Welcome back to a whole new season of the PolicyViz Podcast! I’m excited to bring you a whole new exciting slate of guests this year covering a huge array of data visualization and data communication strategies, technologies, and techniques.
Maureen Stone (Tableau Research) has been involved with Tableau since 2004, when she was asked to design the initial data colors for Tableau 1.5. She joined the company in late 2011 and became a founding member of the Research Team in 2012. As a member of Tableau Research, she continued her work on optimizing the use of color in visualization. She served as research director (2017-2021), and has recently retired (June, 2022). While best known for her expertise in digital color, she has a broad experience in information visualization, interactive graphics and user interface design. She is a member of the IEEE VGTC Visualization Academy and the author of A Field Guide to Digital Color.
Episode Notes
A Field Guide to Digital Color
Maureen’s website
Paper: Rainbow Colormaps Are Not All Bad
People
Christian Chabot
Pat Hanrahan
Jeff Heer
Jock Mackinlay
Chris Stolte
Vidya Setlur
Danielle Albers Szafir
Colin Ware
Papers
A Linguistic Approach to Categorical Color Assignment for Data Visualization by Vidya Setlur and Maureen Stone
Rainbow Colormaps Are Not All Bad by Colin Ware, Maureen Stone, Danielle Albers Szafir, and Theresa-Marie Rhyne
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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. Well, welcome to a new season of the show, I hope you had a delightful summer and a restful summer, and a safe and healthy summer. I spent a lot of time just relaxing, working from home, going into the office a little bit more. Very nice summer here in Virginia. Unfortunately, while a lot of the country was under terrible heat extremes, terrible weather, it was actually quite nice here in Virginia, so I don’t know what to tell you about that. But I am ready to come back for another season of the podcast, very excited for the guest I have for you today, and going on into the fall, and, of course, into next spring. Lots of stuff going on here at PolicyViz, I’ve been running a lot of new blog posts, thinking about a lot of different types of things in the area of data visualization, including qualitative data visualization, how we should convince our managers and our bosses to think about data visualization.
I also just finished a new virtual data visualization course, so if you or your organization never thought about trying to learn more about data visualization and didn’t want to bring in a trainer for a full day thing, I just partnered up with Skillwave to offer a new course, the art and science of data visualization, I’ll put a link to that in the show notes. So that might be something that you would be interested in learning more about; if you listen to this show, you probably would like to learn more about data visualization, so I hope that will be a class that will be useful to you. I’m also thinking about doing lots more stuff with Excel. As you probably know, I published a new book Data Visualization in Excel, last May – May of 2023, and that book has been doing great. And so, I’m trying to do more video tutorials on YouTube and on my Instagram feed so that you can learn more about how to do better with data visualization in Excel. Lots of stuff coming up, lots of changes in the field, particularly on social media as Twitter, now X, has sort of changed the feeling of the field, but I’m still out there, trying to do my best to convince people to think harder about communicating their data through better charts, graphs, diagrams, and more.
All right, having said all that, let’s turn to the first episode of the 10th season of the PolicyViz podcast. I am so delighted to chat with Maureen Stone, formerly of Tableau, now retired about color. As you surely know, color is one of the more important aspects of our data visualizations, it can help encode values, it can help direct people’s attention, and there’s lots of ways that people think and argue about colors, whether they’re good, whether they’re bad, whether we like them, whether we don’t, it’s objective, it’s subjective, it’s semantic, it’s so difficult. And Maureen is one of the leaders in the field specifically, and most importantly for us on how to use color in our data visualizations. So Maureen was so nice to chat with me last spring about a style guide question I had when it came to data visualizations and color, and so, we reconnected in August, just a month ago, to chat about her work and her experience and some of the challenges that she has seen and tried to resolve when it comes to color; and I think for those of you who are using Tableau, how the early days of choosing and developing color palettes in Tableau worked.
So Maureen was gracious enough to come on the show, and so, now you get to listen to my conversation with Maureen Stone on the first episode of the PolicyViz podcast for season number 10.
Jon Schwabish: Hey Maureen, welcome to the show. Great to see you.
Maureen Stone: Thank you. It’s good to see you too.
JS: How are things, how’s the end of your summer going?
