Mona Chalabi is a data journalist and data editor at The Guardian US, where she publishes columns about data visualization that relate to the news. Prior to that, she has been known for the Dear Monacolumn at FiveThirtyEight and by the Strange Bird’s voice “telling us about the things that make us feel lonely.” Mona’s outstanding visualizations and drawings have even been commended by the Royal Statistical Society. Her talent and unique style stand out in every way, so we invited her to talk a little more about the behind-the-scenes of her visualization masterpieces.
NR: Mona, tell us about how you became aware of the field of data visualization and decided to get started.
MC: It was when I was creating data visualizations about Iraqi Internally Displaced Persons (IDPs) and refugees for the International Organization for Migration (IOM). But I very quickly decided that I wanted to share data visualizations with a much broader audience, so I also did a one-day workshop at the Frontline Club, which is a journalism club in the UK.
NR: Can you explain who you mean by a “much broader audience?”
MC: I was frustrated with the fact that, for so many NGOs and charities, we were only sharing our data and information with donors rather than the people impacted by the policies. I understand that’s part of the work of advocacy, but it creates a feedback loop where you constantly think you’re right because the people who should challenge you and tell you if you’re wrong don’t get to see the data visualizations.
In the IOM example, we’re sharing it with the UN, asking them and countries’ governments for more money, but the Iraqis who were represented in the charts don’t get a chance to say to us “you’ve told us that we need food and blankets but what we actually need is electricity generators”.
So now when I say the “broadest public,” it is the people who were affected by policies and who are trying to make informed decisions in their life.
EM: Your work seems to speak in a sort of comfortable everyday manner that doesn’t seem aggressively radical. Was that something that you were actively pushing against? Do you think of yourself as leftist or more as sort of holding people to account within the system?
MC: It’s not about scrapping the entire system. In the UN example, I’m not telling them to change what they’re doing, but rather to listen right and be true to their actual goals. But I do aspire to be a radical and I hope that I manage to show some of the injustices that exist. Every single data visualization person approaches data sets with their own hypotheses and they’re still built on their privileges. But the more that my career progresses the more privileges I accumulate, the further I get from understanding what a truly radical position could look like.
I also don’t think journalists, myself included, try hard enough to ask people what matters to them. I personally rely on members of the public and people like my sister, friends, and family to call me out and to hold me accountable to that radical agenda.
I did an illustration about Amazon’s profits and how little they’re paying taxes. It did really well and it got people frustrated because it shows that there is something broken in the system. But it doesn’t really show the mechanics of the system, and that is what data visualization I find is quite weak at.
So data visualization isn’t very well catered to a radical agenda because all it can do is show you the consequences of the system and not how it operates. Again, I’m trying to understand the mechanics of the injustice a little bit better.
EM: Do you think that’s a quality of data visualization itself or of the people doing data visualization? Does this have to do with why you do hand-drawn rather than clean lines?
MC: One thing I’ve talked about a lot is intuitive storytelling. I think one problem with data visualization is that we’re trying to communicate complex things and just draw it all out and we say “find a story yourself.” It’s one of my issues with interactives where it’s on the person looking at it to find a story for themselves.
I made a visualization of children that are detained by ICE. It looks like an illustration or cartoon of Trump receiving and giving money and these organizations detaining children. That’s a demonstration of the system hopefully in a way that makes people say: “When I want to break this chain, I can either focus my energies trying to stop Trump from giving money to these companies or ask for change in the way that the lobbying system works and the fact that these companies give money to a presidential candidate.”
There was this illustration of how becoming a mother affects your salary. It’s a double-page board game in a magazine. At the top, you say whether you’re a mother or a father, and then give your marital status, and then your race. It breaks apart each of these demographic factors so that you understand the final numbers. Generally, most journalists simply report on the national averages. But exposing the system and exposing its consequences go hand in hand. You must show which groups are affected by these policies and how they further oppress the already oppressed.
EM: A lot of your pieces are sort of in-your-face negative, in a way that data visualization doesn’t typically do. There’s a distantiation that comes from the clinical way people use traditional data visualization, whereas you’re literally showing the tip of the iceberg of domestic violence and how many of them are reported and such.
