Transcript
Speaker: Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. I hope you are well, safe, and healthy. And thanks for tuning back into the show. On this week's episode of the show, which is the second to last episode of season 11 of the podcast,
Speaker: I am reposting an episode from many, many years ago with Edward Tufte. Now, for those of you who may be somewhat newer to the field, this name Edward Tufte may ring a bell, may not ring a bell, maybe something you've heard about somewhere, but not quite sure.
Speaker: So I wanted to raise this episode back up because in our sort of new era of data visualization tools and of course artificial intelligence, ah really sort of supercharging a lot of the work that we do and and maybe even putting some of the work that we do at risk, I thought it would be interesting to look back at one of the ah trailblazers in the data visualization field.
Speaker: Now, if you don't know, Edward Tufte was a statistics professor at Yale University, and he wrote a couple of books in the early 1980s about being a more effective data communicator. And what he would do is he would go on the road and do these large, basically workshops in these large ballrooms.
Speaker: ah You would go and you get these four books. ah Ultimately, he created wrote these four books that you would get, and he would ah lecture and pontificate about the best ways to visualize data. from his perspective. And I think over the last five to 10 years, a lot of his work has come under fire because he does sort of portray it as the objective truth based in research and based in fact, when a lot of his work is based in his opinion and his personal aesthetic, which of course is part of all of our data visualization toolkits and how we create our own data and how we communicate our own work.
Speaker: A lot of it is based on our own preferences for fonts and layouts and color. But I think it's interesting to just think back about where data visualization started and where it has come over the last several hundred years.
Speaker: And Tufti is one of the first, I would say, mass educators in the data visualization field. And so Tufty comes down to Northern Virginia ah still to this day, about once a year.
Speaker: um And this time, several years ago, I reached out to him to see if he would be interested in coming on the podcast. And so I met him down at the Courtyard Marriott in Northern Virginia, where he does his workshop.
Speaker: His workshops are held in a huge ballroom. Hundreds of people sit this ballroom. has two huge screens. And so I met with him there in the evening after his workshop had concluded for the day.
Speaker: And we sat at the table. um I didn't have the sort of pro ah podcast recording equipment I have today. um Of course, most of my podcasts are recorded from the comfort of my own home via Zoom or Zencastr.
Speaker: But we met in person and he had a couple beers while we talked and it was a very nice, lovely conversation. um And as you're going to hear, I asked him all about his approach to data visualization, his approach to aesthetics and his approach to teaching data visualization, which at the time was where I was really interested in pursuing. What are some of the best techniques to help people be better data communicators, be better data visualizers?
Speaker: And so what I've done for this episode is improve the sound. i went through and edited the sound with some help from my sound editors brought up the level so that it's a bit clearer, a bit easier to hear. And I think if you have any interest in knowing where our field came from to how it has blossomed into what it is today, i think you're going to find a lot of interesting content in this episode.
Speaker: So without further ado, this is my interview with Edward Tufte, reposting from several years ago, but you can only find it right here on the PolicyViz podcast.
Speaker: Professor, sir welcome to the show. Thank you, thank you. Thanks for taking some time out. You're down in here in Virginia giving your famous workshop, um standing in the huge ballroom. So I'm very excited to talk to you. Let's start with um some of the work that you're currently working on ah There are three or four main projects.
Speaker: First, I'm doing my one-day course, and I do about 30 courses a year. I still love it after all these years. We're at now 275,000 people have come to this over the years.
Speaker: And i love teaching. And the course is always kind of teaching my next book. I get all four books now, but I'm teaching more out of my current manuscript. And I've always done that.
Speaker: I've sort 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 start you have to understand things. You have started to teach it and you understand it, right. You gain more understanding. So I'm trying to gain self-understanding.
Speaker: And so I'm teaching. The current book, documentary film, 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.
Speaker: I regard the most important interface as the interface between the light comes into the eye and goes into the brain. So I don't like the software metaphor, but the the idea is to improve the software, how people think.
Speaker: And all my work has kind of been about that secret, how make people smarter. But this is overtly now, about how to think analytically and judge evidence and reason about it and skills to develop attitudes it's kind of on the background it's sort of an aspirational autobiography that I wish I could do all these things that I suggest that people should do with it so I wish I could follow all my words some of the advice I try hard to do myself but it's very hard yeah so it's sort of an aspirational and the
Speaker: and the So it's to improve analytical thinking. First, about seeing and how to see better.
