So I’m excited to chat with you about writing a Dataviz book

04:27 – 04:31

for all ages, like, you know, thinking about everybody. Yes.

04:32 – 04:35

But maybe we can start with intros.

04:35 – 04:38

It’s a little easy for you to be like, oh, I did this, this, and then the other.

04:38 – 04:45

But maybe, sort of talk about, you know, where you started and how you got to the point where you’re like, there needs to

04:45 – 04:48

be this book, and I’m ready to write it.

04:49 – 04:55

Totally. Great. Great. Great. Well, so I’m I’m Nancy. That is true. Start the the

04:55 – 04:56

present Yeah. Start there. Yeah.

04:56 – 05:05

In the present tense. So more than anything, I’m a data practitioner, which means that my career, my days are dedicated to

05:05 – 05:10

using data visualization to help people share what’s the most important to them.

05:11 – 05:20

I say all the time that my job is the fun part of, of so many scientific and analytical pursuits because I get to take the

05:20 – 05:23

end result or, you know, that’s a little hand wavy.

05:23 – 05:30

We work on it the full, the full trajectory, but I get to make it impactful and actionable and pretty and fun and expressive.

05:31 – 05:33

So, yeah, my job is the fun part.

05:34 – 05:40

My academic background though was in statistics and actually started my career in medical research.

05:41 – 05:46

But, obviously, medical research uses quite a bit of visualization, so that’s kind of where I got my first exposure.

05:47 – 05:55

And then when I realized that it’s this marvelous confluence of technical thinking and creativity and relationships and expression,

05:56 – 06:04

I decided to focus solely on that and and have this ability to interact with so many different fields.

06:04 – 06:10

And like I said, so many different things that people care about. Yeah. Yeah.

06:10 – 06:12

So that’s that’s what I’m doing now.

06:13 – 06:24

But as far as why write a book and why set out to do something so absurd as to write something for everyone. Right?

06:24 – 06:29

It’s like, what’s more canonical than the expression of, like, you can’t please everybody. Right.

06:29 – 06:36

And then just say, oh, Jon can. Right? Who am I to say? Right? But I did. I did say that.

06:36 – 06:38

And I think we did a pretty good Jon.

06:38 – 06:43

We being myself and my my fabulous editors, Tamar Munzner and Alberto Cairo.

06:44 – 06:52

I think I think we’ve done it or as close to doing it as possible. Mhmm. So so why?

06:53 – 06:57

I had this it was a couple years ago. It was during the pandemic. Right?

06:57 – 07:00

So we had a lot of time alone with ourselves.

07:01 – 07:12

And I had just taught a class at the University of Washington, and I had just recently been working at Microsoft with, you know, so many brilliant adults.

07:13 – 07:21

And I realized in this, like, flash of light that I’m spending all of this time either at work or teaching or wherever explaining

07:22 – 07:35

to adults these very fundamental concepts of data visualization, even if it’s their own work or it’s, you know, just part of their day to day routine. Right? And I saw this gap. I was like, wow.

07:35 – 07:44

These brilliant people are using this tool without ever having truly sat down or had the opportunity to learn what they’re

07:44 – 07:55

doing, to learn what it means to visualize. We’ve taken math classes. We’ve taken grammar classes. People have learned how to code. People have taken science classes.

07:55 – 08:06

We’ve learned everything peripheral to data visualization while using it, but we haven’t sat down and learned what does it mean to visualize. Mhmm.

08:06 – 08:09

And if I’m teaching them, who’s teaching their kids?

08:10 – 08:12

So, you know, a quick Google at the time.

08:12 – 08:22

Right now, we’re lucky in over the past 2 years, more content, more resources for young people have started to emerge in the database space.

08:22 – 08:36

By the time, there was very, very little, and certainly nothing in mainstream circulation. So I said, okay. Well, who’s teaching them? No one? What if that’s me? What if we go for it?

08:36 – 08:37


08:38 – 08:48

So we did. And I I felt like it was important to reach everybody in one go because you and I may have our bookshelves full of Data textbooks.

08:48 – 08:51

I think that is both statement Jon minority of people.

08:52 – 08:52

Yeah. 100%.

08:52 – 09:00

So Yeah. With the operating assumption that most people will only ever read one database book. Can I get that book?

09:01 – 09:03

That one book that’s gonna be in someone’s house.

09:03 – 09:10

Can I make something where, a grown up on Wall Street feels cool having it on their desk?

09:11 – 09:17

And a 5th grader in North Carolina feels cool having it in their backpack.

09:19 – 09:26

So, yeah, I don’t know if that 5th grader is ever gonna feel cool with I mean, there’s the rare 5th grader who feels cool

09:26 – 09:29

with a data book in their backpack. But I get your point. I get your point.