MS: Oh it’s been surprisingly busy between doing some music stuff and doing some – went down to the SIGGRAPH 50th Conference, the ACM SIGGRAPH Graphics Conference, and then, getting ready to talk to you.
JS: So I think like my kids started school yesterday, so it kind of feels like my life kind of revolves around the school year, but I’m guessing, now that you’re retired, you don’t really have that kind of pace anymore.
MS: I don’t have that kind of pace around school, haven’t for years, my kids are well out of the house. But by being an amateur community orchestra player, it seems like – and my husband is too – it seems like the structure in our life is coming from the rehearsal schedules. But I’m also trying to get some more things in structured that are around tech and color and the likes, so your podcast is a very welcome deadline.
JS: All right. Great. So I think most people, I mean, certainly everyone who uses Tableau, but most people in the data visualization field know you from your work on color, your book, A Field Guide to Digital Color. And so, I wanted to get into that, but I think it’d be really interesting for people to hear a little bit about your background and how you ended up at Tableau, like, at the beginning, working on the colors, and we can talk about that in a little bit, but maybe you could just tell folks a little bit about your background and how you ended up there.
MS: Yeah, thanks. So I’ve spent the first 20 years of my career at Xerox Palo Alto Research Center, Xerox PARC, and I was there in the late 70s – starting in the late 70s, doing illustration and design systems under John Warnock and Chuck Geschke who are the Adobe founders; and we were really early into the whole world of graphic design and imaging going digital. And so, there was this big transition from stuff that had been crafted by experts done with film to it’s all just bits now. Being part of the computer graphics community, of course, there was this revolution how people were creating things – you didn’t have to paint it, you didn’t have to go point a camera at it, you just mushed bits around. And they were creating these wonderful images, you know, kind of things that we now see in movies, we’re all digital photographers now. And so, in that context, I was working specifically on illustration and design tools, but was very interested in color, and how it affected, you know, how you created it, and then, how you got from the display out to the print. And that task, which is actually really hard, so anybody who’s been around long enough, I mean, it’s still a problem, you make a beautiful thing on your screen, you press print, you go, oh my gosh, what is it. Back then it was really hard, and what came out of the printer was really bad.
So that got me pulled into the imagery production world, and to solve that problem, we had to get away from thinking about colors just RGB values or printer CMYK values, and think about what they meant perceptually. So we had to go in and out of a perceptual color space, and understand how people thought about color and could see color. So that’s what really got me kicked off into the color world, and that was the mid-80s, and did that work with some folks from the University of Waterloo. So from there, I kind of said, wow, I like this color stuff, and got engaged in the people who do practical color of various forms, started bringing ideas to the computer graphics community. So I decided after 20 years, I wanted more flexible life, and I started a one-person consulting firm called StoneSoup Consulting, and that was in 1999. And as part of that, I did a couple of different interesting things; one is to write the book, the Field Guide to Digital Color, because I learned all this stuff, and I wanted to write it down. And the other was get engaged with Pat Hanrahan, and the Computer Graphics Lab over at Stanford University. So we were living in the Bay area at the time. And I’d met Pat back in the SIGGRAPH world, and Pat was getting, if you think about it in the early 2000s, he’s getting really interested in DataViz – Stolte is there, working on his thesis.
And so, Pat and I got together and he says, you know, I want to think about how best to use color and visualization, let’s work out some principles, I’m giving this course. And together, we came up with some very key ideas about using color for function, and how you would use it in Vis, how to take this massive world of color and kind of precis out what we really needed. So cool, cool, and suddenly we move to Seattle, because of my husband’s job, I’m still contracting, and a little later that year I get a call from Stolte and Jock Mackinlay and Christian Chabot, and they say, you know, we’ve got this thing called Tableau, which I actually had known about, and I’ve known Jock forever as well, can you come help us make the color really, really excellent. And I thought, sure, that sounds great, that sounds like fun, color for bar charts, how hard can it be.
JS: Right.
MS: And that just turned into my second 20-year career.
JS: So, like, right at the beginning, they were thinking about color in kind of an innovative different way, like, we’re not just going to pick something random, we want to think very hard about it.