Speaker: And then about reasoning about what you see. And then, I think this is most important, producing. Making something of the seeing.
Speaker: As Steve Jobs said, real artists ship. is, they make something, they execute. Why else in a way do it? yeah And that's how it gets. It has effects and becomes tested.
Speaker: And then there's a cycle, after you go through this, editing, which again is now trying to see your own work with fresh vacation eyes, innocent eyes, and to think more and to think afresh about it and then produce.
Speaker: That's very important. That goes over and over is that cycle. 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, of producing a creative product?
Speaker: 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.
Speaker: The data visual is pretty narrow. yeah This is about, and I don't believe ever in pre-specifying a mode of production. In other words, a lot of people come and ask me,
Speaker: course, I have to talk to the higher-ups, and you know they're a little impatient and little stupid. How can I use visualization? And I say, do whatever it takes. know Don't pre-specify. yeah Don't even pre-specify a data set.
Speaker: And don't pre-specify, for heaven's sakes, a method. You should use words and images and sock puppets and maps and charts. and And that's a very important point in the book, is the spirit of whatever it takes.
Speaker: And that's a very different spirit and the process oriented a lot of scholarship is things like how to use data visualization study economics well the real question to me is answers and economic question yes and do whatever takes right right and that's one of the big points the book actually interesting is is so a little bit modeling through but it breaks out of this rote march where you use conventional methods or pre-specified method.
Speaker: And there's this big literature that's always a little soft, using data visualization to so to study topic X. And they're usually not a contribution to either field.
Speaker: 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.
Speaker: It's making a finding about economics or making a finding about data visualization. In fact, you bring a method to it. Of course, any any you know competent scholar buts may use different.
Speaker: right Back to the thinking on... And so that's that's going to have four essays, some on the idea about margins and edges and frames and surrounds and what goes on in the interaction between the frame context and the material inside.
Speaker: The Thinking Eye essay is the big thing. And there'll be an essay about called Seeing Around, which is about seeing in three space. And I've been learning about that very hard for 10 years, doing sculpture.
Speaker: Right. And I've been showing a lot. i showed at Fermilab and the Aldrich Contemporary Art Museum. And 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 like pieces. Right.
Speaker: And i do do it because partly I think I understand design in Flatland. Nobody ever will understand all of the content in Flatland, but I understand Flatland.
Speaker: And you want a real interesting problem. See, we're good Flatland and flat land that reasoning about a cell on a spreadsheet or reading a poem.
Speaker: can that same kind of analytical intensity apply to as we see the world. yeah That focus, discipline, and simplicity of content which is not true the world.
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 a sculptor's call a painting or an engraving, a flat on the wall,
Speaker: in maybe two or three months I don't see it anymore. And I have to move it to see it with fresh eyes. 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 piece is moving, it's from different angles, it's raining, the dogs are running around, and the season is different.
Speaker: And it is such a pure, wonderful, intense environment
Speaker: contemplative, and often beyond words. People, have a 234-acre sculpture farm that will be left by my foundation in perpetuity.
Speaker: It's 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.
Speaker: This year. And our fifth one is on October seventeenth in Woodbury, Connecticut, ah saturday coming Saturday on October 17th. And there are signs up in the driveway, diamond signs. e One of them is a driving up, says, shut up and look.
Speaker: yeah It's undiplomatic, but i I mean it. yeah That conversation uses probably half or two-thirds our brain processing power.
Speaker: Well, why not for a little while devote all the brain processing processing power to seeing? It's amazing the difference. Some of the land I have has old New England stone walls.
Speaker: 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.
Speaker: And then the light seemed to change. And so you could look underneath the trees and the shadows were no longer blown out to black. And the whites, like reflections off the snow, were no longer blown out the white.
Speaker: So it was like a perfect photographic day where you have gentle filtered light, except it was happening in our brain. right Because we were all our brain power. And so it it increased the dynamic range of the eye, which is already pretty good. yeah And so the blacks weren't blown out and nor were the whites.
Speaker: And I was just i just was so thrilled by that. The only thing difficult was the footsteps of my companion on the leaves became annoying. Conversation couldn't cover that up. Right. Right. And then also people change their experience when they don't talk.
Speaker: 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. under this condition, no talking. yeah And so seeing graces have replaced social graces with that concentration.
