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Episode #21: Edward Tufte
214 Plays9 years ago
On this, the 21st episode of The PolicyViz Podcast, I am very excited to welcome Edward Tufte to the show. As you might expect, I was excited to talk with Professor Tufte, so this episode is quite a bit longer than...
The post Episode #21: Edward Tufte appeared first on PolicyViz.
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Transcript
Introduction with Edward Tufte
00:00:11
Speaker
Welcome back to the Policy Vis podcast. I am your host, John Schwabisch. I'm here with a very, very exciting guest on this week's episode. I'm here with the one and only Edward Tufti. Professor, welcome to the show. Thank you. Thank you. Thanks for taking some time out. You're down here in Virginia giving your famous workshop standing in the huge ballroom. So I'm very excited to talk to you. Let's start with some of the work that you're currently working on. There are three or four main
00:00:41
Speaker
projects. First I'm doing my one-day course and I do about 30 courses a year and I still love it after all these years. We're at now 275,000 people have come to this over the years and I love teaching and the course is always kind of teaching my next book. They get all four books now but I'm teaching
00:01:11
Speaker
more out of my current manuscript. I've always done that. I've sort of been one book ahead and trying it out. It's a good way to learn to talk through things to a class. You have to understand things. You have to teach it and you understand it right. You gain more understanding. So I'm trying to gain self-understanding.
The Thinking Eye: Tufte's New Book
00:01:30
Speaker
And so I'm teaching the current book, documentary film,
00:01:36
Speaker
which is going so slowly that books and videos will probably be the same thing by the time I get it done, is called the thinking eye. I regard the most important interface as the interface between the light that comes into the eye and goes into the brain. So I don't like the software metaphor, but the idea is to improve the software that
00:02:02
Speaker
how people think. And all my work has kind of been about that secret, how to make people smarter, but this is overtly now about how to think analytically and judge evidence and reason about it and skills to develop an attitude. It's kind of on the background
00:02:25
Speaker
It's sort of an aspirational autobiography that I wish I could do all these things that I suggest that people should do. So I wish I could follow all my words. Some of the advice I try hard to do myself, it's very hard. So it's sort of an aspirational. So it's to improve analytical thinking.
Artistic Process: Creating and Shipping Work
00:02:56
Speaker
about seeing and how to see better, and then about reasoning about what you see, and then, and I think this is the most important, producing, making something of the seeing. As Steve Jobs said, real artists ship. That is, they make something, they execute. Why else in a way do it? And that's how it gets,
00:03:24
Speaker
has effects and becomes tested. And then there's a cycle after you go through this of editing, which again is now trying to see your own work with fresh vacation eyes, innocent eyes, and to think more and to think fresh about it and then produce. And that's very important. That goes over and over is that cycle.
00:03:49
Speaker
So is the book about the process through creating a data visualization or a graphic or is it sort of more general about creating, just creating, being a creative, producing a creative product? It can be teaching, it can be poetry, it can be writing, it could be a movie. It's about anything that somebody else looks at. Right.
00:04:14
Speaker
The data visualization is pretty narrow. I don't believe ever in pre-specifying a mode of production. In other words, a lot of people come and ask me at the course, I have to talk to the higher ups and they're a little impatient and a little stupid. How can I use visualization? And I say, do whatever it takes. Don't pre-specify. Don't even pre-specify a data set.
00:04:41
Speaker
And don't pre-specify for heaven's sake some method. You should use words and images and sock puppets and maps and charts. And that's a very important point in the book is the spirit of whatever it takes. And that's a very different spirit than being process-oriented.
Challenges in Visualization for Economics
00:05:00
Speaker
A lot of scholarship.
00:05:02
Speaker
is things like how to use data visualization to study economics. Well, the real question to me is how do you answer some economic questions and do whatever it takes by doing it. And that's one of the big points of the book, actually, is it's a little bit muddling through.
00:05:23
Speaker
But it breaks out of this, you know, rote march where you use conventional methods or pre-specified methods. And there's this big literature that's always a little soft.
