The Half-Full Learning Curve or “Could we be happy with what we already know?“
I sighed at the sight of the calendar I was holding in my hand. It was a paper one, the granny kind you ripped a page a day with the big bold numbers on it. It indicated the 20th of June though we were in September, proof of its moderate success in helping me better anchor myself in time. I tackled the meticulous task of ripping the 73 pages that separated me from the actual day. At last, I reached September and stared at the date, for it wasn’t any day of September, it was the first.
I can’t vouch for other European countries but here in France, we have two beginnings of the year—one on the 1st of January and one on the 1st of September. It would be difficult to know which one is the most important—though one is certainly more festive than the other—both marked the fresh start of a new year, both signified the end of the holiday season and most of all, both were heavily symbolic milestones that require you to take good resolutions. (It’s nice to have both because it gives you a second chance at failing.)
Therefore, staring at the big “one” on the calendar page, I was left wondering about which resolution I could take, work wise, for this new year, apart from ripping pages of the calendar as days go by. And believe it or not, but for the first time in my data designer career, I didn’t promise myself this was the year I would finally learn to code.
Until that moment, I kept hammering myself about the importance of knowing how to code, both as a data designer and as a woman (in a world hugely encoding by white guys). And trust me, I did try to learn—I attended bootcamps and followed online classes. I multiplied the learning attempts in persistent yet lazy efforts, continuously trying, continuously failing.
But now that I think about it, it wasn’t only coding. Starting in data visualization, I was hyper aware of all the skills I seemed to be missing. I kept discovering new ones on every corner! My statistics’ knowledge was embryonic, my data management skills were nonexistent, I could hardly babble in html. I was a diapered baby on the floor of a room full of shiny objects, either too far or too high for my reach. From my anterior professional lives, there was only one toy I had brought with me (or so I thought) and it was the duller of all (or so I felt)—design. Sure, I could design. Back then, I was an art director in a Parisian creative agency so designing websites, ads and books was familiar ground. The rest of dataviz skills though? All those were uncharted territory.
Yet, after a couple of missions, I noticed something funny. Before my years in design, I had been a French teacher. Those few years of trying to teach meandering grammar rules to poor students turned out to be extremely precious in my new occupation, for what is data visualization if not a visual, pedagogical effort? And before that, I had been a literature student, doing a bit of freelance writing for a magazine. The ability to digest a text or study, analyze and rewrite it proved to be quite useful as well. Suddenly, I realized I was more equipped than I previously considered myself to be.
Still, I wasn’t at peace. There were too many holes in my armor, I wasn’t geared up enough. Thus began the years of self training (insert boring montage of me in front of a computer, taking class and reading books to the tune of “Eye of the Tiger”). OpenClassrooms and Domestika were the neighborhoods I was wandering in. I had notebooks for every new skill I needed to master. I even thought about taking an Excel class (and excel class!); what had I become?
I believe we’re all taken by a similar frenzy of learning after staring at the depth of the knowledge we’re missing, joining the dataviz gang. There is just too much to know, too many skills involved in data visualization. Statistics, data science, data mining, data management, design, writing, UX, storytelling… Aside from a couple of people—Leonardo da Vinci, who seemed to master everything (and yes, I’m looking at you, Nadieh Bremer and some of your kind)—the rest of us are bound to feel wobbly. Still, isn’t it interesting how we all decide to focus on what we’re missing, what the others all seemed to know better instead of what we already know? In terms of seeing the glass half full or half empty, are we all quite pessimistic while seeing our fellow practitioners as filled jars?
You could argue that this hyper awareness of a structural inner flaw, related to the very nature of dataviz itself, is a good thing that pushes us to keep learning, keep aiming for a better, fuller version of ourselves. But staring at this big “one” on the calendar, I want to say no. No, I don’t want to learn how to code, I don’t need to learn how to code. I’m good already, thank you. Sure, I intend to keep improving myself as a designer throughout my entire career but I’m tired of wriggling, trying to fill with a spoon an inland sea I perceive empty. In a life where the curve can never be truly full, I believe we have to learn to be content with what we’ve already poured in.
Oouh! I wonder if there’s online classes on soft skills management…?
Loved this column? Rendez-vous here on Nightingale every 15th of the month for a new one!
Datacitron (aka Julie Brunet) is an independent data & information designer as well as the Creative Director of Nightingale, the journal of Datavisualization Society. She believes in the accessibility of information through design and storytelling, and the virtuous role data designers can play in our society