MS: They wanted the appearance of their product, the Vises you produced to be just excellent, both Pat and all of them – Jock, Pat, Chris, all cared tremendously about the presentation; they knew that good design was essential for people understanding them. And this is back in the world when people were using Excel and great backgrounds and black gridlines and screaming bright colors and everything created around data looked terrible, unless you were doing very Bespoke sort of visualization. So that is really fundamental – Tableau cares about color, but they also care about all aspects of the visual presentation of the Vis.
JS: So you get into Tableau – I want to be able to make this clear for people, because I’m guessing there are some people out in the DataViz world or who are sort of on the periphery, they make graphs, and they say, like you said, how hard can it be. So you make a couple of color palettes, like, what do you do with the rest of your time. And so, that’s, like, you were, like, director of the research – is the research department – I don’t know what they call it at Tableau.
MS: We just call the Tableau Research, but all of this predates that by a long time. This is 2004. And I worked as a contractor, getting a contract a year, up until 2011, when I actually joined Tableau as a UX designer, because that was the job Jock had, and Tableau Research came shortly after that. So this was applied practice from what I already knew, and sort of drove a bunch of research that I did, even while I was still a consultant.
JS: Right. So tell me a little bit about those early days of developing those palettes, because now there’s a lot that are built into the tool, but the very beginning…
MS: Oh, that’s [inaudible 00:12:39].
JS: Yeah.
MS: So the first thing we had to figure out was, how are people to think about color, right? If you do a Bespoke sort of Vis, you kind of create the Vis and then you color it. In Tableau, what you’re doing is you’re moving your data, you’re dragging and dropping, and it makes Vis after Vis after Vis. And so, we wanted color to be associated with the data and its function, not a, you know, oh, we’ve created an illustration, you now paint. And, in fact, we really didn’t want to give people the freedom to just arbitrarily paint their views. We wanted them to, by default, create excellent color. So that meant that it functioned well, categorical colors distinguish different categories, quantitative colors try to give you a sense of the order and magnitude of numbers, and we wanted the Vis to be front and forward and all the other stuff quiet behind it, the gridlines, the background, anything we called formatting. And we wanted it to be right for every view, so that makes the problem really, really hard, because when I assign the color to the data, to the categories themselves, not to the marks, then you start seeing a lot of interesting challenges about, well, that color looks good small, but it looks terrible large; I’ve got a lot of categories here, how do I remember what color goes with which, I can always produce a legend, but we discovered people kind of liked them to be semantic. Of course, there’s issues of legibility and accessibility…
JS: I was just saying, and there’s the difference between categorical and quantitative and sequential and all these different pieces that like, I remember those early days of Excel with that dark gray background and the bright green, and the Tableau colors are just much more, relative to those, are much more subtle – but relative to those everything was more subtle, I guess.
MS: Yes.
JS: So was there a goal of saying we’re going to have this many colors and this many separate palettes, or was it more constrained than that, less constrained than that?
MS: The original goal was to have something that worked by default.
JS: Yeah.
MS: And so, that was an open question. Well, just as was it palettes plus application today to the right UX, and once we decided on that there are a lot of details to work out, was, well, how many colors are in the default Tableau palette. People like to see a lot of them, but if there are too many of them, you have chaos, and you can’t distinguish them. So I did actually a bunch of very applied color research pushing colors around in a perceptual color space, using a combination of Adobe Photoshop, there’s that CIELAB space, and my own custom tools to kind of push and measure. So I mean, there are metrics you can use, they aren’t perfect, but they’re better than just having to eyeball everything, and thinking about all these issues, and we came up with 10, we came up with the Tableau 10. And kind of an interesting thing about the Tableau 10 is that it lines up pretty well, you can name those colors. We knew that there’s a red one and a green one and a purple one and a blue one, and this turns out to be really important for how people think about color, and certainly talk about color, if I’m pointing to the display, and I say, you know, that red bar there, as opposed to this reddish-orange-mauvish-purplish thing.
JS: Yeah.
