If you conduct a quick internet search on “history of data visualization,” you’ll nearly always see Florence Nightingale included in the annals of history. Why? It’s not like a Nightingale Rose chart is easy to read, or a cinch to make, or even all that common.
One clue to the answer lies in the fact that she is most often the only woman on such lists.
Many women know that when you are the only woman present on a panel full of men, two things are highly probable: (1) you are working in a male-dominated field, and (2) you are likely a token because somebody thought there had better be some diversity. Florence Nightingale is our data visualization historical token female.
History is written by cis white men. And history is upheld by them too, even among the data visualization crowd of today, who cull these lists of historically important figures and decide whose stories will be remembered, whose work will become “foundational.” At least initially, they, like me, perhaps took to Nightingale because they readily recognized her name from high school history books (written by — guess who) where her role in the war was recorded— though her Rose chart usually wasn’t printed.
But if her inclusion in the annals of viz history is really about how impactful or accomplished Nightingale was as a visualizer, we would see greater discussion of her OTHER charts and graphs, instead of reducing her contributions to a single image. We would learn that she was the first woman voted into the Royal Statistical Society. We would read about her as the equal to other “founding fathers” of the field. (RJ Andrews is expanding on this topic, which you can find here.)
If her presence in these lists was really about including historically important visualizations, we would see those same lists and books and classes include many more female visualizers throughout history. Who, you ask? Take Emma Willard, a pioneering educator who revamped the way geography was taught by designing her own set of more accurate and contextual maps. While typical geography curricula used maps that focused on the thirteen original US colonies, thereby starting the discussion at the American Revolutionary War, Willard drew maps based on Native American territories (though they would likely be disputed by Native Americans) to focus the conversation on what was happening on the land before the invasion. They would also include Florence Kelley, Annie Maunder, Marie Neurath, and Mary Eleanor Spear— just to name a few. Stay tuned for more profiles on these women to come soon in Nightingale and in the meantime, look up the work of Catherine D’Ignazio & Lauren Klein.
So why is Nightingale the token?
I’m no historian, but I suspect it’s in part because the accounting of viz history is rooted in Europeans and their wars: Nightingale’s Rose chart on soldier deaths, Minard’s alluvial diagram of Napoleon’s march, Playfair’s original pie chart of the domination of the Turkish empire, and so on. The scope of our field’s history, like so much of the way history has been framed, is Eurocentric in view and rooted in conquest.
If we wanted a fuller picture of the history of data visualization, we would need to include North African cave paintings dating back to 6000 BCE that tracked shifts in weather and migration. We would need to include the Aztec calendar that was our original timeline (well, time circle — just goes to show you that linearity is not universal).
These historically important visualizations go far beyond Europeans and wars but have been almost entirely omitted from historical discussions primarily conducted by cis white men about cis white men. (If you want to stop reading because of this phrase I challenge you to first ask yourself why and then to keep reading to see what you can learn from being uncomfortable.) When history is written to uphold the dominant power structure time and time again, it’s no wonder we get visualization history that includes cis white men and the token white female. (Just to be clear — white women and people of color have most certainly had their role in upholding various aspects of this dominant power structure, too — Florence Nightingale included. But given clear patterns of oppression, many would agree that everyone else is following the cis white male lead.)
Women have always had to fight for credibility and recognition in a male-dominated world.
Nor is this struggle specific to women exclusively. We know that the contributions of many non-male, non-cis, non-white people have been rendered invisible over the years. And while you’d think visualization (or really any major field like science, mathematics, or engineering) would get visibly more inclusive as time moved forward, such has not really been the case. In the modern era of data visualization, many point to Cleveland, Tufte, and Few — who all share a particular demographic in common — as forefathers. But how many of the foremothers referenced above could you name? Have you been taught the legacy of their impact on the field, or seen the countless examples of their work? It would be absurd to assume these women didn’t exist — when really they fit an unfortunately all-too-common historical pattern that their work is simply not recognized by the dominant voices throughout history.
Even today, where the data visualization field is large and international, cis white men still dominate, through both hidden and overt ways. Men and women alike are probably able to identify some of the more overt ways, so let me tell you three stories about the more hidden ways that data visualization is still a man’s world, despite the efforts of women (and some men) to change it.
At the end of 2014, I listened to an episode of a prominent data visualization podcast in which four cis white men, located in different parts of the world, in which they reviewed the cool visualization milestones and important moments for our field that year. You know, providing recognition and credibility. In their episode and in the show notes that accompanied it, they didn’t feature a single woman. The kicker was that, at the end of the podcast, they lamented how hard it was for them to get noticed. All four of them have books, cited articles, talks, and/or widely-recognized visualizations. While we all may feel recognition is hard to come by, a group of fairly visible men lamenting they’re not more visible, while directly contributing to the exclusion of women from the very narrative of our field, demonstrated a complete lack of self-awareness. In other words, if they thought they had it so bad, try being a woman (or person of color for that matter) in this field. It led me to write a blog post (not updated) that culls together a list of women in data visualization.
My post led to praise and anger on social media from men and women. Some women did not want to be pointed out as a woman. They were afraid of being tokenized, included just because they are a woman. Some men were defensive, claiming they just didn’t know that many women in the field, and that intentionally identifying them would feel tokenizing or even stalker-y. It is a complex tension, isn’t it?
