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Evolving Data Visualization: Crafting the Second Edition of Visualize This with Nathan Yau
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Nathan Yau’s Flowing Data website was one of the first data visualization websites I discovered in my own data journey. With his new book, Visualize This, now out, I thought it would be a great opportunity to talk with Nathan about his work, his book, and how his own approach to data has evolved over the last several years.
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Transcript
Podcast Season Finale & Upcoming Episodes
00:00:13
Speaker
Welcome back to the Policy of This Podcast. I'm your host, John Schwabish. On this week's episode of the show, getting near the end of this season, by the way, only a few more episodes, so hang in there with me.
Introduction to Nathan Yao & Flowing Data
00:00:25
Speaker
On this week's episode of the show, I welcome Nathan Yao from Flowing Data. Now, if you don't know about the Flowing Data website, I mean, you probably do since you listen to this podcast, but
00:00:34
Speaker
But if you don't know about the Flowing Data website, you should definitely go check it out. Nathan is a statistician who has been running the Flowing Data website for over a decade. He has a great number of resources on the site, both free, especially his daily newsletter or email where he highlights a visualization or a statistical concept that he likes or finds really interesting.
00:00:55
Speaker
and then also a paid part of the site where you can get our tutorials, JavaScript tutorials, other types of members-only content. But
Visualize This - Book Release & Insights
00:01:03
Speaker
the Flowing Data website was one of the first websites I found when I started early in my data visualization career and has been an invaluable resource to learning how to create better, different, and more engaging data visualizations and charts and graphs and diagrams.
00:01:17
Speaker
And importantly, Nathan has a new book coming out, Visualize This, The Flowing Data Guide to Design Visualization and Statistics. So, of course, we talk about that book, we talk about Nathan's background in data and statistics and data visualization, and we talk about all the things that go in
00:01:34
Speaker
to making that site run and to what's going on in his new book and how he's approaching or how he has approached a second edition of his original book, Visualize This. So I hope you're going to enjoy this week's episode of the podcast. I know you will. You're going to learn a lot about data visualization. You're going to learn a lot about the flowing data site and what goes on behind it. And you're going to learn, of course, a lot about the flowing data owner, Nathan Yow.
00:02:00
Speaker
So
Exploring Second Edition Updates & Design Communication
00:02:01
Speaker
without any more delay, here's my interview with Nathan Yao from Flowing Data. Hey Nathan, welcome to the podcast. Great to like actually talk to you in person. Yeah, thanks for having me. It's good to finally see you.
00:02:18
Speaker
many years of us sort of like running in parallel and working in the space. Really excited. I reached out this time because you've got a new edition of your first book, right? Visualize this coming out any moment and wanted to touch base and talk about that and flowing data, which is come, which is what, this is how long? 23 years?
00:02:41
Speaker
Is that right? Since 2007. So I thought we could start with the book. So what's the new edition? What can folks expect? Yeah, I have the second edition of Visualize This is coming in June this year. It's an updated version of a book of Visualize This, the first edition, which I wrote. It was published in 2011, but I wrote it in 2007.
00:03:09
Speaker
nine, ten, I guess. And I started updating for the second edition as sort of like a very simple update. But then it started kept going because I hadn't written a book in a very long time. And I basically ended up changing all the examples and rewriting almost all the copies. So it's basically a brand new book. But like the root of my
00:03:36
Speaker
process that was developing in 2011 is still there in the second edition. Is it following the same structure of the first book, just updated for changes in the field and changes presumably in your perspective over the last decade or so?
Writing on Design & Statistical Integration
00:03:59
Speaker
Yeah, the structure is similar.
00:04:03
Speaker
some of the chapters have changed and I've rearranged things that I feel is better for learning how to visualize data. So the first edition, I kind of, I wrote it when I was in grad school and I had just finished my, my master's and I was headed towards a PhD in stat. And so the first edition was written as like a way of exploring data sets
00:04:29
Speaker
and focusing on the data and then going to the visualization versus, um, focusing on like the design parts and the methods and the chart types. Um, so as framed as this, uh, with data types. And so this, the second edition is still framed with data types and asking questions about data and answering questions and then sort of like iterating and exploring and then focusing on audience and designing for communication.