MS: So think of your Crayola colors that you got, there’s kind of eight of them, it turns out, there’s been research that says there’s kind of 11 basic color names that we English speakers all use and recognize. And so, the Tableau colors are white and black, they’re not those colors, so we had kind of a blue-greeny thing that doesn’t really have a simple name but to make a full 10. And as we talk later about research, that sort of research around the semantics of the color, how you hold the color in your head, turns out to be really interesting and important to make it easy to use. In terms of how screaming bright are they, we knew that good design principles did not make things that were screaming bright. If you also – back then even people were still printing those screaming bright colors, don’t print either. Yeah, so we wanted something that really looked sophisticated, but legible. Every time I’d make palettes, I’d get feedback from Chris who’s got a very good eye and from Jock, and I’d get, you know, Christian Chabot would have opinions about the UX and the general, you know, did he think he could sell it, did the customers love it, this is really tight iterative sort of thing.
JS: Yeah. And then, when the tool started to kind of take off, and there were more and more users, did you start to – did you receive feedback from customers, and were you able to, like, was any of it useful that you could implement?
MS: Well, certainly, the first thing we got was more colors. We kept doing that but – and so the Tableau 20, but beyond the default as well. So the Tableau 20 is an interesting story, because that was kind of a challenge from Chris, how far can you push it, you know, maybe we need more. And at some point the colors, mentally they’re not distinguishable, you can’t kind of remember what they mean. But if I’m taking a big hairball of a scatterplot, and I want to pull apart all of those different categories, even if they’re not quickly and uniquely identifiable, I can at least see that they’re different. So having a goodly number that you could pop on there that worked seemed important. So I pushed and I played, and I really did push a lot of colors around, and I stared at them, and we had these great feedback loops going, I could just send RGB values to the Devs, and they would put it in the product, because you can’t really tell until you try it in the product if it’s really going to work. And then, I came upon the scheme of doing them in pairs, light-dark pairs. And that worked, the light colors, you know, they’re not as great visually, but in there, in that mix, and particularly, since you can kind of identify them well, there’s dark blue ones, this is probably the light blue one, that seemed to really help. And so, that’s how we got up to 20.
And again, there’s this, you know, what is your brain trying to do with these, not just your eyes’ aspect of it. But they wanted colors for, oh they needed some sort of stoplight colors, because business people need those. So I carefully designed them, so, in fact, they vary enough in lightness, they are distinguishable by people with red-green colorblindness. That was great, but they’re different, they wanted just different aesthetics – if you’re going to do a dashboard, and you had multiple views, you didn’t want to use the Tableau 10 over and over again, because the red one here might be different, mean different data, so we put these disjoint palettes in. So here’s brown and yellow and gold and green; and over here, we’ve got purple and blue and gray. And then for the quantitative colors, those color gradients, people just had a lot of needs based on their conventions. There was kind of one color gradient for every color in the Tableau palette, but there was always the arguments about finance people want red, green, and I’m going, yeah, but you can’t see that when it’s qualitative like, you know, so we really pushed actually an early decision people might appreciate is to make blue-orange, the first defaults, both in the category, and eventually in the quantitative, they really wanted red-green to start.
JS: Yeah.
MS: So that’s the kind of feedback, it was customers saying, I am using this, I have real functions, I have real standards, and Chris would just come back and say, can you make more palettes both for marks, but also for things like the little bars and formatting and shading. And everywhere, there’s a pixel, it’s got to be some sort of color.
JS: Right. So I want to get to how then you were doing research, but then sort of later when it’s sort of more formal at Tableau, but I’m curious in this piece that you mentioned, where you would develop a palette or a color, and you’d send it to the developers and they would put it into the tool, so that when – because you had mentioned earlier about how we can perceive color differently if it’s like a little mark versus a big thing on a big screen, and even when it’s printed out. So what did those experiments look like for you, was that you bringing in data in Tableau, and just trying as many things as you could?
MS: Well, so I call that problem color and size, and it’s a really well known color perception phenomenon. So I’m going to give people a little bit of a geek definition, color is not how you create it, it’s not the RGB value, it’s how you perceive it; and how you perceive it depends on a lot of factors, we know it depends on background; and it turns out, size is a huge factor, so if you ever think about, you know, you ever go paint a room, and you got the little paint chips, so that’s a good color, and then you spread a bunch on the wall and say…
JS: Yeah, have experienced several times, yes.
MS: Well, I didn’t know that it would apply to DataViz, but, in fact, it does.
JS: Right.