09:29 – 09:41

So so how do you go about trying to find the sweet spot in not just the style of the writing. Right?

09:41 – 09:46

Because you can write differently for the 5th grader versus the 30 year old.

09:47 – 09:49

But also in the examples you use.

09:49 – 09:54

So we were sort of as we were talking before prepping for for, recording.

09:55 – 09:59

And I was talking about how, like, the first example in the book is on ice cream.

09:59 – 10:03

Now everybody gets ice cream, and so that I see is, like, for all ages.

10:03 – 10:15

Now had you started the book with, like, per capita GDP or, like, you know, government funding for a program, like, that’s clearly for the 30 year old. Right?

10:15 – 10:19

But how do you sort of think about threading the needle to kinda, you know, meet the needs of both?

10:20 – 10:26

Totally. Yes. Ice cream is for everybody, and I stand by this.

10:27 – 10:29

And and I actually love I love this.

10:29 – 10:36

I think it’s just such a funny little quirk of of people that, the ice cream example has come up in a couple conversations.

10:36 – 10:38

It’s like even when I started writing it. Right?

10:38 – 10:44

We sat down as the as a team, like, is this too is this too young if we’re going for all ages?

10:45 – 10:45


10:45 – 10:54

But everyone knows ice cream. So my compromise there was to instead of having kind of simple traditional flavors to make more interesting flavors. Right?

10:54 – 10:58

Like horchata flavored ice cream and red velvet or pumpkin pie. Pie.

10:58 – 11:03

So I made I I used my local Seattle ice cream shop as inspiration.

11:03 – 11:03

I was

11:03 – 11:05

like, what would they do? What what kind of

11:05 – 11:06


11:06 – 11:09

Interesting flavors would they have? Because I go there. Right?

11:09 – 11:15

And I’m not getting the the the typical strawberry chocolate vanilla. I’m getting the whatever. You know?

11:15 – 11:17

Right. Okay. So now before before you go on

11:18 – 11:20

So that was how I solved that. Yeah. Yeah.

11:20 – 11:23

But before you go on before you go on, so what is this Yeah.

11:23 – 11:26

Just for the Seattle people, what is this ice cream shop that you like to go to?

11:26 – 11:28

Oh, Frankie and Joe’s.

11:29 – 11:31

Okay. Alright. Good to know. Yeah. Alright. 10 out

11:31 – 11:31

of 10.

11:31 – 11:35

Next time I’m in next time I’m in Seattle, that’s I’m just gonna mark it down.

11:35 – 11:36

That’s my next I would say Great.

11:37 – 11:39

I’ve had a bad habit of having ice cream for dinner.

11:39 – 11:41

That’s not a great place to do that because it’s

11:41 – 11:42

very rich.

11:42 – 11:50

So I would keep the conventional order in that case, but that’s my only word of caution, which expect ice cream. Alright.

11:50 – 12:00

So this is this is good. So so folks are sort of getting they’re getting a little bit of the, like, literally Seattle flavor from this from this conversation. So that’s that’s the big star. Yeah. Okay. Great. Absolutely. Yeah. Absolutely.

12:01 – 12:08

So right. So the examples, that’s the kind of one way that I dealt with the tone. Mhmm.

12:08 – 12:11

Another thing that really inspired me was Reddit.

12:12 – 12:15

Have you seen the subreddit explain it like I’m 5?

12:16 – 12:17

I don’t believe I have

12:17 – 12:20

a question. Reddit. Okay. Yep. So there’s a sub there’s a subreddit. Right?

12:20 – 12:26

There’s a page on Reddit where people ask questions, and they can be simple or complicated, and they say, I just don’t understand this.

12:26 – 12:29

Can you explain it to me as if I were a 5 year old? Mhmm.

12:29 – 12:39

And that’s so delightful and magical to me, because if you can explain something complicated in simple terms Mhmm. That’s when you truly understand it.

12:40 – 12:47

And most things, it’s not that they are too hard to understand.

12:47 – 12:50

It may just take you may just need some more time to understand it. Right?

12:50 – 12:51


12:51 – 12:53

Things are complicated, but everything is solvable.

12:53 – 12:58

Everything is made of small pieces that, taken bit by bit, can be digestible.

12:59 – 13:02

So I I use that as kind of inspiration visualization to say, alright.

13:02 – 13:07

Well, I think we can tackle almost anything in simple terms.

13:07 – 13:07


13:07 – 13:12

And if I’m having trouble explaining something in simple terms, that doesn’t mean stop.

13:12 – 13:15

That just means take the complicated part of it and break that down further.