00:04:59
Speaker
Right. And because
Nathan's Data Journey & NY Times Internship
00:05:01
Speaker
you come from like a math statistics background, how do you think about writing the design aspects of stuff like that? I always coming from like an econ background, I always feel a little odd writing about design when like, you know, I don't have that training, just, you know, just kind of learning by doing. So like, if I go all the way back to when I started learning about visualization, it was, um,
00:05:28
Speaker
It was about reports. It was like I was analyzing the data and I just needed to kind of make really quick charts, put it in a report. And it was sort of like a second thought, like an afterthought. But then I started working with personal data collection, which was my dissertation topic. And it was more like people were collecting data about themselves as a diary aspect to it. And it was not so much about
00:05:56
Speaker
like analyzing yourself and trying to improve yourself in some way. It was more like, collect data by yourself, like you would write in a diary and then go back, you know, five, 10 years later and see yourself in that light in that way. And so I designed visualization in that way. That was more like, it was more contextual and it was more about like a cue to remember things that happened later on. So it was like a supplement to everything else in your life.
00:06:26
Speaker
And then from there, I ended up at the New York Times for a summer internship. And then so that kind of like pushed me along for just, it was only like six weeks, but it kind of like spurred my thinking along and that has sort of influenced me pretty much like throughout my career. And so like communicating to a very wide audience who doesn't work with data,
00:06:56
Speaker
And taking statistical concepts like distributions and, you know, core tiles or whatever, and trying to explain that to people who don't work with, who don't analyze data.
Communicating Statistical Uncertainty
00:07:08
Speaker
Right. And so that kind of just like ballooned from there and I ended up doing it for, you know, 20 years. Yeah. Um, do you have.
00:07:20
Speaker
Well, I guess recommendations in the book or in your head on how to more effectively communicate the uncertainty piece and the distributions piece. I know it's like a big challenge for people because like you said, I mean, you know, you say quartiles or quantiles to people, lots of people's eyes glaze over.
00:07:38
Speaker
And I know you do a lot of this on your site and in your blogs, and I want to come to some of the differences between the static work you do and the interactive stuff you do. But just generally speaking, how do you think about communicating uncertainty to folks who may not have a stats background? Yeah, so I always remember when I was in Vegas with some friends. And as a stats student, I learned everything I could about Blackjack and the probabilities. And then so I learned
00:08:08
Speaker
the perfect hand and when you would hit, when you would stay. And so
Practical Guide to Data Visualization
00:08:14
Speaker
I would tell them, hit, stay at certain points, and then that wouldn't always be a winner. They would sometimes lose, and they would get upset. Why did you tell me to do that when you claim that the odds were in my favor if I did this? Right, yeah.
00:08:37
Speaker
So it was like this very simple uncertainty problem because they already looked at kind of like the individual data points, but they had to, you know, extrapolate for like hundreds or thousands of, of hands. And so you always have to think about it long-term. Um, I was kind of like, think back to that. I guess people think they often think about the individual data points, like the current individual currencies. Um, but it's harder to think long-term.
00:09:06
Speaker
So I kind of link on to that part where to show the individual data point so that they can easily relate. They can find themselves in the data in some way and then extrapolate that in some way.
00:09:18
Speaker
Even with demographic data, someone can zoom in about themselves, about their own groups, and then they can sort of go from there and extrapolate and look at the whole entire population. But they can always have that, they have the anchor of themselves in that data. So it's like, it goes back to my personal data collection stuff where it's like,
00:09:42
Speaker
You have that individual point, that diary entry, thinking about yourself and then seeing how that relates and how you relate to the rest of the world. Right. Does the new book dive into, I mean, that's pretty, that's a pretty.
00:09:58
Speaker
nuance dataviz challenge. Does the book dive into those sorts of nuanced detailed aspects or it like how I guess the question is how do you view this book? Is it for
Data Visualization Tools & Flexibility
00:10:11
Speaker
a beginner in dataviz? Is it for someone who's been in for a while? Should they get the first edition and then the second edition that they roll in that sort of fashion?
