Krisztina Szűcs is a Budapest-based Data Visualization Designer. Her background is in Graphic and Interface Design and she has been creating data visualizations since 2012. Her primary focus is data visualization but she also practices UI and UX design. She won an Information is Beautiful Award last year for PlotParade.com. 

I’ll be up front about this week’s episode of the podcast: I’m a big fan of Krisztina’s work! Her Spotlight on Profitability project is one of my all-time favorite infographics and is included in my new book (coming out in a few weeks). In this week’s episode of the show, we talk about her work, her process, and more. I hope you’ll enjoy it.

Episode Notes

Krisztina’s website

PlotParade

TinderInsights

Spotlight on Profitability

My forthcoming book, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks

Related Episodes

Episode #178: Valentina D’Efilippo

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Transcript

PolicyViz Podcast Episode #186: Interviewee Krisztina Szucs

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope you are well and healthy as we get towards the end of a very rough 2020. I’m really excited for this week’s episode, I interview one of my favorite designers in the infographic information visualization space, Krisztina Szucs jumps on the show to talk about her work, talk about her graphics, her process for making her sort of unique, dense static visualizations. We talk about one of her older projects, spotlight on profitability that I’ve actually used, I use in my classes, I’ve also featured it in my new book, Better Data Visualizations, that comes out in early January. We talk about her recent projects, a data art project that she built in D3 called Plot Parade. We talk about sort of a fun data visualization project that she did using Tinder data. So we really talk about a whole lot of things and also talk about her process, so if you are working in Adobe Illustrator, if you maybe lean more towards the art or design side of the data visualization field or process, this episode is probably really right up your alley. So I’m just going to leave that there and just get right to the interview. So here’s my discussion with Christina, I hope you will enjoy.

Jon Schwabish: Hi Kristina. How are you? Welcome to the show.

Krisztina Szucs: Hi Jon. I am fine. Thanks for having me.

JS: Of course. I’m so excited to finally be able to have you on the podcast. I have been a fan for a long time, and I think, if I’m not mistaken, we first met last fall in London at the [inaudible 00:01:56]

KS: Yes, that’s right.

JS: Yeah. So I’m very excited that I was able to get some time with you to chat about your work, and there’s a lot to talk about, a lot of great stuff. Folks don’t know your work, they soon will after our discussion. So I thought maybe we’d start with having you talk a little bit about yourself and your background and the work you do, and then we can jump right in and talk about some of your past projects that I love and some of your new projects that have just come out.

KS: Sure. Okay. So I am based in Budapest, Hungary, and I am a data visualization designer, and I am in the field since 2012. So my background is in graphic design, and I went to Art University, so I have a master’s in graphic design. I always loved data visualizations, and I especially love maps. I didn’t know at the time that they are part of data visualizations or something like that, but I always loved maps. So at the university I was attending to, they always encouraged us to try different fields. So we didn’t have [inaudible 00:03:04] specifications, we tried logo design and brand design and UI design and even a little bit of fine art. So I always wanted to try data visualizations, and the university was [inaudible 00:03:20] the opportunity for that for me. So at that time, I entered some of the data visualization competitions that were around, so I just wanted to try myself, if it works for me, if I can do it, and I also wanted to have some kind of a feedback. And I like these competitions, because they had the dataset ready, so I didn’t need to bother with that one. I find that I’m always terrible at gathering datasets, because it’s a paradox of choice. So I don’t know if that is enough, and that it’s good enough, so I love [inaudible 00:04:00] and I’ve worked with a dataset that somebody has created or collected. So I entered these competitions, and I also wanted to learn from others, because other people are working on the same dataset, and then they are sharing their work, I think that’s the best way you can learn much more about the field. So I entered, I don’t know, maybe two or three competitions, and I won two of them, and this sort of made me believe that I’m good at this, and this is a good career for me. So I also liked doing data visualizations, but I also received some good feedback from outside that people also liked my work, and maybe I should continue doing this.

JS: Right, and I want to talk about that third contest that you lost, because I have problems with why you lost that one. But I want to get back to the university. So was the university, when you said, I was explore data visualization, were there classes in areas in and around data visualization about using data or about graphic design that specific to data, or did they just say, go try your hand at creating graphic design using data?