13:16 – 13:17

Yeah. Yeah. So that’s

13:17 – 13:19

why, I mean, the first chapter is what is data.

13:19 – 13:24

Like, let’s start from the very atomic unit of the data visualization.

13:24 – 13:30

Let’s assume nothing and go from there. Right? We all learn to speak. Right.

13:30 – 13:35

Yeah. Yeah. No. You need to be able to kinda speak that that basic language. Yeah.

13:36 – 13:40

Again, thinking about all ages and and most data vis books don’t do this.

13:40 – 13:47

There’s not, more Jon sort of like the data of, like, how to process data, how to work with data, where to get data.

13:47 – 13:49

That probably is like that’s separate books.

13:49 – 13:55

But did you think about kind of the intervening steps of definition of data? Right.

13:55 – 13:59

Then there’s the, how do you get it? How do you work with it? How do you analyze it? And then here’s the visualization piece.

13:59 – 14:02

I mean, I don’t think there’s many data biz books that sort of fill in that

14:02 – 14:02

The whole thing.

14:03 – 14:04

App there. Yeah. The whole thing.

14:04 – 14:09

I did think about it. I decided to not go there.

14:09 – 14:18

So this is purely visualization in the sense of, you know, what what we reveal at the at the end and what, you know, practitioners

14:18 – 14:24

know as just encoding information onto something perceptible. Not even just visualization, perceptible. Right?

14:24 – 14:28

I have the the chapter on sound and touch too for that exact reason.

14:28 – 14:36

This is more than just, this is not a taxonomy, a practice of taxonomy where you take, oh, this concept needs this type of graph.

14:37 – 14:42

This is this type of data can be shown with any number of different encodings.

14:42 – 14:46

It can be abstracted in any number of ways. That’s what visualizing is. Right?

14:46 – 14:49

We can we can put all the pieces together later, and that’s truly part of it.

14:49 – 14:54

But at the crux, right, you’re just mapping your encoding.

14:54 – 14:55

Yeah. So

14:55 – 15:01

I focused on that. We have to stop somewhere. Right? Yeah.

15:01 – 15:05

I also very intentionally didn’t away from

15:08 – 15:08


15:11 – 15:19

because those Right. Stay away from those, because those are ultimately also just little blobs of encoding.

15:20 – 15:20


15:20 – 15:25

You can get there once you know what visualization means.

15:25 – 15:26


15:26 – 15:32

And same with the data collecting and analyzing. I think just beyond the scope.

15:32 – 15:35

And I had already doubled what I Jon contracted to do, so so I needed

15:35 – 15:38

I had the same experience when I wrote my book for them too. I don’t know.

15:38 – 15:42

Maybe they just underestimate, like, what we’re gonna write. I don’t know.

15:42 – 15:43


15:43 – 15:49

You mentioned having worked with and taught folks and and done work as a freelancer. Have you worked with kids?

15:50 – 15:59

Like, how did that work inform how you wrote the book and sort of, like, the circular piece of, like, getting feedback and going back and forth? Yeah.

16:00 – 16:07

Personally, I had not worked with kids before writing this, so I fully admit that. I will defend, though. I have been a kid.

16:08 – 16:13

So I know that that experience is not all of it. Right? I’m not Yes. Parented.

16:14 – 16:16

I have not been a school teacher. Sure.

16:16 – 16:28

But constantly present in my mind throughout this book was, what would 10 year old Nancy have wanted to see? What would she have understood?

16:29 – 16:30

Where would she have gotten hung up?

16:30 – 16:35

Where would she have been more curious? Where would she be impatient?

16:35 – 16:40

And now I need to give the payoff a little bit sooner because you’re losing focus, because it’s a lot to take in.

16:41 – 16:41


16:41 – 16:50

So granted that’s, you know, an audience of 1, but as special as we like to think we are. I know that’s true for myself. Ultimately, that that is limited.

16:51 – 16:53

You know, if it works for 1 10 year old

16:53 – 16:54


16:54 – 16:58

My hope at least was that that would resonate with more.

16:58 – 16:58


16:58 – 17:03

And there’s also very intentionally not an age number put on here.

17:03 – 17:04


17:04 – 17:14

Because things you know, everybody’s different. And maybe for some people, they just won’t be interested in it until they’re 15 or 65.

17:15 – 17:20

And somebody may love it and just soak it up at 5. Right? Who am I to say? Right?

17:20 – 17:25

I don’t wanna I don’t wanna impose any assumptions on people.

17:25 – 17:25


17:26 – 17:29

But my target audience was 10 year old Nancy.

17:30 – 17:36

Gotcha. And then at the end of each chapter, there’s, you know, it depends on the chapter, obviously, but there’s, you know,

17:36 – 17:42

at least a couple of pages, maybe up to 5 or 6 pages depending on the chapter of exercises. Mhmm.

