The academic politics of data visualisation

This is just a short observation, but one I think is worth sharing.

(forgive current lack of links and references, but I got the blogfire in ma belly)

With my digital humanities hat on, this week I participated in some training laid on by Warwick’s Centre for Interdisciplinary Methodologies. We covered Gephi and Mondrian, two data visualisation tools. It was run by Bernhard Rieder and it was fantastic to get my hands on some training data sets and start to use those tools for real.

Bernhard contextualised the growth of interest in data in the humanities and social sciences.  At one point he asked himself, “is it reductionist to work with data about complex multidimensional social issues? Well, yes”. But as he contextualised, the point at which you’d use these tools is as one of many methodologies.

By the magic of the twitters, he has shared his slides with me, and here they are:

My introduction to data visualisation really came a few years ago, from Tony Hirst and then Martin Hawksey. It was in two other contexts: social network analysis, and learning analytics. The context that attracts the most controversy is learning analytics. Believe me, I’ve been in workshops where people have got upset and angry looking at barcharts and network diagrams about student progression and correlations between grades and online activities.

My observation is this: it feels that the e-learning field is first encountering data visualisation in the frame of learning analytics. Learning analytics is a highly political field in this age of funding cuts and emergent mooc business models.  It is not surprising that the e-learning community views data visualisation with skepticism, given that loaded framing.

But from a broader academic technologist perspective, I see the drivers for data visualisation from within research methods, from research altmetrics, from public engagement with scholarship. I see it as a positive move, an area needing rapid skills development. I don’t see it as reductionist. But if I didn’t have that broader angle, I’m sure I would.

2 thoughts on “The academic politics of data visualisation

  1. Pingback: Blog Spurt | fragments of amber

  2. Hi Amber,

    Ah, always difficult to use a big word like reductionism, which has many negative connotations (and one could ask why?) and then not dedicate enough time to it. A couple of points to clarify:
    – I would consider all description to be reductionist in the sense that social reality can never be full captured.
    – In the context of network analysis – and any computational methodology really – the necessity to formalize means that we have to apply categories, filter, purge, etc. Very few phenomena could be said to be, by themselves, not more than points and lines.
    – Reduction and formalization make computation and that opens up new ways for knowledge production. And it’s probably the only way to think in what’s quickly becoming a real intellectual frontier: “the many”.
    – I would consider what people call “qualitative” to be reductionist as well, but in different ways.

    Interestingly, much of the new tools make not only quantitative approaches easier, but also qualitative ones, e.g. by providing ways to create a collection of items.

    My argument would be this: instead of pitting the qualitative against the quantitative, the manual against the computational, the interpretative against the counting, we’d be better served to appreciate the blind spots and biases inherent to every method and to fully engage the deeply political choices in choosing method(s). To blanket refusals of barcharts and network diagrams I would reply that they engage in their own way of limiting their intellectual options and by missing the opportunity to engage “the many”, they are, well, reductionists.


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