MS: And so, the real problem is technically what happens is that as the stimulus is the thing you’re looking at gets smaller, the color appears less vivid, less colorful, okay? And as you shrink them way down, pretty soon you’re just getting kind of warm, cool colors. Just two cases, and you come up and, anyway, it’s interesting. So we said, okay, what can we learn about this in a practical engineering sense. I mean, you go off to the vision scientists, and they’ll tell you all sorts of reasons why this is, but they don’t tell you how to fix it. Right?
JS: Right. Easy to identify the problem.
MS: Exactly. So we had a research intern named Danielle Szafir who’s now a professor at UNC, and she worked with me and Vidya, and we actually set up a bunch of experiments to try to model how size affected your perception of color. And so, we put people through these horrible experiments where they’re staring at a screen and there’s a big ball and a disc in the middle, and two little ones on the side, and we go, which ones on the side look the same as the middle one. And just a lot of collecting data from people and Vidya’s feeding them cupcakes to get them to come back and do more of these things. And from that, we did a bunch of data fitting and modeling and came up with some approximate linear models that gave us some sense of what would do as if you could put it in the product to adapt the RGB value to the size. Right?
So everybody says, oh what color is it, and they mean, the RGB value. Right. But we’re saying what color is it, we mean, what does it look like. And so, we actually have a – to me, the most fascinating example of where people see this was in like the background washes that you want to use across lines or just to make the background a different color. Those need to be really, really light. I mean, if you’re working in a dark and light world, you want things that are just off the white. So what does a typical formatting color picker look like? Oh, it’s a little array of rectangles, just tiny little squares. And if you make those colors light enough to be aesthetics and you make them little squares, you can’t tell them apart. You just simply can’t. So we had to do various tricks. Now, asking the Tableau engineers to put in my evolving model of color and size, and they did not want to hear about that, but they were willing to do some little engineering hacks that we worked out for the early versions of Tableau – we first started by having the colors in little triples. So if you look at the Tableau color picker, there’s darker ones, and then lighter and lighter ones underneath in little steps of three. And that’s because those little light ones is probably what you really want on the background, but you can’t tell what color it is, but you can tell what color the ones above it are, and which are also useful for other things. Right? So we’re kind of labeling them.
The other thing that we did when we redid the colors, 2014, I think, we just flat out lied in the color picker. We put colors there, but it’s not lying. Right? We put the right color there. There’s just a different RGB value. So we actually adapted what the user saw, so that when it was applied, they got what they wanted. And so that, you know, for this whole – there’s a lot of color issues, but the color and size in particular, I want to make a point people, it’s not about the RGB value, and that kind of thing is perfectly okay to think about what RGB triple do you need here, or what RGB triple do you need there? It isn’t not that, it’s what it looks like.
JS: Yeah. Were the engineers, were they generally willing to make those changes? I’m guessing you had lots of conversations about the actual user experience of how a person goes in and makes that selection, like, what were those conversations like?
MS: Well, in the early days, we were kind of doing all of everything, the company was extremely small. By the time I actually got there, so this work is, you know, after I’m a Tableau employee, and I’m after in Tableau Research, we had a really good UX design team and a bunch of engineers that were competent UX implementers, shall I say, and they were very open to working with me and other researchers, if we could make it easy enough. And so, the beautiful thing about a lot of color work is that it is, and I don’t have to transfer complex code to them, I just have to give them some RGB triples. Now, if I just give them a spreadsheet that’s a big pain, because they have to transcribe them. So I would write code that would actually write the C Plus Plus tables they needed, and let them drop it in. And if you do that, then they’re like, okay, I’ll just drop the new table in, it’s easy-peasy. And so, I also worked a bunch with the Tableau formatting team, which we were working mostly on format, but it kind of affects marks a little bit. But as we worked on the new design for the formatting system, I just found it a great experience to work closely with the visual designers and the UX designers and the engineers to say, how can we, you know, what is practical, what will really work here, but it’s good practice, but it also gives this really interesting and excellent – it’s doable, it’s practical.
JS: Right. But it’s also interesting the way you describe that because it is, even within this team, this broader team, you are talking to the engineers and trying to talk in their language to make their lives easier, which is like a lot of what data visualization is about is kind of talk someone’s language so that they can get something out of the graph, and you’re doing that in the process – a very meta, I guess, to build a data visualization tool where the whole point is to be able to communicate to people who may not actually speak that data language, just kind of interesting.