17:42 – 17:52

And in your head, was that for the 10 year old Nancy, or was that for 65 year old Nancy? Or for, again, for, like, everybody?

17:53 – 17:57

Sure. Sure. That was I I made the you know, the solutions are in the back. Right? There’s no

17:58 – 17:58

Yeah. Yeah. You

17:58 – 18:00

could just look up what the answer is. Yeah.

18:00 – 18:01


18:01 – 18:08

So I didn’t I wanted something where, you know, maybe someone learning independently, a young person, adult, whoever, could

18:09 – 18:14

kind of test their understanding there in a very low stakes kind of accepting way.

18:14 – 18:18

Like, all the solutions say, oh, you could do this a variety of way.

18:18 – 18:22

But the important thing here is that you understand this component.

18:23 – 18:28

But it’s also I wanted to make sure that I didn’t rule out classroom usage.

18:28 – 18:34

I know there’s a full gamut of complexities to getting new material in school systems.

18:35 – 18:35


18:35 – 18:40

But having no exercises would pretty much eliminate that possibility altogether.

18:40 – 18:48

So I wanted some exercises where, you know, in a in an education environment, the the teachers were equipped with something

18:48 – 18:51

they didn’t have to think about think of it themselves. Yeah.

18:51 – 18:54

So that’s for you know, it’s there if you need it, but Mhmm.

18:55 – 19:03

I don’t think any of there’s no, like, new content revealed in them that would prohibit continuing through. Yeah. You didn’t just get through it.

19:04 – 19:10

The other big thing I wanted to ask about was, your take on Data rules.

19:10 – 19:15

So I noticed you have a section on, like, bar chart should start at 0. Yes.

19:15 – 19:21

And you have a thing on, like and then there’s a there’s there’s a section on, like, 3 d, like, be careful.

19:21 – 19:28

And then you have a section at the end, like, a chapter at the end that’s, like, here are ways to kinda distort or mislead. Right.

19:28 – 19:37

But my read of the book is that, you know, we’re sort of similar in this way that there aren’t really rules. It’s a creative medium. And Yeah.

19:37 – 19:48

So so I guess I guess if I were to sort of distill that down to one question, like, what is your philosophy on Data rules versus kind of, like, loose guidelines?

19:50 – 19:51

Sure. Sure. Sure.

19:51 – 19:52

Or just like it’s a wild wild west and go

19:52 – 19:53

away. Yeah.

19:53 – 19:54

You know? Yeah.

19:54 – 19:56

I think the no. I love this question.

19:56 – 20:01

And it comes up almost daily in Yeah. My practice. Right?

20:01 – 20:08

Because everyone wants to be for the most part and and Alberto has this in his book, how charts lie. Right?

20:08 – 20:12

A very strong emphasis on most people don’t mean anything bad. Right?

20:12 – 20:22

Most mistakes, most lying in visualization, even though we get a really bad rap for being misleading, I would say most of it is really well intentioned.

20:22 – 20:28

So that’s why I called the chapter whoopsies because, right, instead of something more sinister.

20:28 – 20:29


20:29 – 20:35

I think the only real rule is don’t lie. Don’t be dishonest.

20:35 – 20:38

And that includes don’t be unintentionally dishonest.

20:38 – 20:44

You need to know how that can occur accidentally so that you don’t right.

20:44 – 20:47

Like, the bar charts is a great example. I can totally understand.

20:47 – 20:50

And I’m sure I’ve done it before starting a bar chart, not at 0.

20:50 – 20:56

But once you realize why that’s a whoopsie, then you say, oh, this actually is misleading.

20:56 – 20:57

That’s not what I need to do.

20:57 – 21:00

That’s not, that doesn’t align with my intent here.

21:01 – 21:05

So always fall back on what’s true. Right?

21:05 – 21:13

I I have a little mantra at work that is we do not edit data for aesthetic purposes. Right?

21:13 – 21:21

So if you have an outlier or something that is inconvenient in your visualization and it makes it look less cool, you have

21:21 – 21:24

my full sympathy, but we need to hold ourselves to that.

21:24 – 21:28

We do not edit data for aesthetic purposes. We don’t lie. Yeah. Right? Essentially.

21:29 – 21:36

Other than that, I think knowing, like, knowing the the the rankings of encodings. Right?

21:36 – 21:41

Oh, certain encodings work better or worse for different types of data. And this is metric. Right?

21:41 – 21:46

This is just based on our minds and our perception systems.

21:46 – 21:53

That lets you maintain a little bit of creative liberty, but also kind of guide you in what’s a good choice.