00:05:33
Speaker
using data visualization to study Topic X. And they're usually not a contribution to either field. That they don't have quite enough content knowledge of either. And they try to gain credibility by combining these two things that people haven't done before. That's not a contribution.
00:05:53
Speaker
It's making a finding about economics or making a finding about data visualization. In fact, you bring a method to a new field. Of course, any competent scholar that's going to use different back to the thinking eye. And so that's going to have four essays, some on the idea about
00:06:21
Speaker
margins, and edges, and frames, and surrounds, and what goes on in the interaction between the frame context and the material inside. The Thinking Eye essay is the big thing. And there'll be an essay called Seeing Around. This is about seeing in three-space.
00:06:47
Speaker
I've been learning about that very hard for 10 years doing sculpture. And I've been showing a lot. I showed at Fermilab in the Aldrich Contemporary Art Museum. I had a gallery in New York for three years. And I've been doing something which is very hard for artists, especially sculptors. I've been selling pieces. And I do it because partly I think I understand design in flatland.
00:07:17
Speaker
Nobody ever will understand all of content in Flatland, but I understand Flatland. And you want a real interesting problem. See, we're good at, in Flatland, at reasoning about a cell in a spreadsheet or reading a poem. Can that same kind of analytical intensity apply to as we see the world? That focus and discipline and simplicity of content, which is not true,
00:07:46
Speaker
And so everything is so luscious and so complex and often so empirical of strategies in seeing in the real world. So when I have a flat, that's what our sculptures call a painting or a gravy, a flat on the wall, in maybe two or three months I don't see it anymore.
00:08:09
Speaker
And I have to move it to seal the fresh ice. Every time I go out in the sculpture fields, it's different. The light is different. I'm moving differently. The light is moving. The pace is moving. It's from different angles. It's raining. The dogs are running around. The season is different. And it is such a pure, wonderful, intense environment, contemplative, and often beyond words.
Tufte's Sculpture Farm
00:08:39
Speaker
When people, I have a 234-acre sculpture farm that will be left by my foundation in perpetuity, this open space, which shows the work of one artist, my work. And we have an annual open house. We're open one day for a year each year. And our fifth one is on October 17th in Woodbury, Connecticut, this coming Saturday on October 17th.
00:09:08
Speaker
And there are signs up in the driveway, diamond signs. And one of them is a driving officer that says, shut up and look. That's undiplomatic. But I mean it. That conversation uses probably half or two thirds of our brain processing power. Well, why not for a little while devote all the brain processing power to seeing? It's amazing the difference.
00:09:36
Speaker
Some of the land I have has old New England stone walls. We were out walking along them. And I said to my friend, let's stop talking. And after about 10 minutes, you could hear the faintest noise far away. And then the white seemed to change. And so you could look underneath the trees, and the shadows were no longer blown out to black.
00:10:06
Speaker
And the whites, like reflections off the snow, were no longer blown out to white. So it was like a perfect photographic day where you have a gentle filtered light, except it was happening in our brain. Because we were all our brain power. And so it increased the dynamic range of the eye, which is already pretty good. And so the blacks weren't blown out, and all over the whites. And I was just so thrilled by that.
00:10:34
Speaker
The only thing difficult was the footsteps of my companion on the leaves became annoying. Conversation couldn't cover that up. Right. And then also people change their experience when they don't talk. So a couple will start to split up and walk around the pieces and be possessed by them. And when you walk by somebody, they won't acknowledge you.
00:10:57
Speaker
under this condition, no talking. And so seeing graces had replaced social graces with that concentration. And this really came true. Paul Ekman, who's a great psychologist of emotions and nonverbal behavior and expression of emotion, visited the sculpture grounds. He's an old, dear friend who
00:11:22
Speaker
And he went out, and he meditates, and he's done books with the Dalai Lama and stuff. And he said they were beyond words. And I started crying, because he really got it. And I was so happy that somebody with his capacity, power, could do that. So that's the sculpture part. And a lot of things that I learned in Flatland
00:11:48
Speaker
similar. So, in Flatland, you have posity space, or figure and ground, and painting and diagrams and everything. Well, in the real world, you have the material, and you have airspace. But sculptures like me, and even really super ones like Richard Serra, agree or say that air is a material.