Speaker: And this really came true. Ekman, who was a great psychologist and of emotions and nonverbal behavior and expression of emotion, visited the sculpture grounds. He's an old dear friend and he went out and meditates and he's done books with the Dalai Lama and stuff.
Speaker: He said they were beyond words. And I started crying because he he really got it. right And I was so happy that somebody with his capacity, power, could do that.
Speaker: So that's the sculpture part. And a lot of things that I learned in Flatland are similar. So in Flatland, you have positive space, figure and ground, and painting and diagrams everything.
Speaker: Well, in the real world, you have the material and you have air space.
Speaker: But sculptures like me and even really super ones like Richard Serra agree or say that air is a material.
Speaker: And when I'm building something, there are great big pieces. I have two million pounds of stone megaliths, and it's big pieces. There's more talk between my colleagues who were running the backhoe and welding and so on about air than about the material.
Speaker: And more talk about the interface between air and the material and also the joints rather than bodying the body and the material. And so it's kind of eerie. And it changes in three dimensions. It's not fixed the way it is on flat land.
Speaker: And if you walk around, the light changes. And so it testing it's very empirical. yeah You know you have to look at it. There are certain strategies for looking that are part of the thinking eye.
Speaker: So I'm looking at a three-dimensional object and we're kind of editing it. And 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.
Speaker: You have to because it changes gradually. Or if you walk around a piece, you have to walk fast. and So I turn my back on it. And then I turn around suddenly after I've gotten 10 meters away.
Speaker: And so I see with fresh eyes. yeah 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.
Speaker: Keep your eye fresh. oh Move at a pace where you can really detect change so it's not too smooth. because You can detect before and after right change. So all this, hands and it's all hands-on. i love the physicality.
Speaker: It's not like the computer screen. Right. It used to be the way it was in design a long time ago where you cut and basted and had X-Acto knives and all of that. And there was a craft hand, yeah but that's gone. yeah Probably for the good, but it's it's gone.
Speaker: But and have you investigated looked at some of the the data art that people are doing? Unfortunately, I unfortunate ah forget her name, but there's a woman who makes baskets you know baskets based on biological data. have you 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?
Speaker: ah i will Looking at this as an artist yeah only, it's not first-rate art.
Speaker: My view that on all things I only care about enormous excellence. I don't want to see anything else.
Speaker: I fought the war against stupidity for a while. Maybe about 5% is in fact the war against stupidity against PowerPoint and Char-Chunk. But I'm not really interested in that.
Speaker: It attracts people's attention because it's critical. but i'm in my life, in my, almost all my work is very positive. And the whole basis is the identification of excellence, the careful study and celebration of excellence, and maybe the fourth time in looking or reading, maybe being a little skeptical.
Speaker: 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, there are 20 countries and You know the the best information designer in the universe is Galileo.
Speaker: And 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, yeah you know.
Speaker: And whenever I come out of ideas, I look through his collected papers. And, you know, oh, my goodness, look at that. Look what did, yeah. Yeah. So, as an r as an artist, that kind of work doesn't count.
Speaker: 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, and...
Speaker: I showed them at Fermi Labs, so I had real physicists looking at them. That was really fun. Yeah, I thought that was interesting for that. And one of them said, and but 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. though And I had kind of half-anticipated I said, well, you know, all these little fudges, particularly constants in quantum mechanics.
Speaker: And so this is just a little fudge. It's a virtual understanding. Kind of like that. Like a virtual photon. um But that was so wonderful because it was reassuring. Yeah. that like The science was okay.
Speaker: But I saw them really as an artist. Right. Right. So you've mentioned all the books and sort of looking back. So so I want to ask you to look back. you've been the first book came out in 1986? Three. 1983. So you've been teaching these classes for 30 some odd years.
Speaker: Well, I taught them at Yale for quite a while. I took early retirement. I left Yale when was 59 because... fifty nine because The teaching was going so spectacularly well.
Speaker: My joke is that I retired from Yale in order to teach and do research. Okay. That I was unencumbered by it. Right. And I didn't have to worry about it because of the successes of the books and the tour.
Speaker: And so I started started teaching the courses in 1993. Okay. And I had two books then. I had Envisioning and then the first book.
Speaker: Mm-hmm. 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. Yeah, right, right. Nails tour. It's practical tour. Right, this was the Kitchen Tour.