00:10:20
Speaker
Don't get the first edition, get the second edition. Definitely. But buy two of them. No, don't buy yet. What? Yeah. So the second edition will be much more useful than the first because just because the tools are different, the design is different. Like when I wrote the first edition, we didn't have to think about mobile flash did flash still existed. Yeah. It was a time where
00:10:47
Speaker
like visualization on the web was kind of figuring its way out. And it was sort of like this translation of like PowerPoint. And so it was people coming from PowerPoint and then making graphics for the web. So it was like these really big infographics or long slide decks or slide shows. But now it's sort of like, I mean, you have phones. And that is a really big chunk
00:11:16
Speaker
of what you have to do and what you have to figure out. So I talk about designing for mobile and thinking about your audience and designing for the data that you have and the tools that you have. And it's very much about, it's for people who are figuring out like what they want to visualize, how they want to visualize it and what tools they're going to use. I try not to focus on any specific tool. I have
00:11:44
Speaker
So there's an introduction to a very wide variety of tools with concrete examples of using those tools. Because it goes back to when I learned visualization, it was a very informal process. I never took a class. And I've picked it up as I went along. And in my experience, it's been that you try to
Evolution of Data Visualization Books
00:12:08
Speaker
learn as many tools as you can and that makes you, your skills become extremely flexible in what you can do and the data that you can work with and the kind of like the screen size that you can work in. Yeah, yeah. So there seems to be in the database kind of book, literature, library and evolution in the books from the
00:12:34
Speaker
intro, tufty, few, my books would fall into this, like kind of like this intro, like how to, sort of like setting the baseline. And then there's newer books that I think are sort of evolving with the field, like Jen Christensen's book on science graphics and
00:12:53
Speaker
Fidget Settler's book on functional aesthetics and those sorts of books. Do you view this book as like the intro guide or is it within this sort of evolution of the field of we're moving beyond like these quote unquote rules into the practical and how to push all those boundaries? I would say it's for people who know what visualization is, but they don't know how to do it. And so they like,
00:13:22
Speaker
So when I wrote the first edition, it was very much like a handbook written to myself because, so it goes back to that New York Times thing. And I had, before that, it was all about charts for analysis and charts for exploration. And I'd done very little for charts for presentation and communication. So I, to try to prepare, like, in retrospect is totally worthless, but
00:13:49
Speaker
I tried to, I read as many design books as I could, and like the established design visualization books as I could. When I got to the New York Times, I was, I guess I was surprised that no one even, I don't know, I think I had it in my mind that they kind of pontificated about like the design aspects of charts, because the New York Times is like, they're the best.
00:14:17
Speaker
Yeah. So I was like, they're definitely thinking about all the colors and like all the design aspects and their audience are thinking about all these tiny things and they, you know, sip tea at a large table and like point out all the the mistake. But it was like, we just got to make this we just got to make it like, yeah, what do you think is best and then try to and then just go from there
Insights from the New York Times Experience
00:14:39
Speaker
and right.
00:14:40
Speaker
So there was like a bunch of trade offs of the technology that was involved and the people who were available, the data that was available. And so you kind of like take into all those trade offs into account. Yeah. And then you make something that is useful, as useful as you can. Right. Um, and so like visualize this was trying to get past the, all that big cluster of design products that I read that ended up not helping me. I mean, they helped me in my like original, like kind of base thinking.
00:15:09
Speaker
Right. Um, but then I like, I just needed to actually do something. Like how do I use illustrator? Um, how do I use art in the context of communicating data? And so I learned all of that, like while I was there. And then I've learned that over the years. And so visualize this is very much geared towards this is like step by step, how you do it. I'm not waving my hands at all. Like I'm not like brushing over things. Like this is, here's a code.
00:15:38
Speaker
here's the files, here's the data, and here's what I did. And you can do what you want to do. And next step, take your own data and do it. Use similar things. Right. Now I remember for a while you were writing code in R to create the graphics and then would pipe them over to Illustrator to add annotation and to clean that up. Do you still do that or has R
00:16:05
Speaker
And I know you do D three stuff and lots of other things on the, on the, on the flowing data site, but has our gotten far enough now where you don't need to take that extra step over to illustrator as much for me? No, no. Um, yeah, I think for a lot, a lot of people, I understand that, you know, it's, it's like really, really efficient to stick, stick within the software. Yeah. And our.