MS: Well, I mean, I started my life as a software engineer, so, you know.
JS: Right. So you kind of knew that language. Okay, so now we’re like 2014-2016, Tableau Research has kind of started up as its own, kind of, sounds like a formal group, and you’ve talked about the work you did with Danielle and with Vidya and some others. The other thing that you mentioned earlier that I know you worked on is on color semantics, and you’ve mentioned a little bit, but I was thinking maybe you’d spent a little bit more time talking about that work.
MS: Yeah, happy to do it, just to be specific, Tableau Research started in 2012, and I worked as a researcher throughout my career there, but towards the end of my career, I was more of a manager. So a lot of the research really kind of got done in the 2012 to 2017 timeframe. And you can put a link on your podcast to Tableau Research website so they can see it.
JS: Absolutely.
MS: So, Vidya comes to tableau with a deep background in natural language and semantics, and she was carrying a vision for Tableau to engage semantics more in the way it helped people think about visualization and the tools for that. And so, we had observed an interesting phenomenon, and this is actually something I did some research on, about color naming, with Jeffrey Heer who was then at Stanford, but now at the University of Washington, and his students. If you have a Vis of say, I don’t know, fruits, let’s pick something simple, and you apply the Tableau 10, and the colors go wherever, you may end up with purple bananas and orange cherries and the like; and you can kind of instantly say, this is wrong, this would be so much better if the colors of the Vis match the colors of the object, or, more generally, some color that I strongly associate with that data value, so things like brands, sports teams. And if it doesn’t, it kind of makes your head hurt; and if it does, Jeff Heer and his students even did some research to prove you will, in fact, remember better and be a bit faster at the task of selecting things and finding things. This all kind of makes common sense, but being in research, we have to kind of prove it all.
So Vidya had noticed this, we had a whole bunch of examples from Tableau Public, where people had gone through extraordinary measures to be sure that the semantics all lined up, and she said, can I do this automatically. And so, there’s a paper that we wrote together, where she used her semantic language and scraped the web for Google tools to come up with automatic assignments based on the semantics of the categories, and we blended that – now, you can say, well, I can do that, but there’ll be terrible colors. But we then used that to snap those two color palettes that I had already designed, so we knew they were decent colors. And we’re really excited about that, and that would have been a great feature to add, but it has its tendrils out into the web, and also by that time the company is not self-contained, and involves potentially using tools and IP that we don’t…
JS: Right, it just gets bigger and bigger and bigger, right.
MS: Yeah.
JS: So you have that paper, so now you’re a manager, but what’s interesting, so you retired, when, in 2021?
MS: It was just a bit over a year ago, so it’s June 2022. Is that right…
JS: But now you have a new paper with Danielle and with Colin Ware on the rainbow color palette. I want to give you a few minutes to talk about it, but to me, it’s fascinating, because in my relatively limited time in the data visualization field, the first big fight you run into when you get into this field and you hear about Tufte and Few is she never ever used pie charts. And there’s still that fight, and then, there’s still less of a fight, but bar charts just start at zero, but it’s like the two big ones that are like, or maybe three big ones, never, never, never, like, no pie charts, no 3D, and no rainbow color palette. And you have a new paper out, I’m going to bring it out, because I want to make sure I get the title exactly right, because it’s right here. So rainbow color maps are not all bad. So this is like, I don’t know – I don’t know how many people get excited about this. For me, this is really exciting. Right? This is like shaking one of those like fundamental beliefs, yeah, right, shaking the fundamental beliefs that people have, but maybe not based in certainly thorough research. So I thought we would talk about that before we wrap up, because it’s really kind of exciting, exciting stuff, I think.
MS: Thanks. Yeah, Colin Ware was the instigator of that paper, and he reached out to me and Danielle, and if you – so Colin comes more from the scientific visualization community than the InfoViz community. So there, the whole world of – we’ve talked in Tableau mostly about categories, but we too also map quantity to color. It’s just we don’t do it over images. And he had been asked to review yet one more paper, or no, actually found it published one more paper saying the same old things about why the rainbow is bad. And we went back in the first paper that said the rainbow was terrible was in the mid-80s. And here it is, the 2020s, and you have to ask yourself a couple of things – why do people still use them? Is it because people are idiots or is it because there’s something we don’t know? And furthermore, why do we keep publishing the same old rants over and over again, because nobody’s adding any new information about it? So it kind of breaks down into two kinds of domains – what is a rainbow is the first one.