Speaker: and 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. Right. You've got to go out on the road. and you And you were self-publishing, which at the time I suspect was a new sort of thing. Yes.
Speaker: Yes. It's a glorious story, but didn't want to react to it. So that was the beginning. had two books. used to go out with the books in the trunk of my car, make a little pile.
Speaker: And now we have moving vans and roadies. And 700 gigs and 270,000 people later, it's still... seven hundred gigs later and two hundred and seventy thousand people later it's still I really love it. i get I even almost like flying because i'm going to I'm going to a place I do something I enjoy. Yeah, yeah.
Speaker: So, okay, so 20 some odd years of teaching this particular workshop. I'm curious. 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 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.
Speaker: So that's a lot I know in one question. Well, I can say about the people who come, I do zero audience research. like pay I don't think about the audience at all.
Speaker: I'm not interested in reactions. I talk to a lot of people. I have office hours. But I'm too busy yeah ah writing books and selling books and going to course to do market research. yeah 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 tremendous transformation due to one big thing, 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.
Speaker: So in years past, everybody was looking at the Dell laptop and the resolution of that was 8% of the resolution of paper. You now get a 4K screen, which is 4.5 times HD for $800.
Speaker: 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.
Speaker: The graphics, a lot of them are still doing second semester product things and programming, but it's in a new language. e there's not software is way behind, particularly on the windows side way behind and it's 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 under ah done under five-fold madame magnification there are jokes in those books that only you can only see yeah maybe three-fold and
Speaker: and So it's like a dream for me that now I could see on a backlit screen back in a spectacular color and interact with it and enlarge it.
Speaker: And you know I'm a big fan of paper, but when I look at it on a good screen, it just glows. Yeah, it does. 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.
Speaker: and you know a great piece you can't normally be that intimate with. So it's the intimacy. And software, I think, has been generally disappointing that I have a diagram but that shows the, it's originally from the New York Times, that shows the weather for every day for a year with temperatures and highs and lows and normals.
Speaker: And so somebody sent me a thing. Hey, I did this in R. yeah Well, my students in 1984 were running something called CalComp. You know, in Harvard Graphics, there was ah you know that. And doing those things.
Speaker: And so what gain is there, in a sense, that it's now being done in 20 different e languages? The other thing is Brett Victor said something very interesting the other day, which is,
Speaker: that there's a huge gap in the computer world between an idea and the implementation of it. And that hit home.
Speaker: I first made Sparklines in the mid-90s when was consulting for Hewlett Packard and they were going to have a Unix box in every patient's room.
Speaker: Medical box. and we Or maybe wheel it around. Or maybe we had one. 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 categories, but empirical measure, and then what was what happened, the name of what happened.
Speaker: And they'd be very long and very empty, in fact 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. And so but and so And i was teaching my students that in the late 90s.
Speaker: Well, so last week on the Apple Watch, I saw sparklines, medical sparklines, just right there. but And it made it to Epic, which is big medical data thing, two years ago. That's a long time for that to happen. Right.
Speaker: It made it, actually it was interesting, it made it to Excel pretty early on. I was really, 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.
Speaker: But the the actual day-to-day implementation, finally, you know that I mean, it's it's been implemented quite a few places, in sports particularly, but but still, to see it as a piece of brand new, a nice thing yeah like that, that's a long time. Yeah.
Speaker: and 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, in certainly in Microsoft, because they have terrible baggage problem with all those people out there.
Speaker: Part of the baggage, though, is created by software houses that use damn proprietary formats. That is an awful problem.
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 recovery for medical records and and for electronic.
Speaker: And that's been a delight to the accountants. But you you see medical records, electronic records all over the place, but you don't see them in the survival data for patients.
Speaker: But I've been trying to push that a little. And the big problem is there are at least 1,000 devices in the hospital, each of which has its own damn proprietary format.
Speaker: It's like a tragedy. and they don't talk to each other. They don't yeah 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 age at a major Cleveland Clinic.
Speaker: So the way that Yale communicates, say, cardiology, with Cleveland cardiology, is by something called fax. People under 40 don't know what that is.
Speaker: You go into an active practice, and there will be 10 side inches in a day of faxed medical records, yeah even though they both have the same electronic records thing.
Speaker: They can't talk to each No, they can't to each other, right? And it's this problem that people that the programmers or the software houses think they own the content just because it happened to pass through their format.