00:16:32
Speaker
is flexible enough where you can definitely reproduce any graphic that you want. So I use R and then Illustrator still. The advantage is that you don't always know how it's going to look. And you want to be clicking and dragging to see what a color looks like, what labels look like, how it's going to fit on the page, how it fits in the structure.
00:16:59
Speaker
And so illustrator or any illustration software is, it just lets me like see exactly what it's going to look like and be able to export it in different formats for the web. And what about the work that you do for your interactive pieces? Do you, for that task that you just mentioned of playing with color and moving things around, are you playing around with static versions in Illustrator or is you doing, are you doing all that in the code and doing it all in the browser?
Static vs. Interactive Visualizations
00:17:28
Speaker
So I always start with analysis and exploration, because I usually don't know what exactly I'm going to visualize or how I'm going to do it. So for that, R is my thinking language. I just pull the data into R, and then I explore and look for structures and visualization methods that would work. And if I land on something, and I think that interaction or animation is going to make it easier to understand, then I bring it
00:17:58
Speaker
I format the data, clean it, processes it, and then it goes to JavaScript, CSS, and HTML. After that, it's all browser work. I'm doing trial and error between the editor and the browser. I'm always curious about this because I do not code in JavaScript, but I'm always curious about the difference here.
00:18:23
Speaker
Is there a reason? Cause I could imagine creating like that first view in the browser and I can imagine, you know, making that as an image and going into Illustrator to sort of play around with annotation layers and different pieces, but it sounds like you're just working in the browser. And so how do you think about the difference in your process between a static version where you have like two tools versus a JavaScript and interactive piece where you're just kind of working in that single tool?
00:18:53
Speaker
For me,
Data Analysis with R & JavaScript Presentation
00:18:54
Speaker
the interaction part, the coming into D3 is always, is never my first step. It's always way on the tail end of my process. And so it's always in the language for me is R is to, so I just like don't have to think about the syntax or structure or anything, cause I've used R for so long. And so if I have a question, I answer it, I do it real quick and I can, you know, iterate in R, you know,
00:19:22
Speaker
for hours or days or whatever. And then I'll spend a much smaller chunk of time in JavaScript and coding it. Because even though I've been doing it for a while, I still have to look at things with JavaScript, with web stuff, because it's always changing. And it's also just not my primary
00:19:45
Speaker
um, like analysis language. And some people use, are able to analyze data in JavaScripts. And so more power to them.
00:19:58
Speaker
So it's always like, I will have a very concrete thing in my head before I head over to the website. Gotcha. Gotcha. Okay. Um, so like we talked about the beginning, you've, you've been running the flowing data sites since, since 2017.
Flowing Data Site & Audience Engagement
00:20:12
Speaker
And the site has grown in some ways, but sort of still has kind of a core set of things that you produce. There's like the daily email with an example that sometimes it mostly a data visualization, but other times it's just like interesting stories that are.
00:20:27
Speaker
connected to the field. And then you've got the membership piece where you could take tutorials and D3 and Excel and R, and then you've got your own database projects. And so I guess there's kind of two questions for you, like, you've kind of like kept this kind of just rolling ahead. And I'm curious about how you keep it fresh for yourself after all these years, and also like
00:20:53
Speaker
which part of your work is your most favorite thing to do out of all of those? Yeah, I get this question. I've gotten this question a few times. Yeah. Because it's been so many years and I.
00:21:08
Speaker
for some reason I find things to do. I have no plan for it because flowing data started as just sort of like a way to share things with my classmates. So I was at UCLA. I had to move to Buffalo after my master, but I was still working on my PhD. So I used flowing data, which was a domain name that I got.
