Last night the ENAC (broadly speaking, the Faculty of Architecture at the Swiss Institute of Technology in Lausanne) had the pleasure to reflect on their own collaborative networks. Through the realisation of a bespoke project named “affinity map” realised by my friend and colleague Dario Rodighiero , the ENAC had a first touch-and-feel of how their policies of collaboration and mutual support have (or have not!) worked.
Dario’s main objective is to “make the invisible visible”. In times where data and big data pressure businesses and academic alike, Dario’s tool is inspiring; it has certainly forced each component of the map to reflect on their own work and behaviour. We can picture some relieved faces of lecturers and researchers (“yes! I have loads of connections!”) and some others a bit more ashamed. The affinity map is based on the process of gathering and meticulous organisation and display (visualisation) of ‘messy data’, coming from people’s work. The map is much closer to reality than the shiny world of Data Science wants us to believe for we deal quotidie with messy data.
The affinity map showcased last night makes us rethink:
One way or another, we tend to be under pressure; we need to collaborate. Before, colaboration would very much be interpreted as weekly meetings with the team at work, monthly meetings with the division and perhaps Christmas parties with the company/institution. Now, collaboration is becoming part of KPIs and most certainly, there is no project and no institution that doesn’t strive for the best possible results through collaboration. We are being asked to collaborate to win prizes, collaboratively review RFPs, submit inter-disciplinary and collaborative research proposals to increase our chances to win etc… Yes, we are under pressure, and changing the habit of ‘pretending to collaborate’ to a habit of ‘true collaboration’ is tough. There is very little knowledge on how to monitor collaboration. Dario’s visualisation, hopefully, will become a useful tool to understand the full extent of collaboration and, also, understand how it also translates into efficiency and competitive advantage. It might sound scary but, willingly or not, we have embraced business logics of efficiency, exposure and global reach.
How can data perform a new reality? This question is far from clear. However, what we see in this very quick and simple video isn’t only the translation of some very complex data into a nice dataviz project; it is also a whole new way of understanding the working of a small world, a lab, an institution or a company. The lack of collaboration or the wealth of networks of collaborations are translated into an algorithm that codifies the institutions’ ‘willingness’ to collaborate. The tool is performed through data; the academics’ were forced to look reflexively into their own practices. The affinity map certainly raises tough questions and it certainly brings up possible discussions on the discursive formation of collaboration and can have unknown -disciplinary- consequences (more policies?).
Networks of academics, their works and their networks of supervisions is a fascinating matter that opens the door to reflections over the impact of such a tool on universities’ research and teaching frameworks and policies, on governance and, most certainly, on the ethics of transposing collaboration and inter-disciplinarity into an eye-catching visualisation. All in all, a pretty awesome job