The original rainbows, where, if you think of like the HSV color space, and you just kind of go around that circle, so what you’re doing is you’re going around to the corners of the RGB color cube, and taking the brightest and most vivid colors, and when that was invented back in the mid-70s, that was really exciting, because computers were really slow, and that was really cheap, okay? And it was better than asking people to think in RGB, fine. But when people started doing digital imaging for visualization of this PsyViz stuff, they grabbed that, and they plopped it on their images, and they created visual chaos. But we know why that particular rainbow creates visual chaos, and one of the reasons is because the colors and lightness are jumping up and down and up and down, whereas if you think about an image of a 3D surface, you would want to look at in grayscale and see the shading just exactly like it would be in nature with the black and white photography the way your eyes would work; and your color map needs to do something similar, otherwise, you start losing shape from shading. But a lot of his tasks aren’t shape from shading dependent, and there are a lot of rainbows that can be made. I mean, a rainbow is just a multi-color color map, right? We use them all the time and people like them, because the classic, I’ll just start with one color and make it lighter, you can’t see really very many levels, especially if the data is scrambled. And so, people add some color to it, and now you can – the fact that your brain says, oh this is the red part, we are back to semantics again, this is the blue part, this the green part. X is kind of a ruler for where you are, and makes it really easy to create categories, regions of similar data values.
Now, the other problem is, if those are arbitrary categories, they don’t have anything to do with the data semantics, then they give you a false idea of what’s important. But if they do line up with the data semantics, in fact, it’s extremely powerful. So that is, you know, this is about the task and why people do it; and so, our messages were twofold, if you’re going to do rainbows, use good rainbows, we know enough, you can stop beating up the HSV Rainbow. I don’t want to see another research paper…
JS: Right, using the HSV one.
MS: In comparison to well-designed multicolor ones, and say, oh, this one is terrible for all these reasons. And, you know, that’s in the literature. But then again, now we’re back to also why do people want multiple colors. Right? I think we’re hearing something about how it is easier to see if you do get these categories. And even for some tasks, I’m less familiar with than Danielle and Colin, but especially Danielle who’s going even the battle rainbow doesn’t work too badly, once you get used to it, because, in fact, what you do want is to color these things that are the same values in different parts of the images, bright, recognizable colors. And that’s a very different, you know, so we just don’t know enough about what people want, and what they’re trying to do with the color in these quantitative situations.
JS: But it’s so interesting, because that also ties back to what you said earlier about semantics and a yellow banana versus a purple banana, and also your conversations with the engineers, because I remember talking to, I think, like family, and talking about the rainbow color map in the hurricane, the weather map saying it’s not, you know, you should use something different. Like, why? Red means it’s hot. Blue means it’s cold. So people understand it, at sort of a core level, but then we get into all these arguments; and the three of you have sort of demonstrated which, to me, I don’t want to say was mind blowing, I’m not going to go that far, but the fact that, yeah, the rainbow palette doesn’t have to be kind of the one that you just see online somewhere, you can vary those segments, and that I think is just really interesting.
MS: You touch on a really other important factor. Those weather maps, people are really familiar with this.
JS: Right.
MS: And in some sense, they’re not broken. Right? What you’re doing is you’re saying, you know, this is hot, this is cold, you can set them up so that, yeah, the color name shifts on the 10s boundaries, right, 60, 70, 80, 90. That would be really easy to read. And so, why yell based on some principle, you have to think about what people are doing. Sadly, with the color maps as they’re keeping having to extend them up in terms of temperature, but…
JS: Right. So I want to close up by your thoughts on, because you mentioned several times already that there’s more to know, and I’m curious where you think the big areas are that people can and should and are exploring in the research world.