00:21:31
Speaker
that I had to buy that was free because I had to use the hosting. And then so I started sharing things on flowing data and sending it to my classmates, then sort of kind of went there. So if you like, if you go back to like the very beginning of flowing data and look at the early posts, which is, it's kind of embarrassing for me, but it's me like trying to learn and figuring out what visualization I think
00:21:57
Speaker
Like there's probably something, some posts that I wrote, like what is visualization? Cause I probably didn't even know what, what I actually did not know what visualization. Right. Right. So it's like me working things out in my head. Um, and then it kind of, and now I make things and then sort of, it's suddenly kind of like caught on and other people found it. Um, and so just like throughout grad school, I worked on, I was learning visualization. So in the very
Creating Tutorials & Encouraging New Visualizers
00:22:24
Speaker
beginning, it was about like learning.
00:22:26
Speaker
visualization, what I wanted to do with it, and how it like applied to the rest of my work. And then I graduated. I finished my PhD. And then by that time, flowing data was enough to be my my full time work. Yeah. So I just kept going with flowing data. And that was part like part advertising, part memberships. And
00:22:54
Speaker
Actually, I think it might've just been advertising. As I say, I don't think the membership started later, as I recall. Yeah, I think membership started a few years later. So it was just advertising at the time. Yeah. And that was when independent publishers were able to get advertisers. Right. It was not just Facebook and Google AdSense. And so, but like, for me,
00:23:21
Speaker
the way that it just keeps going is that most of it is based on my curiosities. And so like, I'll see things or things happen to me, or I read something. And then there's always a question that comes up. And I and usually I can try to answer that with data. And so the curiosity keeps it going about the data itself, not so much like, what novel visualization can I use or like, what fun, like,
00:23:47
Speaker
How can I get more audience? How can I get more people to look at my stuff? It's more like, I have this question, I'm going to try to answer it. And if I have the question, then some people usually have that question too. Sometimes a lot of people have that question and sometimes a handful, but that just keeps it going.
00:24:07
Speaker
And do you, for the pieces that you create, I'm trying to tell you the last, there was one that you did, I think on, there was like a beast worm you did on marriage or fertility or something like that by occupation or something recently. When you create those, cause you're just, you know, trying to answer a question, do you always take the code and write a tutorial on it? Or is that just for four pieces where,
00:24:34
Speaker
you think there's a gap, or you've solved it in kind of an interesting way, or do you just always say, I'm just going to provide this tutorial because someone is going to want to build a Beastworm chart in JavaScript, and I might as well just give it to my members? It's not for every example, but if it's a chart type that I haven't written a tutorial for yet, or if there's a way I can generalize the very specific thing that I made, and I'll make it
00:25:04
Speaker
easier to use that people can apply their own data. That's a big thing. I want people to be able to take their own data set and visualize, use that method and adjust it. And not just like copy mine, like I provide my data and what I did, but I want them to know how to do it. So if there's a way to do that, and it's useful, then I write a tutorial or a guide for it. Or I'll just
00:25:31
Speaker
If I'm talking about a very specific process, I use the weekly newsletter called The Process and it just kind of walks through my thinking and what I was doing. Sometimes it's just like I toyed around with a new feature or new interaction that I haven't used before. And so I'll write about that and kind of...
00:25:54
Speaker
It's still me figuring it out back in 2007. What is this slider for? Is it useful? I don't know. We'll find out once it's done and see if anybody uses it. At this point, my approach with anything new is just throw it out and see what happens.
00:26:18
Speaker
Because it converges to something and you want it to diverge to new stuff.
Feedback Loop & Community Engagement
00:26:26
Speaker
Right. Well, I mean, it's also interesting because you have this sort of members piece where
00:26:33
Speaker
I kind of feel this way with my newsletter. I kind of feel like it's a little bit of a sandbox where I can sort of play around and test things out and get feedback without having to, you know, worry about the, I mean, not worry, but you know, worry about the getting it quote unquote, right or not, and just kind of try some things. Even within it's, you know, whatever, however many people it is, but just kind of try and see what people think.