21:53 – 22:00

Because there are situations where you wanna say, look, this could be objectively the best length position, whatever.

22:01 – 22:03

But I need something fun for whatever reason.

22:03 – 22:08

And so you wanna bend you wanna kind of say, I see that. I respect that.

22:08 – 22:16

I’m gonna go with something that maybe is less good, but is way better ultimately because it’s fun and people pay attention to it.

22:16 – 22:27

Or you have a wall of bar charts, and you need a way to distinguish one concept from the other. Right. I think that’s great. I think

22:27 – 22:27

it’s great.

22:27 – 22:32

I mean, I don’t think there’s any need to be overly severe in that sense as long as Yeah.

22:32 – 22:36

You’re not lying, and you know why you’re making that choice.

22:36 – 22:45

Right. One of the things that I struggle with is and I and I see this a bit in your book, but I see it in in certainly the

22:45 – 22:47

case in my book and many other books is Mhmm.

22:48 – 22:50

A lot of the books focus on the classic graphs.

22:50 – 22:55

I think your book, my book, there’s a handful of others that sort of push, Like, there’s lots of other stuff that people don’t know about.

22:55 – 23:05

But then there’s, like, more of the, like, infographic y design things like and even not like an infographic, but like a timeline

23:06 – 23:08

or a flowchart or just a diagram.

23:08 – 23:10

Like, it gets into this, like Yeah.

23:11 – 23:13

Like, there’s not even, like like, a bar chart. Okay. So it’s a standard.

23:13 – 23:18

The bars go left to right and blah blah blah, and, like but it has a structure. Whereas, like, a timeline

23:18 – 23:19


23:19 – 23:29

You know, a line or a bar or a circle or a multiple like, so how how do you like, within the book or just in your in your

23:29 – 23:34

work or when you work with people when you’re teaching, like, how do you think about some of these diagrams sort of give that

23:34 – 23:40

introductory, yeah, kind of the same let me let me I’m trying to distill my questions down.

23:40 – 23:42

My head is still not here, but, like

23:42 – 23:43

Yeah. That If

23:43 – 23:47

if CRC press was to come back to you and say, this is great.

23:47 – 23:49

We want you to do a SQL.

23:49 – 23:52

We don’t want you to do data visualization for people of all ages.

23:52 – 23:57

We want you to do data diagrams for people of all ages. Ah.

23:57 – 24:03

I know I’m sort of throwing this, like, brand new book project, this brand new question at you right now, but, like, how would

24:03 – 24:15

you think about those, like, basic guidelines of kind of visual types that are Right. I mean, essentially super freewheeling. They’re not like Right. Almost no structure to them.

24:15 – 24:22

Right. Gee, I think well, it’s not the fun answer, but I might say I’m the wrong person for this.

24:25 – 24:28

Right. I mean, that’s that’s totally fair. I mean, that’s totally fair.

24:28 – 24:33

I mean, I I certainly, like, wave my hand at this question when it comes up. And I’m just like, hey.

24:34 – 24:40

If you go to, I don’t know, timeline storyteller.com or if you go to Venngage or, you know, these other sites and you look at Yeah.

24:41 – 24:44

Timelines, there’s, like, a million options right away.

24:44 – 24:51

So, like, I can’t give you really, like, really rules of thumb even because but it’s something I struggle with a little bit

24:51 – 24:58

because I think a lot of people do, you know, like an org chart is a pretty common thing that people make.

24:58 – 25:00

And they wanna make them look good.

25:00 – 25:05

And so but, like, it’s kinda hard to, like, give them Yeah. Even the basic. Right?

25:05 – 25:09

It’s true because they also are everywhere. Right? Anybody who’s

25:09 – 25:10


25:10 – 25:18

Worked in any corporate environment or has seen many of these infographic Yeah. Kind of just chart based things.

25:18 – 25:28

I think the so, a, I don’t know how to write a book on that because I just I like you, and it just isn’t I guess I think it’s too too

25:28 – 25:29

Yeah. Yeah.

25:29 – 25:42

Too structured in how I think. But even with even outside of that being my comfort zone, I think we can dissect a lot of these

25:42 – 25:44

charts and still see this language of encoding.

25:45 – 25:46

Yeah. Yeah.

25:46 – 25:48

They they still inform each other. Right?

25:48 – 25:48


25:48 – 25:51

You have a flowchart that’s everywhere. You know, it’s going all over the place.

25:52 – 25:58

But there are some, you know, some of the ribbons that are thicker than others. That means something.

25:58 – 26:03

You have chosen to make some areas one color, and perhaps there’s a gradient between them. That means something.