Speaker: And the first person who said accepted proprietary formats in the government and in universities and everywhere, that's terrible what they did. They should write contracts. We don't do proprietary, period. If you want this, only bidders who do open. Because the cost now...
Speaker: because the cost now Of locking those things away. Yeah. Right. 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 the deep stuff that's really going to help make a difference?
Speaker: I think that it can make a great deal of difference by information throughput. And ah you can certainly see the incredible difference it's made at the graphics news reporting at the New York Times.
Speaker: But scientists have had big data since Galileo, and their work remains still at the cutting edge. Best visualizations in the world are found in the journal Nature, not at spy agencies or visualization shops or supercomputer centers. It's practicing scientists yeah who have immense amounts of data.
Speaker: But in terms of 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 small sample size only one there's still specification error regression toward the mean cherry picking and all the other things and so it it can make some difference, particularly in how you communicate with people.
Speaker: But it hasn't made too much difference in exploratory. yet Real scientists have solved the problem already, and they're so far ahead of social science, hu except maybe for the Times.
Speaker: But that's where to look. And a lot of what the Times has done is not visualizations, it's now being receptive to evidence other than words.
Speaker: even ah And words and photographs. That was what they did. They now do imaging of data and they and they do a lot more data analysis.
Speaker: And so that's that's what's made them. And the visualizations are terrific and and access to the world and they're wonderfully done. But that's only part of of the revolution. e It's bringing...
Speaker: numbers to journalism. yeah that That was always hard yeah to for them to do. I trained some journalists quite a bit at the Woodrow Wilson School years ago, and you know they were they were word people or or photographic people.
Speaker: And the Times really did it beautifully by calling them graphics news reporters. It was about news And there are 40 people in that department. Only one of them is a designer, my student, Jonathan Corum.
Speaker: And the rest of them are statisticians or economists or reporters who have come and learned enough visualization. yeah And some of the stories, they write themselves.
Speaker: So it's done to report, right not to use a method, but to report the news. So do you so do you think it's about... um So you've mentioned it's an interdisciplinary work.
Speaker: in the 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. yeah um But what's interesting is that some of the people who do the graphics,
Speaker: also do the reporting. So Jonathan Coram often reads the scientific articles and so on and then turns them into visualizations by talking to the scientists and reading reading their work.
Speaker: 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, not about the I've done now with that for 30 years. i still i'm still I'm still shocked that people think that's an amazing insight. I mean, I always but i always use Ben Schneiderman's quote, which says, visualizations about insight, not about pictures.
Speaker: It all rests on that on the data. yeah Yeah. Well, it's about it's about explaining content, explaining things. Yes, exactly. So where are we headed? So we've got all the new tools.
Speaker: Maybe people are starting to build these teams like the Times and and other agencies. where Where do you think we're headed in in the field of, not going to call data visualization, I'll say communicating data.
Speaker: or Or reasoning about data. Or reasoning about data, right. Right, that's thinking. Yeah, or thinking about data. Yeah, thinking about data. My proposal, which I teach in my class and which is at my website,
Speaker: It's called maps moving in time. So it's 4K or 6K video. So no more sticks and ticks, 10, 20, 30. Sticks and ticks and still land.
Speaker: But we should have the information throughput of video. I never did there look at films for many years from a data point of view. because they were so much information through the book. It was scary. yeah And they knew what they were doing. And they they had an infinite amount of money. yeah and so on It was frightening.
Speaker: 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.
Speaker: And that slow panning leads to gentle 3D. Most 3D stuff It gives you a headache, or you have to wear funny glasses, or sticks you in the eyes too aggressively. But the panning of the Swiss mountain maps, you can see a ski lift between two mountains and the mountain peaks. It's just incredible.
Speaker: And so this has the information throughput and design power of a classic, probably one of the best maps ever done. Their contour lines, and it's just amazing, incredible typography.
Speaker: It's the Swiss Alps, great content too. and But combining that with essentially infinite scroll and probably not letting the user interact too much because they'll get impatient.
Speaker: It's a slow pace. And so they're getting this tremendous information throughput on a 4K or 6K screen. mean We're talking 6,000 4,000. Just amazing things.
Speaker: And they're getting that with an intense design, oh a great map, a contour map. and with video throughput. And so you're cutting edge of resolution, of video, and at the classic cutting edge of the layout. yeah And so I've been doing some things, and there are demos on my website, of maps moving in time.
