12 February 2017
It's increasingly insufficient to think that more information leads to better decisions, or even a better informed public.
My entry into this world of tech, data and social good was via campaigning for the right to information. I still believe that’s a core and necessary part of what we do, but I also think we need to invest more into the layers after just accessing the information, and think more carefully about what skills we’re advocating for in data litearcy, and how.
In the talk the quote above is taken from, James Bridle talks about different types of information literacies that are needed to judge, understand, and act upon different types of information. It seems obvious when you think about it, but I feel like many information accessibility initiatives don’t take the time to differentiate between those types of literacies in a meaningful way (self included!).
In a piece danah boyd wrote in early January, entitled Did Media Literacy backfire she examined how our collective attempts at strengthening media literacy may have effectively missed the mark. It made me think hard about my work in the past around “data literacy”, and whether our efforts unconsciously encouraged bad practices that had quick gains but perhaps long term negative effects.
Things like teaching a data visualisation workshop using tools that make it very easy to make a visualisation from a spreadsheet of data. At least in the ones I led in the past, and in the very large majority of the ones I’ve seen, we rarely addressed issues of visibility, power, critically assessing the sources of the data and understanding its limitations. Instead, it was much easier (and, I’ll admit, more fun!) to get people excited about telling a story that they wanted to tell – especially if they had been wary about working with data beforehand. That fear often dissipated upon seeing it in a shiny chart or infographic.
Our aim was to get them more comfortable in working with data, but as I’m seeing all the time these days, calling out power dynamics almost always makes someone (usually members of the most powerful group) feel deeply uncomfortable. That discomfort is necessary and needed, but it can be difficult to make happen.
Last week, I had the pleasure of seeing Rahul Bhargava lead a workshop which was one of those rare few I mention above. He talked about data visualisation in a more comprehensive way than simply focusing on using the tools, and called out this fantastic point:
He also talked about the secondary effects of data visualization – it’s not all about the chart or the graphic that’s produced. It can be about providing a way for a community to feel more ownership and be involved in building the narrative that they’re telling the world about them. In the case of the physical, painted data murals that Rahul and his partners have been creating, actually making the visualisation can be a shared activity and a way of interacting with other members of a community in a meaningful way.
This feels like a great way of developing those different literacies that James mentioned above, but I have to admit, I’ve rarely come across initiatives like this. We started thinking about the challenging issues around power, visibility and engagement with last year’s Responsible Data Visualisation Forum (#RDFViz) but I think there’s a lot more to be done, and more broadly than just around visualisation.
All these are reasons why I rarely talk now about just “data literacy”, and instead, I now frame it more clearly as critical data literacy. The skills needed to copy and paste your spreadsheet into a great tool like Datawrapper or RAW seem far less urgent to me than being able to critically assess the limitations of your data and what it might make your viewer think; about who can access and understand that data, and what you want them to do once they have understood, to name just a few of those skills.
Generally speaking, I feel like the open data movement has grown up from thinking simply that the availability of open data will lead to better decisions, service delivery or more efficient use of resources. I wonder what the same “growth spurt” would look like for data literacy efforts.