KS: No, they didn’t really say anything to me. So it was kind of free. If I wanted to try something, and they could see the value, then they just encouraged us to do it. So we didn’t have any data visualization courses or something like that. This was something I was interested in, maybe there were books around in the university, but pretty much you could decide what you wanted to work on and what’s going to be your profession at your field.

JS: That’s interesting. So let’s get to these contests because I believe the one that you lost was the infographic you made the spotlight on profitability. Is that right?

KS: Yes, that’s right.

JS: Okay. So this is one of my favorite exploratory static graphs, it is coming out in my new book, it’s in there, so people should go see it on your website, and I’ll put a link to it. Let’s just talk about the graphic first, and then we can talk about the contest, because I think, as I recall, and it’s been a while now, that was like 2012?

KS: Yes, I think…

JS: As I recall, a lot of people in the field were upset about that particular…

KS: Yeah.

JS: So maybe you could talk, try to describe the graphic for people and also, what your thought process was, as you were creating that one.

KS: Yeah. So this was maybe the second data visualization I ever created, and it was a competition arranged by Info Beauty Awards, and the dataset was ready, and they didn’t say which message we need to convey, so I could do anything I wanted with the dataset. And the dataset was much bigger than what I ended up visualizing, and, yeah, my process was just, I wanted to find something interesting in the dataset. So I spent a lot of time on the dataset. It was a competition arranged by Info Beauty Awards, and the dataset was about Hollywood movies, their profit, their budget, the category, genres, whether they want academia or not, and something. So it was a huge dataset actually, and they didn’t tell us what was the message that we need to convey, so we could do anything we wanted with this dataset. And first I studied the dataset, so I tried to find something interesting that might be useful to visualize or that could be something meaningful to others. I found that some movies have huge budgets, and some movies of course have, compared to huge budget movies have very small budgets. For example, animated movies have a huge budget; and usually horror movies which could be shot by a handheld camera, they have a smaller budget. But compared to that, the profit could be kind of similar. So the profit could be much bigger compared to the budget, and sometimes you barely make it to a profitable movie, sometimes it’s much easier. And, of course, if you have a smaller budget, it’s even more easier to make a profitable movie. Maybe this is just a very dumb way to tell this, but yeah, this is what the visualization is about. And I was collecting this information, and I decided that I will visualize the budget and the profit, so how much the movie made worldwide, each movie. And at this time, I didn’t know how to code. So I knew something because I learned ActionScript, and I don’t know, if you remember, flash and things like that. But that wasn’t something that I could use, so I did the [inaudible 00:09:18] visualization manually, and by manually, I mean, in Adobe Illustrator. So I just created a grid between zero and 100, and I just drew the circle and the points manually, so they could represent the correct data. And this is how I drew the [inaudible 00:09:41] visualization. So it was a quite manual process, and because of this, I also couldn’t visualize everything because it could have been too much work. So the reason that I also just visualized a partial dataset because it just wasn’t possible to do everything manually.

JS: Right. But, as I recall, the graphic that won this particular contest had kind of a dashboardy sort of feel to it. It was like, here’s a movie with the biggest budget, and here’s…

KS: Yeah.

JS: Yeah. And as I recall, yours was kind of bending how we viewed, you know, it’s basically like a new graphic type, it was kind of like a slope chart, combined with an area chart kind of thing, because you had the Rotten Tomato score on one axis and the budget on the other axis.

KS: Yeah.

JS: And, as I recall, people got really – I mean, I know I was really, but I think people got really upset that the award went to something that was very fairly standard, I would say, if I recall correctly, as opposed to something that was trying to change the way we look at the data. So I guess, my question really leads into a whole other topic, which is, a lot of your work is fairly dense visuals and using new forms and using new functions – so when you are creating your visuals, are you thinking about using new forms, like, what is your thought process when you are working with data and you’re trying to visualize it?