Two years later, the same four men had another year-end episode and I remarked that it might be nice to have a woman on the show to add to the voice of authority about what was Important That Year. The same tensions arose around tokenism. The thing is, women (all people) want a voice at the mic because of their good work, not their gender. People in a place of privilege should highlight females because their own understanding of what’s happening in the field is broad enough to include the incredible work being done by women. While this piece isn’t meant to be a primer on how to avoid tokenism, engaging equally (in time spent, attention paid, payment rendered and respect extended) with the full body of work that countless women have produced is a pretty solid way to avoid it.
In between those episodes, I started my own podcast in part to grow my own credibility and in part to highlight others. As I was logging into Skype to record our 10th episode, I heard my co-host and the episode’s guest (two cis white men) chatting. My audio had connected before my video. And I overheard them talking about my appearance and discussing whether they found me attractive. Then I heard one of them say “Oh, looks like she’s joining” and they abruptly stopped the conversation. I didn’t really know how to handle the situation. I did what a lot of women do on a regular basis: Smile and act like nothing happened. Gloss over the injustice and try to be a professional. Grin and bear it.
The commentary, however unintentionally harmful, undermined my credibility and damages the ability for everyone involved to provide the best of ourselves to the world. It makes me and every woman who has witnessed something similar have to fight twice as hard to be heard.
Just this year, the aforementioned podcast guest from episode 10 contacted me with an invitation to be a featured speaker (for free) at a conference he regularly organizes. I realized this was an opportunity to respectfully reply that my participation was contingent on the conference abiding by an inclusion rider.
Popularized by Frances McDormand in her Academy Award acceptance speech in 2018, an inclusion rider is a stipulation that requires diversity. The specifics are usually negotiated, but in my case, I asked that the other featured speakers proportionately reflect United States demographics in terms of race, gender, disability, and LGBTQIA+ status. An inclusion rider is one way that people in places of privilege can fight together with those who have less privilege for an equal spot at the podium.
His response to my request was that he would be uncomfortable finding out those demographics, particularly the latter two. I pointed him to Frances McDormand and the group she was working with and their STACKS of resources on how to go about fulfilling an inclusion rider — all of which was easily Google-able. And I didn’t hear from him again. No featured speaker slot means no boost to my recognition and credibility.
My three stories are not just mine. These happen in some iteration to women all across our field (and in every other field, too). Many women share my frustrations with getting equal play in a male-dominated field. Some defensive men might point to the well-deserved rise in popularity and recognition for individual women. Nadieh Bremer, Giorgia Lupi, Stefanie Posavec, Amy Cesal, Mona Chalabi, and Michelle Rial are just some examples of women who have been making waves lately. Their work has an artistic aesthetic with a human touch — often hand-drawn, made with markers, colored pencils, or play-doh. Their work is awesome and deserving of praise but that playfulness is in stark contrast to the spartan, tech-oriented, traditional aesthetic we commonly see from impact-focused thought leaders.
In other words, I just don’t see many legions of cis white men following their lead the way they line up behind other cis white men in our field. And when cis white men do, they are freely allowed to bounce back and forth between the artistic and spartan aesthetics, whereas these women have been somewhat pigeon-holed and not socially graced with the same freedom. Moreover, I would put money on the notion that these women have stories similar to mine.
The problem is most definitely not that there’s only one group capable of being high-quality data visualizers. I have heard, in the darker corners of the internet, commentary along the lines of “women don’t make visualizations up to the standards” which is the data-based version of “women just aren’t funny.” This just begs the question of — by whose standards? Literally, who wrote them? This field is young enough that we can actually find out. Tufte, Brinton, Few and others articulated their standards pretty clearly actually. They have one particular set of characteristics in common. Standards that don’t specifically include other viewpoints perpetuate the historical status quo.
The problem with stories one and three is that they limit the scope of the field. If you only see/recognize work that resembles your own, or by people who resemble you, then your definition of the data visualization community is inherently tiny. Similarly, if you only look at the people in your Twitter feed, you are missing out on a lot of cool ideas and awesome visualizations made by people who happened to be women, of color, with a disability, and/or LGBTQIA+.
So when leaders in our field produce newsletters that predominantly feature the work and thoughts of white cis men, it perpetuates the notion that their views are the ones that matter AND that there aren’t others outside that demographic at all. Stephen Few primarily referred to men’s thoughts and ideas in his newsletter, often referencing something one of them had said in the comments to a previous edition of his newsletter, creating a cis white male echo chamber. Why did he cite these men’s comments? Because men were the vocal majority in his comment thread, a place widely seen as combative and hostile (reference my earlier discussion of men and war). While some in the data visualization world find that trashing Few or Tufte on Twitter will earn you a lot of likes, the fact of their influence on this community is unavoidable.
What does it take to get beyond Nightingale?
This is the section of the article where I conclude with positivity and solutions. But I am going to depart from the long tradition of women asking (begging?) men to change their behaviors. I am not going to provide a bulleted list of concrete solutions and steps to take. Why not? Because those answers are already out there. They have been said (screamed?) for generations. You just have to pay attention. Women work hard for recognition (and non-white women work even harder).
While there are more sources of credibility and recognition, who we cite and who we invite as speakers and who we showcase on our podcasts are important contributing factors. There’s plenty of history-making to go around.
Dr. Stephanie Evergreen is an internationally-recognized data visualization and design expert. She has trained future data nerds worldwide through keynote presentations and workshops including Facebook, Time, Adobe, Verizon, and the United Nations. She writes a popular blog on data presentation. Her books, Effective Data Visualization and Presenting Data Effectively, both hit #1 on Amazon bestseller lists.