MC: To me, the clinical version of data visualization, which is this idea that we are simply presenting the facts and are cold and emotionless, doesn’t make sense for topics that relate to social justice. So, yes I hope that people don’t feel powerless looking at my illustrations, but I also hope that they feel a sense of outrage. I think that that’s the first step and then telling people what to do about it is the second step.
NR: How about career? How did you navigate your early career options until you found your own style? How did you realize this style best communicates what matters to you? Why not a different orientation?
MC: Like every person, I tried to take these sideways steps to get from career A to B, and data visualization was kind of an overlap between what I had studied, my previous jobs and journalism. I think at one point I thought that I’d be a normal journalist and data journalism was like a transition step. But I very quickly realized it was kind of the end goal rather than a midway point.
EM: What made you realize that?
MC: Because it feels really good and I found it effective. I wanted to go into journalism because I like interviewing and finding out about people, and what’s exciting about data visualization was the ability to find out about many more people at a time — and that was even more exciting!
EM: Could you talk about how you determined that you were really good at data visualization? What tips could you offer an early career person to identify what it means to be good at data visualization?
MC: Initially, I was good at writing fast with as few errors as humanly possible, which is valuable in the newsroom. But now, it’s about whether I reach the people in the illustrations. With data journalism, I would do an illustration about young moms and it isn’t assuming that if you’re a 15-year-old mum you’ve ruined your life. Young moms look at the illustration and get something from it and that’s my measure of having done something right.
Having said that, I don’t think we talked enough about how easy is to completely get it wrong. An example was when I created an illustration based on a study about whether or not a language is gendered. It was a peer-reviewed study and it didn’t even cross my mind that they might have miscategorized those languages. So when I posted it online I got lots of people commenting “Armenian is actually genderless,” “this language shouldn’t be here,” etc. I redid the illustration, but it’s still so interesting to me that the first one that contains the errors has way more likes.
How do you remain transparent about having made mistakes? To take it down feels like it’s hiding away because I fucked up, however, I still think that people are taking the first one and not the second one because of the way that the internet works. So to answer you … I feel like I’m good at it and I still get it wrong like relatively frequently and there’s not necessarily a contradiction there.
NR: Have you ever had the impostor syndrome?
MC: Oh really interesting! There are definitely times where I feel like I do not belong in the groups I’m spending time with. But I wouldn’t say I have impostor syndrome, I think I have an outsider syndrome and I wouldn’t necessarily describe it as a syndrome either because it’s a fact and that’s okay with me. I kind of want to remain an outsider. I have a friend, she’s very senior in the New York Times graphics department, and I was kind of flattered when she said to me that my name gets mentioned in meetings. I was extremely flattered when she told me that they said “no she’s not us,” and I’m like “cool! that’s absolutely fine by me.”
EM: So, would you consider yourself like an interloper?
MC: I’d say, active translator. So when I find an academic study or government numbers, I’m trying to translate them for a general audience and I think translators are inherently outsiders. You never inhabit either of these two worlds. I also didn’t see myself as a pure journalist or a pure artist or a pure academic or a pure anything. I’m like just in between these worlds. I mean, I’m good at data visualization. I’m not really in statistics at all and I actually think in some ways that that helps to be a good translator.
NR:What kind of worst comments versus best comments have you gotten on your works?
MC: The best comment I can get from the people I know, friends and family, is “it’s good.” If people say that it’s clear and understandable or suggest what might be different, I’ll say that’s perfect! The worst comment is “it’s wrong!” It makes my heart sink. I don’t really mind anything else. I’ve got quite a thick skin when it comes to sexist or racist insults.
EM: How do you deal with the fact that certain things are wrong to certain people, that many facts are actually very much rooted in someone’s experience and background?
MC: I did an illustration about hirsutism in different racial and ethnic groups that got on very well. The bar chart showed how prevalent hirsutism is in black, white, and hispanic arms. People started saying “this is utter BS! I’m Hispanic, I have hair down to my wrists!” They took it very literally thinking that I was claiming that that’s where the hair ends on different women’s arms. All I could do was sit and watch the comments, because no matter how insane that was to me, if there is a considerable chunk of people who are left feeling more confused, then there’s an absolute legitimate reason for either scrapping the visualization or rethinking it completely. So again, it’s about: Does this contribute to public understanding or their misunderstanding of the topic?
