The ‘aesthetic practices’ of data processing and presentation

An important element of the ‘use’ of data in schools are the various forms of data work behind the packaging, processing and presentation of data. Data is not simply taken ‘as is’ and (re)presented in objective, neutral forms. Instead, a large number of decisions are made (both manually and algorithmically) as data is generated, gathered together, processed and made sense of through its ‘analysis’.

Writing about the processing work that underpins the presentation of data on government portals, Helene Ratner and Evelyn Ruppert describe various ‘aesthetic practices’. This is data work that takes place around the collation of different data produced at different sites and its subsequent presentation for public consumption. The notion of aesthetics does not relate to concerns over beauty per se – rather the behind-the-scenes re-arranging and ‘neatening’ of data in a form that can be smoothly processed.

These practices include various data ‘cleaning’ decisions regarding which data are to included and which are to be discarded (what Ratner and Ruppert describe as a process of ‘managing absence, inaccuracy and indeterminacy’). These practices also include various data ‘packaging’ decisions about how to overcome inconsistencies and ‘frictions’ within a dataset – these latter decisions include how best to standardize, classify and label data (Leonelli 2016).  As Ratner and Ruppert contend, these are all normative decisions that result in some data being made absent, and therefore some information remaining undocumented.

As such instances imply, it is important to understand the collation of data-sets and their subsequent processing and presentations as socially-shaped ‘translations’ of data rather than objective reflections. In this manner, any data portal or data visualization might be best approached as a performative ‘site of projection’. The important questions here, then, relate to what is being projected by whom, with what intentions, and with what outcomes? Conversely, what aspects of the data are is not  being projected?



Leonelli, S. (2016). Data-centric biology. Chicago, University of Chicago Press.

Ratner and Ruppert (2019)  Producing and projecting data: aesthetic practices of government data portals.  Big Data & Society [forthcoming]