00:12:18
Speaker
And when I'm building something, they're great big pieces. I have two million pounds of stone metal. Listen, big pieces. There's more talk between my colleagues who are writing the backhoe and welding and so on about air than about the material. And more talk about the interface between air and the material and also the joints rather than the body material. And so it's kind of eerie.
00:12:45
Speaker
And it changes in three dimensions. It's not fixed the way it is on the planet land. And as you walk around and as the light changes. And so it's very empirical. You know you have to look at it. There are certain strategies for looking if they're a part of the thinking eye. So I'm looking at a three-dimensional object and we're kind of editing it. And I look at it close. And then I turn my back and walk away because you can't walk away fast enough to see how the thing changes.
00:13:15
Speaker
You have to, because it changes gradually. Or if you walk around a piece, you have to walk fast. So I turn my back on. And then I turn around suddenly after I've gotten 10 meters away.
00:13:28
Speaker
And so I see what's going on. I don't see this gradual shift. And so that's what I mean by a strategy for the thinking eye. It's very practical in a way about surprise your eye, keep your eye fresh, move at a pace where you can really detect change. So it's not too smooth. You can detect before and after change. And it's all hands on. I love the physicality. It's not like the computer screen.
00:13:56
Speaker
It used to be the way it was in design a long time ago, where you cut and paste it and have exacto knives and all of that. And there was a craft hand, but that's gone.
00:14:06
Speaker
probably for the good, but it's gone. Have you investigated or looked at some of the data art that people are doing? Unfortunately, I forget her name, but there's a woman who makes baskets, you know, baskets based on biological data. Have you looked at any of that? Have you gone to any of those exhibits to see what people are doing, sort of blending those two? I will, looking at this as an artist only, it's not first grade art.
00:14:38
Speaker
My view is that on all things, I only care about enormous excellence. I don't want to see anything else. I fought the war against stupidity for a while.
00:14:53
Speaker
Maybe about five percent is in fact work in stupidity against PowerPoint and chart time. But I'm not really interested in that. It attracts people's attention because it's critical. But I'm in my life and almost all my work is very positive.
00:15:10
Speaker
The whole basis is the identification of excellence, the careful study and celebration of excellence, and maybe the fourth time in looking and reading, maybe being a little skeptical. But there's so much to be learned from all the stuff in the past. And you see that in my books, which go back, you know, they're in 20 countries.
00:15:34
Speaker
You know, the best information designer in the universe is Galileo. He had terabytes of information through his telescope, you know, the resolution and his engravings and so on. And he just can see so well and he can think better than anybody and he's doing science and nature, you know. And whenever I come out of ideas, I look through his collected papers and, you know, oh my goodness, look at that.
00:15:59
Speaker
So as an artist, that kind of work doesn't count. That it's like an interdisciplinary gimmick in a way, where it's difficult in universities. Usually interdisciplinary work doesn't quite make it in either. I've done a little bit of data art, my Feynman diagrams, which are stainless steel.
00:16:24
Speaker
I showed them at Fermilab, so I had real physicists looking at them. That was really fun. Yeah, I bet that was interesting for them. And one of them said, pointed at one of my diagrams, and pointed at where subatomic particles interacted, and said, how did that miracle happen? In real science, the word miracle is not a good word, right? Not at all, no. And I had kind of half anticipated it.
00:16:53
Speaker
And I said, well, you know, all these little fudges, particularly constants in quantum mechanics. And so this is just a little fudge. It's a virtual, I understand. Like a virtual book spot. But that was so wonderful because it was reassuring that the science was OK. But I saw them really as an artist.
00:17:19
Speaker
So you've mentioned all the books and sort of looking back. So I want to ask you to look back.