MS: Well, I would say both research and product, so one is simply to stop focusing on single Vises, and start thinking about dashboards and more complex displays. I’ve seen some early research on people trying to normalize the colors across different, you know, you patch together a bunch of views, and now you want anything that’s the same data value to have the same color, and anything that’s a different data value to have a different color. Well, computers can help you do this. And at the very least, they can tag it, and people are showing that, of course, you can set up optimization systems that will even set it. So let’s stop thinking about color as just one Viz. I’d like to encourage people based on the rainbow color map paper type work to say, we stop just saying there’s got to be simple, hardcore rules that everybody has to follow. This is people trying to look at things and see them and understand their data underneath it. This is not an easy problem. It’s not going to be a handful of rules. There’s a handful of rules that keep you from, see how to say this politely. You can get rid of the stuff that clearly is terrible. And then you can leave people with a core of things that are good enough. So stop trying to optimize for perfection, oh, is it six colors, or is it five, and say, how do we make sure you get rid of the really terrible cases, and then, let people have the flexibility to play within the okay space.
JS: Right.
MS: And some people have proposed that AI can help with some of this, that the AI can also play within this reasonable space, given the success of AI for images, I think that’s quite possible. And then, remember that not everybody sees the same. And so, instead of saying, oh, we have to have these hardcore rules, so you never make a Vis that someone with extreme color vision problems, or spatial vision problems can’t use. Can we use our computers, our tools and smarts to make things adaptable, and let people kind of give you feedback about what’s useful and what’s not.
JS: Yeah.
MS: If color can be really important, and it can be really not important, you know, you can make perfectly good visualizations in grayscale. In fact, my primary recommendation for accessibility is all the important stuff should be visible in a grayscale version of your Vis. If you can’t get it right in black and white is what designers have been saying forever. You have to distinguish the marks, double encode them. And then, but if it’s kind of – but then there are certain aspects of color that it doesn’t matter if it’s really exactly the color that I see and you see.
JS: Right.
MS: Does that come to a good conclusion?
JS: Yeah, I think so, I just think there’s a lot more to learn, and you have moved the field so far ahead, and it’s just to me interesting how all – it’s kind of like this narrative of how everything kind of links together, they’re all separate, in some ways, separate projects or separate tasks or separate tools or separate this, but they’re all sort of linked together in how people think, view, talk about color, and then, how we talk to each other both clear within your team, within Tableau Research and any other groups, but also how we talk to people who are using the visualization via the dashboard or a PDF graph or something like that. So it’s just, it’s really interesting. So just to kind of finish up, if you were back at Tableau, so they reached out, they’re desperate, they need you back Maureen. And they say, we just need you to go back into the color tool. Would there be anything that you would go in and change? Is there any kind of, like, last big modification you make, either to the user experience or to the palettes or how many there are, or anything like that, or do you feel like it’s one of the better ones out there at this point, in terms of all the, you know, there’s a lot of tools?
MS: So that’s a really complicated question. At this point, you have to say it’s not Tableau, it’s Salesforce.
JS: Sure, that’s a good point. Yeah, that’s right.
MS: And when I look at color is now often, it’s given to the user experience team and the visual designers, it’s a big company now, it has experts, and most of those experts, while they don’t use exactly the same tools and terminology I do, can get equally good results. And they have user researchers, and they have accessibility officers, they should. And so, my coming back and saying, oh, I wanted to innovate in some research sort of way is kind of irrelevant, right, for the bigger product. I would hope that they would use, since Salesforce is a very AI forward kind of company, and Vidya is still there heading Tableau Research, that we will see some of these semantic ideas get in even to the color world as well as other parts of Vis, and that would be very cool.
JS: Yeah, very cool. Well, Maureen, thanks so much for coming on the show. I appreciate it. This was really interesting.
MS: My pleasure.
JS: Great story. All right, well, enjoy the kickoff to fall and to whatever your next concert is. I appreciate you coming on the show.
MS: I appreciate being invited. Thank you very much.
And thanks, everyone, for tuning in to this week’s episode of the show. I hope you enjoyed that conversation with Maureen, and I hope you check out a lot of the links that I’ve put on the show notes page on the website. I hope you’ll also consider checking out my new asynchronous video course, the art and science of data visualization with Skillwave. I think this is a new way for me to connect with more people in the data visualization field and help people do a better job of communicating their data visually. You can also connect with me on Twitter. Yes, I’m still there. You can also connect with me on Instagram or on the website or you can subscribe to my Substack newsletter that comes out every other week with the podcast. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
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