KS: It’s funny that you are saying that I have this way of doing dense and static visualizations, because I didn’t realize this before. So I’m just seeing what is right for me or what I like, but I never really compared myself in this way to others. So it’s interesting, because I don’t really want to come up with something new, but I like to create something beautiful. So maybe this is because I have a background in graphic design, and I always want to create something that could grab the attention of the viewer. I think it’s important to make somebody interested in the data visualization, and not just because of the data is interesting, but because they find the visual look interesting, and maybe that could make them think that, oh, there is some kind of rich dataset behind that I should know of. So it’s mainly for grabbing the attention of the viewer. But the way you mentioned that I had all my visualizations are dense and static, it’s mainly because I like those kind of visualizations that have a rhythm, maybe you can call it a pattern, but I like to use the word rhythm. So maybe it’s more and more [inaudible 00:12:36] visualization when you have almost the same element but repeated in a slightly different way. So this creates this visual rhythm, and it’s visually pleasing to look at it. And I like to find this kind of freedom in my visualizations. I also found that data visualization is perfect for that. So you cannot go wrong with data visualizations, you have many data points, and basically, whatever you build, you are almost always you are beautiful if you have enough data. So maybe this is one of the reasons, and the other one is this [inaudible 00:13:23] visualization is static and that’s because I couldn’t code at that time. So this was the only option for me to do a static visualization. But nowadays, when I can code, I still do this kind of static and dense visualizations. So there are several reasons for that, I think, because I am also doing UX and UI designs, and I also design the interfaces for my visualization. So you can think about any filters or buttons or overlays or transitions, maybe [inaudible 00:13:58]. So even when I’m designing these, I don’t like to hide part of the data, because I feel like it filters you always hide part of the data that you want to show. And when it comes to interactive visualizations, it’s always up to the user to find what button to press. And I found that I cannot really trust the user that much that they would find it, so I like to put every data point on display at once. And I don’t want to rely on the user to define the right buttons to turn on the filters or some overlays. I’m just afraid that they will miss something. So it’s just an extra step for me to hide.

JS: Yeah, that’s really interesting. I think your graphic design background allows you to bring something else to the field that many others have, but is somewhat unique. It’s also, I wonder if the fact that you started by not collecting your own data and working heavy in data freed you up to not be someone like me, who came to data visualization more from the data side where I probably, using Excel, drop down menus and [inaudible 00:15:23] package stuff, where it’s the kind of standard graphic types, and so you sort of get in this box where it sounds like you didn’t really have that, you came at it more from the design side, where you don’t get in these, I would suspect, like, part of design schools not getting into these boxes of standard form and standard shapes. It’s interesting to me, talking to people on this podcast about how people come to the field from all these different perspectives and specialties.

KS: Yeah, maybe that’s the reason. At the university, we were always working on the same project, and we were competing with each other. So we knew that the first thought that you have about how to visualize something or how to do a design is probably what everyone has. So you need to iterate more or you need to work on the visualization again. You don’t need to stay with the first version that you found. You need to think about this more. But I’m also doing the same thing as others. So I am starting my visualizations with bar charts, line charts or very simple charts, for example, when I just want to understand the dataset. I don’t jump on to the design immediately. I always create something normal, I would say. And if the visualization is understandable and interesting in that boring, bar chart version, then I can move on to the design part. So there is a certain [inaudible 00:17:01] I need to do my visualizations in.

JS: Right. So we’ve tracked your career from university and trying these contests and how you approach the process and in doing a lot of manual work. But now, you have some recent projects that are using D3, and I was hoping we could talk a little bit about those too. So you’ve got – I thought we talked about two of them. So the first project is Plot Parade which was a cool data art project. And then you have this other one Tinder Insights, which I have, I’ll say, no experience with Tinder, [inaudible 00:17:36]. But we can start with Plot Parade, really, really interesting. Maybe you just talk about it and what the inspiration was and how you’re building out these visuals.

KS: Yeah, sure. So Plot Parade is a personal project of mine, which I just started to learn these three, because I started very late, and I always feel like I can learn something new every week about this. So normally, when you are creating a data visualization, you always start with the dataset, then you find the right visual for that specific dataset. But I wanted to reverse this approach. So I was looking at some graphic design blogs or some collections, and I found these interesting graphic designs, where I found these visual patterns that I thought about, and I thought that, oh how interesting this is, but I wonder about the data, what is the data behind. And most of the time, I found nothing, because they were just graphic designs, but I could see some kind of data behind. So I just imagined the right dataset behind these graphic designs, and that’s why I saved these designs for inspiration. And, I guess I was waiting for the right dataset to come along, so I could use these designs as inspiration, but it never happened, because I always need to study the dataset first and find the appropriate visual form for it. So this is why I decided to reverse my approach and start with the visualization first, and then I designed the data structure for the specific visualization. So these data visualizations that you can use on Plot Parade, you can enter your own datasets and these are very simple datasets. So something that you would use in a dashboard, something like that would require only a bar chart or line chart, so nothing something complex. But I always found that graphic design is a little bit ahead in terms of design, compared to dashboards, so I wanted to get those elements from design, typography, spacing, and use them in data visualization, and just try, if it’s possible to visualize this simple dataset in a more beautiful way, sort of using just graphic design. So this is how I ended up creating Plot Parade, and I always have some new ideas or I get inspired by other designers first, and it’s also a good way for me to learn D3, because I just set these challenges for myself and I am always going to this like thinking, oh I don’t know if it’s possible to do this, let me try this. And I force myself to learn, and I found it very useful for me also to deepen my knowledge of D3.