Origins of Tufte's Teaching Tour
00:17:25
Speaker
The first book came out in 1983. 1983. So you've been teaching these classes for 30 some odd years. Well, I taught them at Yale for quite a while. I took early retirement. I left Yale when I was 59 because
00:17:42
Speaker
the teaching was going so spectacularly well. My joke is that I retired from Yale in order to teach and do research. That I was unencumbered by it, and I didn't have to worry about any outcome because of the successes and things of the books and the tour. And so I started teaching the courses in 1993. And I had two books then. I had Envisioning and then the first book.
00:18:13
Speaker
And it was called, the first tour was called the Kitchen Tour, because we were rebuilding our kitchen. I wanted to name it, you know, like the Rolling Stones. Nails tour. It's a very practical tour. This was the Kitchen tour. But also I wanted to get the word out. I didn't want to sit around New Haven waiting for people to come, because they're not going to come. You've got to go out on the road. And you were self-publishing, which at the time I suspect was a new sort of thing.
00:18:43
Speaker
Yes. It's a glorious story, but I didn't want to react to it. So that was the beginning. I had two books. I used to go out with the books in the trunk of my car and make a little pile. And now we have moving bands and roadies. And 700 gigs later and 270,000 people later, it's still
00:19:05
Speaker
I really love it. I even almost like flying because I'm going to a place where I do something I enjoy. Okay, so 20 some odd years of teaching this particular workshop. I'm curious.
00:19:18
Speaker
if you have seen a change in the types of people attend, the questions you hear, and then let me broaden that question into what you've seen in the field of visualizing information, of visualizing data, and how you've seen that sort of change over the last 30 years and maybe where you see it heading over the next 30 years. So that's a lot I know in one question. Well, I can say about the people who come. I do zero audience research. I pay
00:19:45
Speaker
I don't think about the audience at all. I'm not interested in reactions. I talk to a lot of people. I have office hours. But I'm too busy writing books and selling books and going to the course to do market research. We don't have time for it because we're too busy shipping stuff. And so I think the history of data visualization has undergone a tremendous transformation due to one big thing.
Evolution of Display Technology
00:20:16
Speaker
which is the resolution of our display devices is now getting close to the resolution of paper and is almost worthy of the human eye brain system. So in years past everybody was looking at the Dell laptop and the resolution of that was eight percent the resolution of paper. You can now get a 4K screen which is
00:20:41
Speaker
4.5 times HD for $800. It's the high resolution screens that made the smartphones possible, the iPad possible, the 5K Apple, which I just love, possible. And the software hasn't caught up at all. The graphics, a lot of them are still doing second semester product things in programming, but it's in a new language.
00:21:07
Speaker
There's not, you know, the software is way behind, particularly on the Windows side, way behind. And the hardware has made visualization possible. I love it. It's been so much because I wrote the books for paper. And most of all the design work was done under five-fold magnification. There are jokes in those books that you can only see, maybe three-fold.
00:21:37
Speaker
So it's like a dream for me that now I could see on a backlit screen, this spectacular color, and interact with it and enlarge it. And I'm a big fan of paper, but when I look at it on a good screen, it just glows. And art is just amazing. Not quite like the painting, but in some ways it's better than being there because you can be alone with it and enlarge it and almost touch it
00:22:07
Speaker
And a great piece you can't normally be that intimate with. So it's the intimacy. And the software, I think, has been generally disappointing that I have a diagram that shows the, it's from originally from the New York Times, that shows the weather for every day for a year with temperatures and highs and lows and normals.
00:22:35
Speaker
So somebody sent me a thing, hey, I did this in R. Well, my students in 1984 were running something called CalComp. In Harvard Graphics, there was a event and doing those things. And so what gain is there, in a sense, that is now being done in 20 different languages? The other thing is Brett Victor said something very interesting the other day, which is that
00:23:05
Speaker
There's a huge gap in the computer world between an idea and the implementation of it. And that hit home. I first made spark lines in the mid-90s when I was consulting for Hewlett-Packard. And they were going to have a units box in every patient's room, a medical box. Or maybe wheel it around, or maybe have one
00:23:33
Speaker
And they send me what are called flow sheets in medicine, which was like a spreadsheet time across the top in categories, empirical time, not in categories, but empirical measure. And then what happened, the name of what happened. And they'd be very long and very empty, in effect, spreadsheets. So I got these. And then in the margin, there was a little space. And I would accumulate all that history and make a little thing, a pencil thing, a little data word sparkling.
