Making space for unquantifiable data: hand-drawn data viz
What are the hardest parts of your life to measure?
I’ll be the first to say that I’m not an expert in paid and unpaid labor, or global gender equality. This project calls attention to all of those, and uses a dataset on the same from the OECD as its jumping off point. But the project arose because I am fascinated by the forms of truth we can authentically capture and relate through data and data visualization.
For me, emotions and work are both high on the list of hard-to-measure personal data. Emotional labor, the frustrated sister of “care”, might be number one on my list—it’s the kind of action that has taken me years to even recognize in my life, let alone track or try to quantify.
As a data viz person, I’m often curious about the boundaries of quantification when it comes to abstract or ambiguous “data” like care. Some forms of care are clear to see and potentially even easy to quantify: we can estimate the number of meals cooked for a child over the course of one childhood, how many diapers were changed in one infancy, or how many hours and miles were spent driving an elderly parent to appointments.
Other forms of care also make an enormous impact on our lives, but are much more abstract and much harder to pin down as data points. It wasn’t just the number of meals cooked: this mother passed on both a love of food and a connection to family history through her attention to food each week. This father didn’t just pick out a book to read at night: he modeled a comfort with emotional vulnerability and gained a child’s trust through his response to each story. There is so much more to “unpaid labor” than we can ever hope to quantify in data, and unpaid labor is so much more than simply a counterpart to economic productivity.
And that’s okay! But I got to the point where there was a big “so what” bubble over every data visualization I made. I began by making a visualization I know and love: the scatterplot.
It’s simple, it’s straightforward, and it cuts right to the math of everyday scenarios. And in this case, it left me completely uninspired. “Cutting through the noise” doesn’t feel like the right approach at all when it comes to quantifying unpaid labor and care work. So often, that “noise” is the care. Care doesn’t come pre-packaged in data units
From a data viz perspective, I felt more empowered to represent the shortcomings of the current data situation, and prompt users to think about what’s left out, than I did to make a original and cogent point about unpaid labor on a global scale. The OECD data includes barely any data from South America or Africa or Central Asia, so I asked myself: given the lack of global or national data, how can I go radically personal instead?
This project was inspired by the idea that inherently “messy,” ambiguous, or uncertain data can, and maybe should, be visualized in a way that upholds and centers uncertainty, rather than trying to hide, smooth over, or “fix” it. This visualization makes space for critical thought about abstract and concrete ways of knowing the world. It prompts the user to consider the challenges and shortcomings of creating and visualizing data that reveals an authentic truth about a complicated concept. It takes an inherently messy concept and responds not with an attempted solution, a universal truth, or even a simplification, but with attention to the clarity that can come from considering one individual experience and inviting the experiences of others.
That’s a lot of words to say: this visualization is for each individual user to change as they want to. It’s hand-drawn because that helps remind us that people are always the ones making data, and that this data is about people. It’s important not to forget that.
As an end note, a few shout outs: This project is the theoretical product of data humanism and data feminism. I won’t go into lots of detail here, but in brief, here are the women who collectively and unknowingly helped bring this about: Johanna Drucker, who writes that “data is captain, taken not given.” Caroline Criado Perez, who shows in Invisible Women how continued inattention to women-specific data collection leads to a real and true lack of understanding and information about women. Donna Haraway, who pointed out that perfect objectivity — seeing everything from nowhere — is a fucked up god trick, and everyone’s knowledge is situated. And Catherine D’Ignazio and Lauren Klein, who coin the term Big Dick Data to describe projects that overestimate the scope of their knowledge “because data!!” Thanks, y’all.