00:27:02
Speaker
I mean, I worry about people who are new to visualization and they're excited about it and make stuff. And then they put it out there because they're excited about it and they're just learning. But the internet and social media and Reddit is really critical and sometimes mean about it. So I'm worried that
00:27:31
Speaker
that kind of criticism would detract people who potentially could be very excited and, you know, that excitement could turn into be like, very good skills and visualization become very good and, you know, make something
00:27:46
Speaker
that we've never seen before, but because, you know, a bunch of like armchair experts, like took them down and then it kind of like, like, I don't want to make this. I don't want to do this. So with like, with flowing data, just the public, the non-member part that I've been doing since the beginning is trying to like highlight the good stuff.
Public Reaction to Visualizations & Expectations
00:28:10
Speaker
I resist trying to criticize things too much. But I, for the,
00:28:15
Speaker
for the process, the members newsletter, those are people who are already into visualization, they're trying to learn it. And so they're like, more fair minded, I would say. Yeah, because they're trying to learn, they know what it's like to learn. And they're making things too. So
00:28:33
Speaker
I can be a little bit more critical in with that audience versus right just, you know, putting it out there and having 1000 people disagree with you. Right. Yeah. But it also feels like, especially in the process emails, a lot of the stuff that you critique in those emails is not like
00:28:51
Speaker
I didn't like this because of the color or it's it's not like a lot of the I don't want to say subjective stuff because some of those design things are not really subjective but a lot of the critiques that I see you write about are more about misinformation stuff that's misleading as opposed to what someone could construe as your subjective opinion as opposed to this person wrote this thing and like but the math is wrong right like so that that to me seems like a different
00:29:21
Speaker
animal in a lot of ways. Since flowing data has been around so much, people can go back and look at my stuff. You're in a glass house, you should be throwing bricks. I remember I know what it's like to make things and it becomes popular and just have people dump on it so hard.
00:29:48
Speaker
Like now that's funny to me, but I'm trying to imagine myself early on, like what if I was, um, what if I started flowing data, like in my first year and then I did that and someone, like all these people started dumping on me.
00:30:03
Speaker
I don't think Flowing Day would be around, because I would want to do it. You've
Data Underload Series & Interpretation Challenges
00:30:07
Speaker
had a bunch of things that get picked up by big news organizations. Have you ever had anything picked up where you've published something that you thought was pretty innocuous, like it's on basketball data, or it's on marital rates, and you get people dumping on you? Like, has that ever happened where you're just like, what is the problem? Yeah, so I have. Yes, that has happened.
00:30:34
Speaker
I have a series called Data Underload, which is a semi-weekly chart series. And it's a little different now than when it started. When it started, it was illustrative and not to be taken seriously. It was almost like a data comic. So I would, in my mind, make very obvious insights with made-up data and geometries.
00:31:03
Speaker
Um, but then, um, it might be misconstrued as reality because I didn't, it's not, it doesn't look like a, you know, like an SKCD comic or something like that. Um, it looks like someone spent time analyzing data and making charts. Right. I made a chart with like sleep habits when you're old and as you get older, so when you're young, the sleep habits are like.
00:31:28
Speaker
Very structured, you can become a teenager, it becomes like kind of all over the place. And then when you get older, it's sort of like kind of spotty, you wake up early and go to bed early and stuff like that. But it was, I don't remember the exact annotation that I used, but it was totally made up and it was subjective by me. And so I get a lot of emails, I think it was on Reddit or Digg or something.
00:31:54
Speaker
people were offended by it because I was making, I made the mistake of using sarcasm on the internet and assuming that people would take it into context.
Favorite Datasets & Influence
00:32:08
Speaker
But they didn't, so they get mad. Many years later, I took that same chart and I used actual data from the American Time Use Survey. I recreated the chart that I made subjectively and used real data.
00:32:24
Speaker
And people still got mad about it. It was so weird. Did it match up? It did match. It was very close. Yeah. But it was more granular because I had actual data to look at it. Right. And so a lot of the things that people get mad about is when you show a pattern, it doesn't match their experience. Yeah, yeah, yeah. And so it seems like it's wrong. Right. Which I understand, but it goes back to that blackjack problem where it's you have this single thing.