00:24:02
Speaker
And I was teaching my students that in the late 90s. Well, so last week on the Apple Watch, I saw sparklines, medical sparklines just right there. And it made it to Epic, which is a big medical data thing, two years ago. That's a long time for that to happen.
00:24:25
Speaker
Actually, it was interesting. It made it to Excel pretty early on. And they didn't screw it up. I was surprised. They did a very good job. It made it to Google Analytics pretty early on. But the actual day-to-day implementation, finally, it's been implemented quite a few places in sports, but still, to see it as a piece of brand-new, nice thing like that, that's a long time.
00:24:54
Speaker
But Brett Victor was saying that's the common, the things that people have thought of years and years ago are finally coming true. There's a lot of baggage and inertia, certainly in Microsoft, because they have a terrible baggage problem with all those people out there. Part of the baggage, though, is created by software houses that use damn proprietary formats. That is an awful problem.
00:25:22
Speaker
I've been working on some medical patient data. I did some papers long ago, and they're now coming in 94, partly because of recovery money. There was $20 to $30 billion in the recovery for medical records and for electronically. And that's been a delight to the accountants. But you see medical records, electronic records all over the place, but you don't see them in the survival data for patients.
00:25:52
Speaker
But I've been trying to push that along. And the big problem is there are at least a thousand devices in the hospital, each of which has its own damn proprietary format. It's like a tragedy. They don't talk to each other. They don't talk to each other at all. It's so awful. I've been paying attention to what's being done at Yale and what's being done major at Cleveland Clinic.
00:26:18
Speaker
So the way that Yale communicates, say, cardiology with Cleveland Cardiology is by something called FACTS. People under 40 don't know what that is. You go into an active practice, and there will be 10 side inches in a day of FACTS medical records, even though they both have the same electronic records. They can't talk to each other.
00:26:47
Speaker
And it's this problem that the programmers or the software houses think they own the content just because it happened to pass through their format. And the first person who accepted proprietary formats in the government and in universities and everywhere, that's terrible. And they should write contracts. We don't do proprietary, period. If you want this, only bidders will do it. Or they'll open. Or they'll open. Yeah. Because the cost now
00:27:17
Speaker
of blocking those things away. In the data visualization world, we've seen this sort of explosion in Tableau and Plotly. Do you think they're all just sort of swimming on the top of the waters, not really diving into the deep stuff that's really gonna help make a difference? I think that it can make a great deal of difference by information throughput. And you can certainly see the incredible difference it's made
00:27:45
Speaker
at the graphics news reporting in the New York Times. But scientists have had big data since Galileo, and they still, their work remains still at the cutting edge. The best visualizations in the world are found in the journal Nature, not at spy agencies or visualization shops or super computer centers. Practicing scientists who have them at Sun else in data. But in terms of
00:28:14
Speaker
the quality of the credibility and quality of inference, they don't make much difference. Because let's say there are 20 major threats to inference. And so one of them is small sample size, only one. There's still specification error, regression toward the mean, cherry picking, and all the other things.
00:28:41
Speaker
It can make some difference, particularly in how you communicate with people. But it hasn't made too much difference in exploratory. Real scientists have solved the problem already. And they're so far ahead of social science, except maybe for the times. But that's where to look. And a lot of what the times has done is not visualization.
00:29:10
Speaker
it's now being receptive to evidence other than words words and photographs that was what they did they now do imaging of data and they do a lot more data analysis and so that's what's made them and the visualizations are terrific and access to the world and they're wonderfully done but that's only part of the revolution it's bringing
00:29:40
Speaker
numbers to journalism. That was always hard for them to do. I trained some journalists quite a bit at the Woodrow Wilson School years ago. They were word people or photographic people. The Times really did it beautifully by calling them graphics news reporters. It was about news.
