While it might seem unusual to be writing about imagination and imaginaries as part of this project, there are a number of reasons why it is absolutely integral to how data is materialised, and, importantly, how to find new ways to think about digital data. It is hard to pinpoint exactly what imagination means but generally speaking it refers to the creation of new ideas, words and concepts that are not immediately present to the senses. For this reason, it is often seen as antithetical to perception, but there are a number of reasons why this binary construction (i.e. imagination and perception) is problematic, and I discuss this later. In some ways, imagination is almost a kind of folk theory in that there is no direct physical or material manifestation of it, however, it is discussed and spoken as if it commonly agreed upon.
Imaginations and imaginaries are fundamental to our society and in establishing the practices and morals that become widely shared and agreed upon. Charles Taylor’s work on social imaginaries is particularly useful for developing the rather nebulous concept of imagination for the social sciences. He defines social imaginaries as:
…the ways people imagine their social existence, how they fit together with others, how things go on between them and their fellows, the expectations that are normally met, and the deeper normative notions and images that underlie these expectations (Taylor, 2003, p.23).
Given the definition above, it is perhaps not surprising that we are often not consciously aware of the social imaginaries that we draw on to give our lives shape. We are schooled explicitly and implicitly about the knowledge practices, values and beliefs that society holds and these become embedded in our ways of thinking and doing things, to the point that it can become difficult to articulate what they even are.
So what do imaginaries and imagination have to do with data? A key part of our Data Smart Schools project, and in Neil Selwyn and my previous work with teenagers and personal data, has been encouraging participants to explore their imaginations so they can do data differently. However, rather than feeling empowered to work creatively and imaginatively with data, we have found that participants were limited in their capacity to think and engage with data in different ways. In this blog post, I want to extend on this a little further and consider how these findings intersect with materialising data.
How data is materialised is integral to the development of people’s data imaginaries. In its operational state, data is pretty much undecipherable to the human eye – as esoteric as the imagination. Appearing as long strings of numbers, it is difficult for anyone to make any sense of this without it being processed into the more familiar representations of charts and graphs. It is problematic that we do not often think of data in a qualitative sense, but the dominant way of making meaning of data is via the discourse of mathematics, which is not typically thought of as ‘interpretive’. Data is therefore something to ‘get right’ rather than something to interpret in creative and imaginative ways.
This can be traced back to the role of data in cybernetics. I have blogged on some aspects of cybernetics before, but just to recap, it is an approach to automatic control systems, including feedback and black boxes, and is often seen as the precursor to digitalisation and automation. While cyberneticians might see cybernetics as separate system of apparatuses, the French philosophical group Tiqqun (2001) argue that it has become thoroughly blended with capitalism and the politics of everyday life. Cybernetics relies on accurate representation and memory of the past. It is the ‘project of recreating a world within an infinite feedback loop involving these two moments: representation separating, communicating connecting’ (Tiqqun, 2001, p.10). Cybernetics was first introduced as a way of predicting the future and solving the ‘problem’ of uncertainty, particularly during the second world war. In this way, data is Janus-faced – it depicts the past, but only to (re-)orient the future.
Through this lens, a datum is not really a ‘text’ but more like a dot of paint that helps to create a picture. More data produces a more complete picture. For this to work, data must be interpreted in a particular way – to suit the operation of the system and its goals, rather than the whimsy of its subjects. The speed of data circulation is essential to the accuracy of the prediction, hence the importance of ‘real time’ and ‘just in time.’ The closer the data analysis can get to its point of generation, the more accurate it will be. Humans are deliberately locked out of this process – operationally, politically and economically. We are data subjects. It is not surprising then that it is difficult – if not impossible – to imagine it differently. Our data imaginaries are bolted onto an operational system that subjugates our experience rather than placing it at the centre. This is evident even in the more critical and self-reflective uses of data, such as that of the Quantified Self movement.
This is a dangerous position for us to be in. If we do not have the opportunity to even interpret data, it makes it very difficult to imagine it differently. And this is where the materialisation of data becomes key. So too, does coupling imagination and perception, rather than seeing them as binary opposites. How can our perception of data open up new data imaginaries? What opportunities exist to intervene in how data is processed and presented to individuals? Can we interrupt data flows within operational systems and insert our own ways of understanding and categorising information? In past projects our ways of thinking about data differently are always after the fact, or after the data has been processed, so it is not surprising that they have not achieved that much. The most success I have had is materialising data differently for children through the FriendSend app. In this project, data was materialised for educational purposes, and this showed the kids that it was, to an extent, able to be reappropriated and interpreted.
So, at the end of this rather long blog post, I have more questions than answers. But ultimately perhaps the message is, rather than imagining data differently, perhaps the first step is to materialise it differently. Doing so has consequences for the way we view, categorise and interpret information, as well as our relationships with others, and this is may open up the potential for change.
Image: “Artificial Intelligence” from Cybernetics of Cybernetics (1974), University of Illinois Archive.
References:
Taylor, C. (2003). Modern Social Imaginaries. Durham, NC: Duke University Press.
Tiqqun. (2001). The Cybernetic Hypothesis. Cambridge, MA: The MIT Press.
new ways of understanding digital data 