NR: We saw you on TV, on a well-produced show. What is this like to be a television star?
MC: Yeah I’ve done some TV things. TV is a funny corner. But I definitely wouldn’t say I’m a television star…
EM: Why? In the constellation of people doing data viz there’s you and Hans Rosling that have shown up on TV.
MC: I have friends who described me as the Pamela Anderson of data visualization, just because there is a relatively small ecosystem of public-facing women doing data visualization. It’s all relative, so in the world of people who do data visualization on TV, I might have a bit more TV exposure and I think it is very important for data visualization people that all of us maintain a larger perspective.
NR: What do you use for your data viz? And what do you say about some critiques that your illustrations are imprecise?
MC: I use all of the regular data analysis tools that most people do, and on top of that I use pen and paper and Photoshop and InDesign and After Effects.
The starting point is very often an Excel chart that is then traced out. I would actually clarify that the illustrations aren’t as precise and emphasized as the computer-generated graphics. They’re just trying to communicate the imprecision of the data that underlies it. So you’ll never see decimal places on my illustrations because it’s so rare that we truly know anything about them. Things that are written to two or three decimal places are an overstatement of accuracy that is in the interests of the person who put them down because they think it makes them look smarter and more precise.
I don’t do that. I hope that you walk away with scale because what will you remember is the overall fact and the relative scale of two or more things. My data visualization is about scale.
EM: Do you feel like the problem of the decimal place also happens visually? Like, people use visual forms and signals to do that same sort of misusing rhetorical devices for their own to make themselves look smart?
MC: So if you think of a choropleth map showing polling data that has 10 different buckets. You can’t differentiate between one polling and the next. To put them as two different colors on the map is misleading. Have fewer buckets and then be more honest about the fact you don’t know the data to that degree of precision.
NR: Which three words would you use to describe your style?
MC: I hope that accessible and transparent are one of them. I don’t really know… for me, the work only lives in its audience. So I think it’s for those people to make up those adjectives. The intention in the works also varies depending on what the visualization is. If I show you something on Amazon, I want you to feel angry, and if I’m showing you something about the most common dog names in New York, I’m kind of hoping you feel amused.
EM: Do you see the influence you’re having?
MC: In some areas, yes. It’s very strange! A former colleague replicated some of my works. I am mentioning him because to me it’s clear it was me. He took a series of bills and cut them up and pasted them down as he was explaining wage disparities. But it’s hard to say it was just my influence. Maybe there was a general trend on data visualization being more accessible to more people that you could say is part of a broader trend as well.
NR: What do you think that the data viz community is good at when it comes to social justice and helping the powerless? What work with data visualization should always be encouraged and what shouldn’t?
MC: I don’t know what “community” exactly means. Is it data journalists? Is it like such a big and disparate group that doesn’t even necessarily talk to one another? But I think we’re pretty good at accuracy. There can be a lack of creativity sometimes. The chosen topics are not necessarily the ones that are of interest to the general public. I think there isn’t enough checking to make sure that the illustrations or infographics or visualizations make sense to people outside of those that work on the same ventures.
Another big problem is sometimes the arrogant assumption that people are going to spend a couple of minutes trying to understand this beautiful thing that you’ve taken a long, long time creating. The reality is you have seconds, and if you haven’t understood that then the visualization has failed.
NR: Do you have data viz practitioners or artists or data journalists whom you look up to?
MC: I love W.E.B. Du Bois. He’s always the first person I mention. There’s my former colleagues from The Guardian Kenan Davis, Aliza Aufrichtig, Rich Harris, Jan Diehm, and Nadja Popovich, all doing really really good stuff. And my current colleague Juweek Adolphe … there’s so, so many people!
NR: How about your message to the Data Visualization Society? How excited are you about it?
MC: Yeah, thank you so much for featuring me. I am still learning how to catch up with Slack messages but I’m really really excited to work with you!
Mona, thank you so much for this very interesting interview and for sharing your pieces for illustrating this article. You can find more of Mona’s work on her website.