00:30:03
Speaker
And there are 40 people in that department, only one of them is a designer, my student Jonathan Corum, and the rest of them are statisticians or economists or reporters who have come and learned enough visualization. And some of the stories they write themselves. So it's done to report, not to use a method, but to report the news. So do you think it's about
00:30:29
Speaker
So you've mentioned it's an interdisciplinary work. In the new journalism, do you think it's about individuals who have all these different skills or is it about the team? Well, I think it's clearly about the team. You'll notice that on the really big projects there are five names at the time. Well, of course. But what's interesting is that some of the people who do the graphics
00:30:52
Speaker
also to the reporting. So Jonathan Corum often reads the scientific articles and so on and then turns them into visualizations by talking to the scientists and reading their work. And that integration between content and design has always been central to my work. I've dined out essentially on the insight that it's all about the content.
00:31:15
Speaker
not about the particular methods. I've done that for 30 years. I'm still shocked that people think that's an amazing insight. I mean, I always use Ben Schneiderman's quote, which says, visualizations about insight, not about pictures. It all rests on that, on the data.
00:31:30
Speaker
Yeah. Well, it's about explaining content, explaining things. Exactly. So where are we headed? So we've got all the new tools. Maybe people are starting to build these teams like The Times and other agencies. Where do you think we're headed in the field of, I'm not going to call it data visualization, also communicating data. Or reasoning about data. Or reasoning about data, right. Right. That's thinking. Yeah, or thinking about it. Yeah, thinking about it.
00:32:05
Speaker
My proposal, which I teach in my class and which is in my website, is called Maps Moving in Time. So it's 4K or 6K video. So no more sticks and ticks, you know, 10, 20, 30, sticks and ticks in still land. But we should have the information throughput of video. I never did there look at films.
00:32:30
Speaker
for many years from a data point of view because they were so much information through blood. It was scary. And they knew what they were doing. And they had an infinite amount of money. And so it was frightening. But now, so the demo I have is of the Swiss mountain maps, which are a contour map. And then there's a slow panning over them. And that slow panning
00:32:59
Speaker
leads to gentle 3D. Most 3D stuff gives you a headache, or you have to wear funny glasses, or sticks you in the eyes too aggressive. But the panning of the Swiss mountain maps, you can see a ski lift between two mountain peaks. It's just incredible. And so this has the information throughput and design power of a classic, probably one of the best maps ever done. There are contour lines.
00:33:27
Speaker
Amazing, incredible typography. It's the Swiss Alps great content too. But combining that with essentially infinite scroll and probably not letting the user interact too much because they'll get impatient. It's a slow pace and so they're getting this tremendous information throughput on a 4K or 6K screen. We're talking 6,000 by 4,000 and they're just amazing things.
00:33:56
Speaker
And they're getting that with an intense design, a great map, a contour map, with video throughput.
Future of Data Visualization: Video and Resolution
00:34:05
Speaker
And so you're cutting edge of resolution, of video, and at the classic cutting edge of the layout. And so I've been doing some things. And there are demos on my website of maps moving in time. And so it's a complete shift.
00:34:27
Speaker
The problem is that the production requires tremendous amounts of data, and the computational power is almost trivial now, but it requires lots of data, and it's not easy editing video. It's probably the square of the hard, you know, compared to Stillhand or something like that.
00:34:51
Speaker
Or maybe you're going from things to areas squared. But movies, there's kind of almost volume. So it's almost by the cube slightly. And so again, it's saying that the gain is in the hardware video. Well, software video, but hardware, and then in the screens. And so I don't know if people are ever going to do their own little home data.