00:32:54
Speaker
like that's wrong. But you know, overall, we're looking at the whole population looking at everybody. Yeah. And so it's a little different for everybody. Right. And so I think that that thing is like
00:33:05
Speaker
you link on to the individual thing again that makes the everybody part much more understandable. Right, right. It was funny because you had a couple of posts and emails in the process where you were using the time you survey and I was like oh Nathan's like gotten like neck deep into the time you survey and he's like I'm just gonna just gonna
Future of Data Visualization
00:33:27
Speaker
ring as much stuff out of this as possible. And so I saw like, you know, there was like this series, there was like this series of posts, which I made a graph of like, Nathan's use of a particular data set, because like that one sort of like got really big, and then sort of tailed off again. So you did that, you had that a few years ago, I think with like, maybe the LEHD or something from the BLS, there was like a lot of like employment stuff that was showing up. So
00:33:52
Speaker
I kind of do appreciate like the evolution of seeing your use of different data sets sort of show up over, over time. I, I love that dataset. That is, that is my favorite data dataset. The time use one. Yeah. Yeah. Um, so we've, we've covered a lot of ground. So we started way back in grad school in 07, the visualize this volume one, and then the new edition and what you've been doing at the site. And I'm just curious to, to, to finish up here.
00:34:20
Speaker
Looking forward into the future of DataViz, whatever that's going to be, is there something that excites you as you see what people are doing or what you see that might come down the road?
Intuitive Data Visualization
00:34:33
Speaker
I guess because when I think of visualization in general, it's this abstraction of data and it's disconnected from the real world in that
00:34:47
Speaker
Data itself is abstract. The visualization is the abstraction of the data. And so people have to get from the geometries to the math and back to reality. So I think as the computers have gotten better, the tech has gotten better, and people understand data more, can kind of shorten that path between reality and the abstraction and making visualization
00:35:16
Speaker
less of like that thing that's on a report, that thing that is like on the side or is a separate part of an article or anything. It's sort of the data is just there. The chart or the visualization is just there and people can understand it in some way. So that has to do with, well, it has to do with people understanding data. And so I'm hoping that
00:35:45
Speaker
I mean, there are different like mediums that are coming out to kind of shorten that path, like virtual reality and augmented reality. I use a VR headset for fun. I wonder if like data could be brought into that way, where you're interacting in a more tangible way. And even just kind of like, instead of having charts, photos, videos, sort of like their own separate thing, they're sort of more intertwined. So data.
00:36:14
Speaker
Understanding the data and the context of the data is sort of like this Kind of like united thing versus right separate things. Yes separate separate things Yeah, and so I think we're kind of like getting towards that but sort of pretty long way to go. Yeah Yeah, I mean it is still surprising to me how many times you'll read some sort of report and they make a pretty clear argument in the report but then when you look at the graph the graph is just like this and
00:36:40
Speaker
description of, you know, this is the data in the graph and doesn't tell you anything about the argument that they're
Podcast Wrap-Up & Engaging with Flowing Data
00:36:46
Speaker
making. So yeah, it's still a battle to be fought, I think. Yeah. I think the, like the writer, like often people who are reporting the data don't also don't understand the data to its full extent. It has to do with like the uncertainty of it and the probabilities and kind of going beyond like the reported median and mean. That's sort of a big focus.
00:37:11
Speaker
If people can understand that it's very fuzzy, it's usually not certain. Almost everything in data is not certain. So if they can figure out how to communicate that in some way or to understand it and just have a general sense that this may or may not be the actual thing, I think it would be more useful. Yeah, for sure.
00:37:35
Speaker
Well, Nathan, congrats on the new book. Looking forward to opening it in June. And thanks for coming on the show, as always. It was great chatting with you. Yeah, thanks a lot for having me. It was good to be here.
00:37:48
Speaker
Thanks everyone for tuning into this week's episode of the show. I hope you enjoyed that conversation. You should definitely check out the Flowing Data website. You should definitely sign up for the free daily email or newsletter that Nathan sends around. You should also consider signing up for the paid part of the site. There's a lot of great resources in there. And of course, you should check out his new book, Visualize This, The Flowing Data Guide to Design Visualization and Statistics. So until next time, this has been the policy of his podcast. Thanks so much for listening.