26:04 – 26:11

And so even if, even if I’m not the right person to tell you how to make a great infographic, which I will reiterate.

26:11 – 26:15

I think there are great infographics and I appreciate them. It’s just not my area.

26:15 – 26:19

I do think they’re, they are very close cousins, right?

26:19 – 26:24

There’s, there’s a lot of, they inform each other, I think.

26:24 – 26:28

Yeah. Yeah. It’s kind of interesting to think about what an underlying structure

26:29 – 26:30


26:30 – 26:34

Of kind of the diagram world is. And I’m just thinking about,

26:34 – 26:34


26:35 – 26:39

as we’re talking, I think about, like, my kids. Right? Like, my daughter makes Yeah.

26:39 – 26:43

My daughter’s in in high school and and making, like, for her, like, biology classes.

26:43 – 26:45

Like, she’s making flowcharts all the time. Right?

26:46 – 26:46

Yeah. Yeah. Yeah.

26:46 – 26:53

And so I think they’re yeah, I mean, this might be where the you and I have to write the book together because only 2 people

26:53 – 26:56

who don’t know what they’re doing can write something that will help back to

26:56 – 26:58

help people. Yes. Yes.

26:58 – 27:00

Yeah. Yes. Yeah. I would say

27:00 – 27:07

I did address networks, which was a great learning opportunity for me as well because I just had never really sat down and

27:07 – 27:09

thought, like, explain it like I’m 5.

27:09 – 27:16

How do you explain networks to someone who is a complete blank slate in the world of networks?

27:16 – 27:16


27:16 – 27:22

that was really interesting, and a funny thing to fit in. Right?

27:22 – 27:28

Because, like, connection is not really an encoding, but it also definitely is.

27:28 – 27:30

Right. Right. For sure.

27:30 – 27:38

So and it’s you know, I I also really wanted to include it because of so many because all Sankey diagrams that I’ve come across

27:38 – 27:43

in my lifetime and they are confusing. They are cool.

27:43 – 27:45

They are cool and wiggly and confusing.

27:46 – 27:53

And so just for the sake of humanity, I was like, well, maybe we would all benefit if we had sat down to think a little bit

27:53 – 28:01

more carefully about what these mean, or selfishly, just so I have an excuse to also think about them carefully.

28:01 – 28:03

Well, I mean, that’s the thing about writing and teaching. Right?

28:03 – 28:06

Like, you really learn the content when you have to do it. Yeah.

28:07 – 28:13

So I wanted to ask before we finish up, I’m pretty sure I know what your answer is gonna be.

28:13 – 28:20

But do you think data vis, data science should be part of the k twelve curriculum?

28:20 – 28:23

And then, of course, if so, like Mhmm. What should be cut?

28:23 – 28:30

Now we’re right at the end of the school year here in Virginia, and my kids are for those classes were, like, their exams,

28:30 – 28:33

the AP exams are done or whatever. They’re just, like, watching movies.

28:33 – 28:35

So they could they’ve got several weeks.

28:35 – 28:37

They could do it right now, but okay. Yeah.

28:37 – 28:41

But just generally speaking, like, you know, do you think database should be a core part?

28:41 – 28:44

And then what would you sort of push out?

28:44 – 28:52

Right. Where do you trade? Well, firstly, I will say I was raised by a public school science teacher.

28:52 – 28:53


28:53 – 29:02

So I know firsthand just the pressures and restrictions and constraints that they’re under. Yeah. So so let it be known.

29:02 – 29:10

This is not something that we can just dump on teachers as an addition and then hope that it works out. That is Yeah. No. Strongly disagree.

29:10 – 29:10


29:10 – 29:14

That said, of course, I think that it should be part of the curriculum.

29:14 – 29:25

It should be in the water as soon as we can possibly get it there. Mhmm. Because it’s not just one skill. It’s thinking abstractly.

29:26 – 29:31

And there’s no lower bound on that being useful. Right?

29:31 – 29:39

Like, I think anybody who’s who’s who can remember the the journey of learning visualization and probably relate to this feeling

29:39 – 29:41

of it just changing the way you see the world.

29:41 – 29:50

It’s this full other tool that lets you understand and appreciate and communicate information. Yeah.

29:50 – 29:52

Of course, we need of course, we need that. We need it.

29:52 – 29:53


29:53 – 30:00

And I think something special about it is that even though, you know, I put it in one book, you’ve put it in one book, it’s

30:00 – 30:08

visualization is omnipresent, and it is a helpful learning tool in so many disciplines that, I don’t know, maybe we don’t

30:08 – 30:10

need to teach it in its own dedicated class.

30:10 – 30:14

Maybe we don’t need to swap out math content for visualization.