00:35:20
Speaker
visualizations but for in real science and at the New York Times and for serious big-time presentations that's what's it's already starting to happen and that's really powerful so you have the patience and care with the general scan but the rush of information with the video and so it doesn't have the impatience of television which is you know they or they don't jump and stuff there has that
00:35:48
Speaker
kind of concentrated analytical eye with the slow panning. And it's the fundamental human act of thinking, which is in this swamp of material, you want to find the diamonds, the targets, the intervention points, the relevant points. And so it's aimed, like all information displays, to support human cognitive skills. To me, the fundamental principle of design is to
00:36:19
Speaker
let the user perform the fundamental analytical tasks that people engage in when they reason about data. So understand causality, make comparisons, and think in a multivariate way. And that's the point. And that leads to designs. And do you think that visualizations
00:36:44
Speaker
can't help people better understand the data, sort of going backwards a little bit from I'm going to present something to the user and still help them understand sort of the whole network of how that visualization was created. I mean, that's either teaching statistics through visualizations or teaching sort of complicated topics or big numbers or whatever it is through visualization. Do you think people can ultimately understand what's under the hood? Well, it's fairly high powered stuff and I assume that
00:37:17
Speaker
just as as places like Apple and Twitter have made many consumerized many things that they will do it and there might be some nice things and there'll be some people probably young bright young people who will do amazing things with accessibility to those tools and so
00:37:38
Speaker
They're going to be much better at these things than we are. I just hired somebody who's 24 and she's out of this world. She's a humanist and was studying medieval, but she knows R. That's scary. She knows LaTeX. She knows R. Holy wow. So I think, as you think, as students, young people who are
00:38:06
Speaker
quick learners and so on. You've got to learn these things, especially things like coding or things like that. Before age 25, it's all over. I've tried to learn R several times. I can't even hardly get past data entry. I kind of get somehow a loop just like that. Just kidding. It's a spreadsheet. So of course, it'll somehow be not commodified. That's a terrible one.
00:38:36
Speaker
And I hope it won't be like R, that it will be accessible. It will have a general thing. And there won't be an interest in the kind of. So something that's more universal for even the lay user could sort of go in and. Right. So if they can, OK, so they can make a reasonably luscious statistical graphic, some kind of fairly detailed map, and put their data on it.
00:39:04
Speaker
And then, OK, they pan over. So that's now giving, essentially, it's giving interaction, but it's not letting the user completely set the pace. I think that's very important, that it's hard enough to think and also to do interaction. So often that's kind of separation.
00:39:34
Speaker
most of my tools from R and from Illustrator and things like that because I stand about three feet away and I say, Elaine, move that a little bit to the right. I say to my design assistant because I find it hard to do a serious program
00:39:56
Speaker
you know, at the level of Illustrator or R or InDesign and think about that and get frustrated now and then and have to solve a puzzle and also to reason about the data, which I'm trying to do. Right. And so I think, you know, that now it may be that some editing could be, I don't know what could be done
00:40:17
Speaker
by voice and you could say do this or something. But you're asking for a very hard combination because these are big league serious programs and they always have to have a fair amount of them. And there's no real big solution.
00:40:32
Speaker
It's like film editing. The distance between a real film at Avid or Final Cut and the difference between what we can do is a lot narrower now on our devices, but it's not simple. It's not simple and it's not real film. It's inherently complicated and hard. And so clever interface won't help much. I think what it means is if you want to do that stuff, get a bright summer, a geeky summer intern.
00:41:02
Speaker
a young person who knows how to do the code. So you spend a lot of time, I would guess, looking at what people are creating and the conversations that go on. When you see what people are talking about, when either the creators are talking about what they're making or people who are critiquing them, do you have a sense of how the community could do a better job of that?
00:41:30
Speaker
And maybe they're not. Maybe they're doing a great job. Maybe the conversation is great. But do you think there's a community there where perhaps there's changes to be made and ways in which the whole field can sort of move forward? I think if you look around, there are some really excellent critiques. And that's just part of my usual strategy, to find really great things and think about
00:41:55
Speaker
So the science article that took apart Google Flu is an amazing piece done by people at Harvard and MIT. And it makes the excellent and obvious point that of the 20 threats to validity, little data
00:42:17
Speaker
is only one. And so Google made errors, naive econometric errors. You know, model searching and they had a very brittle model which of course crumbled up on retail. Classic. And they're working with time series and stuff and the econometrics was naive. And the one thing I liked, or one side effect of the science article,
00:42:43
Speaker
about Google flu was, it did use the word hubris quite a bit. That's what I mean by a really great critique. I don't think, I think much better than critique is the style of my books, which is very, only five percent in the war against stupidity, which is
00:43:06
Speaker
rather the best critique is to make something wonderful, to celebrate excellence, and say, this is our standard. So one of the very important things I teach in the classes is any diagram that you make, any table, any statistical graphic, any project management chart, anything that looks like any kind of visualization at all, you put it next on your screen to Google Maps.
