I’m too new at data visualization to create a best-of-the-2018 list, and my only work resolution is to keep going. Hi! I’m here! I like graphs! Instead, I’m starting the year by reflecting on why I do this work. Why on earth do graphs matter so much to me?
I recently came across this quote from Brenda Euland’s So You Want to Write: A Book About Art, Independence, and Spirit:
“When Van Gogh was a young man in his early twenties, he was in London studying to be a clergyman. He had no thought of being an artist at all. He sat in his cheap little room writing a letter to his younger brother in Holland, whom he loved very much. He looked out his window at a watery twilight, a thin lamppost, a star, and he said something in his letter like this: ‘It is so beautiful I must show you how it looks.’ And then on his cheap ruled note paper, he made the most beautiful, tender, little drawing of it. When I read this letter of Van Gogh’s it comforted me very much and seemed to throw a clear light on the whole road of Art. Before, I thought that to produce a work of painting or literature, you scowled and thought long and ponderously and weighed everything solemnly and learned everything that all artists had done aforetime… but the moment I read Van Gogh’s letter I knew what art was, and the creative impulse. It is a feeling of love and enthusiasm for something, and in a direct, simple, passionate, and true way, you try to show this beauty in things to others, by drawing it. And Van Gogh’s little drawing on the cheap note paper was a work of art because he loved the sky and the frail lamppost against it so seriously that he made the drawing with the most exquisite conscientiousness and care.”
There are a lot of ways to make art, and a lot of ways to visualize data, and a lot of reasons to do both. But on my best days, my work comes from the same place as Van Gogh’s lamppost. I love my subject, and I want to share it. When I can’t love my data, I choose to respect it. Sometimes I respect the effort that went into gathering the data; sometimes I respect the subject that the data represents; sometimes I respect the truth in abstract and extend that respect to the subject at hand. Conscientiousness and care spring directly from that love and respect. How else can I treat something that matters to me?
Van Gogh loved the lamppost, but he also loved his brother. He drew the lamppost because he wanted to share it. The audience of a visualization matters to me, too. Sometimes I have a personal connection: I know who will use a visualization, and I want them to understand, because that understanding will answer a question or entertain them or help them make a decision. Sometimes the audience matters in an impersonal way. I’m asking for their time and attention–the least I can do is remember that I’m working for them, not for me.
Euland waves off long and ponderous thought, learning, and scowling. But these are also part of the process (especially the scowling). The best visualizations respect the reality of their source material and the capacity of their audience, and extend one to meet the other. Knowledge, skill, and experience make that extension possible.
There are times when respecting my data feels like hauling an anvil up a mountain. I can guarantee that I won’t always love my audience. (Who among us hasn’t made a graph out of spite?) Sometimes I play with form for its own sake, without an eye to audience. Sometimes I want to make something beautiful, even if it doesn’t say very much. But I try to come back to subject and audience, and the simple desire to share.
Prioritizing subject and audience helps to avoid the pitfalls of data visualization. Misrepresenting the data prioritizes an agenda ahead of my subject. Concealing the limitations of my data and the uncertainty in my conclusions prioritizes my ego. Carelessness with a data’s source and context prioritizes my ease. Splashy, opaque graphics use the audience for views and clicks, rather than prioritizing what they could learn.
Love and respect make care and conscientiousness natural. Treating data with respect makes for better visualizations, because it gives us a reason to get it right beyond the fear of getting it wrong. I do this work because the world is important, and I want to show it to you. I do this work because you are important, and I want to show you the world.