30:15 – 30:20

The first bar charts I saw were in a history class. Mhmm. Right? And you see them in biology.

30:20 – 30:23

You see time lines, you know, oil prices over time.

30:24 – 30:34

Can we integrate visual thinking, visualization more pervasively instead of having it be one chunk of time.

30:35 – 30:35


30:35 – 30:39

I that seems valid to me. Yeah. Yeah.

30:39 – 30:41

So so the short answer is I don’t know.

30:42 – 30:50

But we cannot if we don’t have the resources available, and also we can’t teach what we don’t know.

30:50 – 30:53

And so that goes back to all ages. Right?

30:53 – 30:58

We have a lot of work to do to get everybody on board.

30:58 – 30:58


30:59 – 31:01

Yeah. And so I I don’t know. I I wish I did.

31:01 – 31:08

No. No. No. I’m with you. I mean, I for me, in my thinking, I think it’s a little easier at, like, the older ages.

31:09 – 31:11

And I’m sure I’m gonna wrap a whole bunch of feathers with the next sentence.

31:11 – 31:20

But, like, I think there’s a, in math at least, there’s an overemphasis on calculus In high school, especially in the later grades, like Yeah.

31:20 – 31:27

You know, I mean, look, I have degrees in economics, and, like, I don’t use calculus anymore, like, in my day to day. Right?

31:27 – 31:31

Like, most the like, who needs calculus in their actual work?

31:31 – 31:36

It’s, like, engineers and and and economists, and that’s kinda it. Right? So, like Yeah.

31:36 – 31:41

You know, I would substitute statistics and probability for that. I may be first. Yeah.

31:41 – 31:49

But at the younger ages, which is where I think to your point, like, you need to get to 4th, 5th, 6th grade is where you need to start. Right.

31:49 – 31:55

And, you know, in in my experience, my kids learned, you know it’s kind of interesting, actually, like, in in our schools,

31:55 – 32:02

they learned bar charts, line charts, pie charts, and histograms were, like Oh, interesting. There was, like, a core Yeah. Graph type.

32:02 – 32:10

Now a histogram is, I guess, a bar chart, but, like, you know, you get so there’s something, but I think, you know, it is like everything’s a trade off.

32:10 – 32:17

And like you said, especially in certain states, what teachers can and can’t do is, like, pretty strict.

32:17 – 32:22

So, so it’s so and and, yeah, like you said, like, they’re already overburdened.

32:22 – 32:25

They already have to teach a lot of stuff and you know? Oh, yeah.

32:25 – 32:29

It’s certainly hard, but, but we can continue having the good fight.

32:30 – 32:34

Yes. And and I think there’s room yes. We can. We we we shall.

32:34 – 32:34


32:34 – 32:37

But there’s room outside of the classroom too.

32:37 – 32:37


32:37 – 32:45

You know? There’s, just activities after work or in the summer, like like book. Right? Yeah.

32:45 – 32:52

Where he’s just doing activities with his kids. That’s that’s great. That’s a possibility. Homeschools are a possibility.

32:53 – 32:55

You know, like science camps, I think.

32:55 – 32:57

Yeah. I mean, there’s a ton of coding camps now. Right? Like

32:57 – 32:58

There’s coding camps.

32:58 – 33:01

Yeah. I mean, it’s it’s out there for sure. Yep.

33:02 – 33:06

Do you know those little I I don’t know if you have them where you live in your neighborhood.

33:06 – 33:09

There are, like, little mini libraries that people put in front of their house.

33:09 – 33:14

Yeah. Yeah. The little, like yeah. The little lending libraries, like, on a front yeah. Yeah. Yeah.

33:14 – 33:17

I’ve been putting copies in them, and I

33:17 – 33:18

That’s so good.

33:19 – 33:24

Kind of my, like so good. Guerrilla marketing, but they’re disappearing. I think there’s a demand.

33:24 – 33:28

I go back and it’s not there the next day, so I put another. Right? That’s interesting.

33:28 – 33:31

Think there is an appetite for it.

33:31 – 33:35

Now I don’t, you know, I’m not, like, I don’t have a camera. I don’t know who’s taking Yeah. These books.

33:35 – 33:36


33:36 – 33:37

But someone is.

33:38 – 33:38


33:38 – 33:51

Right? And I think a lot of it is that people don’t realize that visualization is a full discipline. Like, what your kids are learning.

33:51 – 33:52


33:52 – 33:54

Visualization is bar charts and pie charts.

33:54 – 33:55


33:55 – 33:58

And, like, how often have you heard the joke? Oh, no. 3 d pie chart. Right?

33:58 – 33:59

Yeah. Yeah.