Speaker: And so it's a complete shift. The problem is that the production is requires tremendous amounts of data, and tremendous, and well, not computational power is almost trivial now, yeah but it requires lots of data, and it's not easy editing video.
Speaker: it's it's you open It's probably the square of hard you know compared to still hand, something like that. Or maybe you're it's it's, yeah, it's you're going for things, the area squared, and but movies are just kind of almost volume, so it's almost by the cube, you know, sort of slightly. right And so, see, again, it's relying it's it's saying that the gain is in the hardware.
Speaker: Video, well, software, bit but hardware, and then in the screens. yeah And so, you know, I don't know if people are ever going to do their own little home data visualization. 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.
Speaker: And that's really powerful. So you have the patience and care with the general scan, but the rush of information with the video. So it doesn't have the impatience of television, which is, know, where they you know jump and stuff. It has that kind of concentrated analytical eye yeah with it with the slow panning.
Speaker: 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, right the relevant points.
Speaker: yeah And so it's aimed, like all information displays, to support human cognitive skills. To me, the fundamental principle of design is that to so let the user perform fundamental the fundamental analytical tasks that people engage in when they reason about data.
Speaker: So understand causality, make comparisons, and think in a multivariate way. And that's the point. yeah And that leads to designs.
Speaker: And do you think that visualizations can help people better understand the data, sort of going backwards a little bit from, I'm going to present something to the to the user and still help them understand sort of the whole network of how that visualization was created.
Speaker: That's either teaching statistics through visualizations or teaching sort of complicated topics or big numbers or whatever it is through visualization. can we Do you think people can can ultimately understand what's under the hood?
Speaker: Well, it's fairly high-powered stuff, and I assume that...
Speaker: just as as places like Apple and Twitter have made many, consumerized yeah many things, yeah that they will do it. right 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...
Speaker: They're going to be much better at these things than we are. no yeah yeah 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 our.
Speaker: That's scary. You know, she knows LaTeX. She knows R. She knows, you know, oh, wow. Right. Okay. So I think, you know, as you think is as this, as students, young people who are, you know, who are quick learners and so on, and you've got to learn these things and especially like things like coding or things like that. Right. You know, before age 25, it's all over. Yeah.
Speaker: I've tried to learn R several times. i can't even hardly get past data entry. You know, I i kind of get somehow a loop. Just kidding. Spreadsheet. Right.
Speaker: So, of course, it'll it'll somehow be, you know, not commodified. That's a terrible one. And I hope it won't be like R, that will be accessible. It won't be, ah you know, it'll have a general thing, and there won't be an interest in and kind of So something that something that's more ah universal for even the lay reader the lay user could sort of go in and... Right. So if they can... Okay, so that they can make a reasonably bluscious statistical graphic.
Speaker: Some kind of fairly detailed map and put their data on it. And then, okay, they pan over it. So that's... Now giving, essentially, it's giving interaction, but it's not letting the user completely set the pace.
Speaker: I think that's very important, that it's hard enough to think and and also to do interaction. Often that's a kind of separation.
Speaker: The way I use most of the my tools from 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.
Speaker: I say to my design assistant, because I find it hard to do a serious program 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, know, that now it may be that some editing could be, I don't know what could be done,
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 that.
Speaker: yeah And there's no real so no big solution. you know It's like film editing. yeah you know The distance between real film, Avid, or Final Cut, and the difference between what we can do is a lot narrower now on our devices, but it's not.
Speaker: It's not simple. Yeah, it's not simple, and it's not real film. Right. and It's inherently complicated and hard. Yes. and And so clever interface won't help much. Yeah. yeah I think what it means is if you want to do that stuff, get a good get a bright summer, geeky summer intern.
Speaker: A young person who knows how to do the code. Exactly. Right, right. um So you spend a lot of time, i would guess, looking at what people are creating and the conversations that go on. when you When you see what people are talking about, when either the the when the creators are are talking about what they're what they are making or people who are critiquing, do you have a sense of of how the community could do a better job of that?
Speaker: and Maybe they're not, maybe they're doing a great job, maybe maybe the conversation is great, but do you do you think there could there's there's a community there where ah perhaps there's changes to be made and ways in which the whole field can sort of move forward?
Speaker: 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 So the science article that took apart Google Flu is an amazing piece done by people at Harvard and MIT.