JS: Right. So I’m just poking around at the project now and some of these are great for bar chart overlaid with pictures or just like a line chart with a nice design aesthetic. But there’s boxes here for more, so is this the sort of thing where you just envision constantly, kind of just updating it as you get inspired?

KS: Yes, that’s right. I’ve been lazy for a while. So I have some finished stuff sitting on my computer. But yeah, I always have new ideas that I want to try, that’s probably something that I’ve never seen before in database. I just want to try if it’s possible. And yes, I want to upload more and more as I will find new time to work on this.

JS: Yeah. Well, I love them. I mean, they’re great. So I look forward to seeing some more of these. Now, the other project is a little bit different – Tinder Insights, DataViz tool to track your Tinder data, I guess.

KS: Yes, that’s right.

JS: So I have even less experience with this. So do you want to, although I will say the first image on the website is this really cool flowchart about, I would guess, some imaginary, made-up person of swiping and matches, but do you want to talk about this project and where this one came from and how it works?

KS: Yes. So I don’t really have experience with either. But this is a project I created with my sister actually. We always wanted to work together on something, and she’s a developer, so we just needed to find the right project. And she’s a Tinder user actually, so the idea came from her because she found that you can get your own data from Tinder. So they are storing everything, and you can get your own data, and they are going to send you a JSON file with all your swipes and how many times you logged in and your chats. And we thought that this is a good opportunity to visualize this somehow. Of course, most of the Tinder users cannot visualize their own data, but we created a website where they could upload this dataset and create these visualizations and infographics for themselves from their own data. So I realized these visualizations when somehow you are looking at your own data. And I think it’s much more interesting, then you are also involved in the visualization, so I really wanted to create something like that when you are looking at your own data, and it’s much more interesting for you. So yeah, the first flow chart is more like the usual data visualization that I’m creating, and the other ones below are more like these infographics or number declarations. There is a reason we didn’t want to make this more complicated, because we are thinking about average user and how much they know about data visualizations and how much they can read data visualizations. So we wanted to keep it very simple and easy to understand, and we are also comparing the users to the average. So we are storing anonymously some information about the people who are uploading their data to this website, and we can do these comparisons, like, how many times you get a match compared to women or men. And we are also adding COVID statistics, so we are comparing how Tinder usage has changed before and after COVID, if people are dating more online or less.

JS: Oh interesting. And so then, are there visualizations that compare the individual to other people, or, I guess, the overall average, I guess?

KS: Yes. You can compare yourself to women and men. So we created these two groups, because there is a preconception, which might be true [inaudible 00:25:41] through that. Women have a much easier time on Tinder or any other dating apps, because they just need to be out there and they can just get many dates. And men, they have much fewer matches, and they need to work more sort of to get more dates. So this is why we wanted to create these companies and so people can see if this is true or not.

JS: Right. And do you ever envision creating sort of separate graphics from this, I mean, eventually, you’ll have some database full of anonymous data with sort of averages, do you ever envision creating, of our sample, this is a graphic of the average swipes and number of dates, etc., etc.?

KS: Yes, that would be very interesting. We still need to have that dataset. But yes, eventually maybe we’d create something like that, something more complex and something more exploratory visualization.

JS: Right. Cool. Well, I am a big fan of your work, of all the complexity that you add, so these projects look great. I’m already a big fan of Plot Parade, looking around so much fun.

KS: Krisztina, thanks so much for coming on the show. This has been great chatting with you. And yeah, thanks for coming on the show.

JS: Thanks for having me. It’s been fun.

Thanks everyone for tuning into this week’s episode of the show. I hope you enjoyed it. I hope you learned something. I’d definitely encourage you to check out Krisztina’s website and her projects and her portfolio, some amazing work there. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.

A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please visit our Patreon page at patreon.com/policyviz.