00:43:34
Speaker
And you ask your IT department and your software people, how come I can't put 120 words anywhere I want on my display? How come I can't use light colors so I have five different layers? How come I have 40% computer administrative debris in my things and they have personally none? How come I have to put, because of corporate guidelines, five logos on my thing? They have one little tiny thing.
00:44:01
Speaker
play in the big leagues. And so that's what I mean by this kind of comparison. And it's not critiquing the thing, it's saying here is a model of excellence. Usually when people are faced with a model of excellence, they say it's too difficult and the higher ups won't understand it. And then in response you say Google
00:44:23
Speaker
is the most widely used image in human history. And you can bet some fresh every couple of days. It's in every language, every country in the world. And people are using it not just to look at, but to find out where they are and how to get there. And people use that information richness all the time. And so that's the kind of critique. I mean, occasionally it's nice to take something apart, especially if they are made silver does some nice critiques.
00:44:51
Speaker
And, you know, you're usually taking apart the forces of evil. You should remember when you do that is that your allies cheat or do bad things just as much as your opponents. That your opponents are not uniquely, and that statistically or cheating, it's just as true of your allies. And so, but a lot of critiques are motivated by
00:45:17
Speaker
disagreement with the substance. And it now becomes a statistical question. But you've got to remember, give me a critique of something you like philosophically. There is a role. It's very active, of course, in peer review. That happens. And it's also active is that the junk is never forwarded again. That's the real sorting of quality.
00:45:47
Speaker
I probably have 10 or 15 years left. I don't want to spend my time looking at mediocre stuff. There's so much excellence left and the chances are that looking at five pages of Galileo will be much better than looking at a week of Twitter or a month of Twitter or a year of Twitter. I enjoy Twitter and it's great fun.
00:46:17
Speaker
And it diversifies my point of view when I get to see all this stuff. But I have a very long time horizon, both forward, in that I'm trying to do stuff that people are going to read for a long time. The challenge is to do forever knowledge.
00:46:36
Speaker
and plus going back in history. And why should this day now be any better than someday five years ago or a day back in Galileo Day? And so there's such a recency bias to our thinking and to the web and to obviously Facebook and to Twitter.
Pursuing Timeless Knowledge
00:46:58
Speaker
It's enormous. And everything is stacked by the most recent on top so you don't
00:47:04
Speaker
I have my website that threads or organize the other way. The oldest is on top and it grows as you go down. I think one of the very important things about the thinking eye is to think hard about your time horizon and what time horizon you're working for but also from what time horizon. It took me until I was in my early thirties to realize that I did not have to read the quarterly journal of such and such
00:47:31
Speaker
because it would turn out there was only one article out of 25 that would ever be cited again. And even the best article might be cited only a few times and they wouldn't even be cited by the author's mother. So it's to get out of that daily flow that many of us have to live in. And I've been really lucky to escape that and to be able to have this
00:47:55
Speaker
long, you know, 50 time horizon. And there's so many wonderful things back then, or back, you know, last week. And they're often more wonderful than today. And that was a great help when I learned that, is to think very long, far ahead. Don't do proper nouns, do verbs, do principles, do forever things. Like some real scientists do, with nature's laws, do that. And then
00:48:24
Speaker
world of the past, really smart people have been doing visualization and explaining and just to spend a day in Galileo's collected papers and just look through the illustrations. There are like 26 volumes and I just do 10 pages and there's another idea.
00:48:48
Speaker
Well, I think on that note of looking into the future can inspire us. I'd like to thank you so much for coming on the show. Okay, terrific. Thank you. And thank you all for listening to this episode of the Policy Viz Podcast. Until next time, thanks a lot. Bye-bye.