Speaker: And it makes the excellent and obvious point that of the 20 threats to validity...
Speaker: little data, small data, is only one. right And so Google made errors, naive econometric errors. You know, model searching, and and they had a very brittle model, which of course crumbled upon retail. Classic. Yeah.
Speaker: And they're working they're working with time series and stuff, and they're the the econometrics was naive. yeah And the one thing I'd like to... or one side effect of the science article about Google Flu was it did use the the word hubris quite a bit. But
Speaker: but that's what I mean by a really great critique. I don't think... I think much better than critique is... style my books, which is very, only 5% of the war against stupidity, which is rather the best critique is to make something wonderful, to celebrate excellence and say, this is our standard.
Speaker: So one 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 i It looks like any kind of visualization at all.
Speaker: You put it next on your screen to Google Maps.
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 can How come I can't use light colors so I have five different layers?
Speaker: How come I have 40% computer administrative debris my things and they have virtually none. How come I have to put, because of corporate guidelines, five logos of my thing? They have one little tiny thing, play in the big leagues.
Speaker: And so that's what I mean by this kind of 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.
Speaker: And then in response, you say Google Maps is the most widely used image in human history, and you can bet it's fresh every couple of days. It's in every language, every country in the world.
Speaker: 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.
Speaker: mean, occasionally it's nice to take something apart, especially if they are... mean, Silver does some nice critiques. And, you know, you're usually taking apart the forces of evil.
Speaker: You should remember when you do that that your allies... cheat or do bad things just as much as your opponents. That your opponents are not uniquely inept statistically or cheating.
Speaker: It's just as true of your allies. and so But a lot of critiques are motivated by disagreement of with the substance. right And it now becomes a statistical critique.
Speaker: But you've got remember, give me a critique of something you like philosophically. right There is a role. It's very active, of course, in peer review.
Speaker: That happens. And it's also active is that the junk is never footnoted again. you know That's a real sorting of quality.
Speaker: And i I don't want to look. I've got... you know I probably have 10 or 15 years left. i don't want to spend my time looking at mediocre stuff. yeah i There's so much excellence left, and the chances are that looking at five pages of Galileo will be much better than looking at you know a week of Twitter, or a month of Twitter, or a year of Twitter.
Speaker: I enjoy Twitter, yeah and it's great fun, but and it diversifies my point of view and I get to see all this stuff. Yeah. yeah 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.
Speaker: The challenge is to do forever knowledge and plus going back in history. Why should this day now be any better than some day five years ago or a day back in Galilee day.
Speaker: And so there's such a recency bias to our thinking and to the web and to obviously Facebook and Twitter. It's enormous. And everything is stacked by the most recent on top.
Speaker: So you don't... I have my website. The threads are organized 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.
Speaker: It took me until I was in my early 30s to realize that I and did not have to read quarterly journal of such and such because it would turn out there was only one article out of 25 that would ever be cited again. Mm-hmm.
Speaker: And even the best article might be cited only a few times, and they wouldn't be even be cited by the author's mother. you know So it's to get out of that daily flow that many of us have to live in.
Speaker: And I've been really lucky to escape that and to be able to have this long, you know, 50-time horizon. there's And there's so many wonderful things back then or back, you know,
Speaker: last week right uh... and they're often more wonderful than today and that was a great help i learned that is to think look very long far ahead don't do proper nouns do verbs do principles do forever things like so real scientists do that with nature's laws do that and then the world of the past really smart people have been doing visualization and explaining And just to spend a day in Galileo's collective papers and just look through the illustrations.
Speaker: They're like 26 volumes. i just do 10 pages and I have another idea. Well, I think on that note, looking into the future can inspire us. I'd like to thank you so much for coming on the show. Okay, terrific. Thank you. right Thanks for tuning in, in everybody. i hope you enjoyed that episode. I hope you'll check out a lot of the other podcasts that I have posted on the site.
Speaker: I have interviewed lots of different people over the years, um lots of people whose work are sort of foundational to the work that we do, including ah Leland Wilkinson, for example, who I interviewed just a couple of years ago before his passing.
Speaker: I'll post links to those episodes in the show notes. I hope you will check them out I hope you will rate or review the show wherever you get your podcasts be them iTunes be them Spotify or be them directly on the Zencaster app where I record and published So until next time this has been the policy of his podcast. Thanks so much for listening