33:59 – 34:06

It’s like our like, you you could you could write on an index card, like, the sentences that you just hear over and over, and that would be the extent

34:06 – 34:07

of of

34:07 – 34:10

common knowledge. I think people don’t know that it’s there.

34:10 – 34:16

And so if we can also just get that switch flipped of like, oh, it is there. And it’s not rocket science.

34:16 – 34:22

And it’s actually very inclusive to different ways of thinking and people and personalities.

34:23 – 34:30

And if we’re not judgmental, right, if we’re a little open minded to it not being too strict

34:31 – 34:31


34:32 – 34:34

I think that will sell itself.

34:34 – 34:41

It yeah. I totally agree. I think it’s also interesting when I think about, like, the sports world because there’s so much data in sports.

34:41 – 34:51

Like, NHL has this new thing called NHL Edge, which is, like, deep data dives, like, super, like, where the players shoot on the ice. Right? And they have this new website.

34:51 – 34:59

And, like but I think what my guess is when people go to that site and they see what you kind of have is, like, the ice rink.

34:59 – 35:03

It’s an image of the ice rink, kind of it’s like a basic heat map of the ice rink. Right?

35:04 – 35:08

And my guess is if you said if you ask people about that, is that a data visualization?

35:08 – 35:11

They would probably say they’d probably say no. Right?

35:11 – 35:17

Because it’s, like, it’s not a bar chart or a line chart where Right. That’s what people think of. Right? Absolutely.

35:18 – 35:18

And it’s and

35:18 – 35:21

it’s broader than that. Yeah. Interesting. Yeah. Yeah.

35:21 – 35:24

So oh, I’m not a I’m not a hockey person.

35:25 – 35:29

The heat map that you’re describing, what kind of gradient is it?

35:29 – 35:30

It’s gray.

35:31 – 35:32

It’s gray?

35:32 – 35:35

Okay. It’s a sequential for every team and every player.

35:35 – 35:35


35:35 – 35:41

And I don’t know if they made this decision because they just didn’t wanna have to deal with different hex codes or, like

35:41 – 35:49

but, like, for every player, every team, when you select and it changes the map, it’s a light to dark gray, sequential color

35:49 – 35:57

ramp, which I find the page doesn’t look as Yeah. Vibrant. Right?

35:57 – 36:04

You would think, like, if you pick the Seattle Kraken, you would get, like, a really cool green or blue. You could pick the washing caps. You’d get, like, the red color.

36:05 – 36:08

But everything is the same the same color. So, you know

36:08 – 36:11

But it’s not the jet palette. It’s not rainbow.

36:11 – 36:15

It’s not rainbow. It’s not rainbow. So I don’t, yeah, so they’ve got that. But,

36:15 – 36:16

yeah, it’s

36:16 – 36:22

it’s it’s kind of interesting. But my guess would be that most people, like, instinctually wouldn’t say, oh, this is like

36:22 – 36:26

a really cool data or even a dashboard, even though that’s really what it is.

36:27 – 36:30

Because it’s not like bar chart, line chart, bar chart. And

36:31 – 36:32


36:32 – 36:35

For better or for worse, whatever that is. So, alright.

36:35 – 36:39

Well, Nancy, thanks so much for coming on the show. New book.

36:39 – 36:39

Thank you.

36:40 – 36:42

Data for people of all ages. Before we go, where

36:43 – 36:43


36:43 – 36:51

Can people find you to ask you questions, connect with you, get you to come out of the business.

36:51 – 36:56

That’s a great I have such a quiet social media presence. I am most active on LinkedIn

36:56 – 36:56


36:57 – 37:04

If that’s if that interests people. Yeah. That’s your best bet is LinkedIn.

37:04 – 37:06

Great. Sounds good. Well, you can I’m

37:06 – 37:09

a little bit analog. Yeah. That’s fine. You get that

37:09 – 37:11

flood of connection requests Jon,

37:11 – 37:12

LinkedIn. So,

37:13 – 37:16

alright. Thanks so much for coming on the show. This was great. Really great

37:16 – 37:17

Thank you.

37:17 – 37:20

Meeting you and and and sharing my love with you.

37:20 – 37:21

Thank you you so much.

37:21 – 37:26

And thanks everyone for tuning in to this episode of the show. I hope you enjoyed that.

37:26 – 37:27

I hope you have a restful summer.

37:27 – 37:33

I hope you take some time out to spend, your summer with friends and family.

37:33 – 37:45

Head on over to the beach, do some swimming, do some relaxing, do some reading, and do some listening to your favorite data visualization podcast, The PolicyViz Podcast. So thanks again for listening. Have a great summer. Until next time.

37:45 – 37:49

This has been the PolicyViz his podcast. Thanks again for listening.