Data Sense: People’s Engagement with Their Personal Digital Data Deborah Lupton News & Media Research Centre Faculty of Arts & Design University of Canberra

Living Digital Data research program How to people use and conceptualise their personal digital data? What do they know of how their data are used by others? How do they use other people’s data? What are the intersections of lively devices, lively data and human life itself?

Perspectives sociomaterialism critical data studies cultural geography sensory studies digital anthropology digital sociology

The 13 ‘Ps’ of big data Portentous (momentous discourse) Perverse (ambivalence) Personal (about our everyday lives) Productive (generate new knowledges + practices)

The 13 ‘Ps’ of big data Partial (tell a particular narrative, leave stuff out) Practices (involve diverse forms of action) Predictive (used to make inferences)

The 13 ‘Ps’ of big data Political (reproduce power relations + inequalities) Provocative (scandals + controversies) Privacy (how personal data are used/misused) Polyvalent (contextual, many meanings) Polymorphous (materialised in many forms) Playful (can be fun/pleasurable)

The vitality of digital devices lively devices mobile ubiquitous companions co-habitants embodied

The vitality of digital data lively data data about life social lives of data data impacts on life data livelihoods

Data and emotion data pleasure data frustration data betrayal data visceralisation data pleasure data frustration data betrayal data boredom data mystery data fear

Data sense data sense digital sensors human senses sense-making

Cycling Data Assemblages project human bicycle digital device digital data human senses emotion space/place

Data collection for Cycling Data Assemblages Project Interview 1 (talk to participant about their self-tracking and cycling practices) Enactment of participant getting ready for a ride and finishing a ride Go Pro footage of ride Interview 2 (talk to participant about the Go Pro footage and the self-tracked data they collected on their ride)

Participant shows how he gets ready for a ride

Participant shows and talks about his cycling data on his phone app

Participant shows and talks about his cycling data on his computer screen

Preliminary findings Self-tracked data … offer ‘objective’ measures over ‘subjective’ embodied sensations ‘documented proof’ that a ride took place and how long and fast it was can monitor changes in fitness over time can be social can tell you if you are struggling or feeling good need to be assessed against previous experiences

Preliminary findings Self-tracked data … can be motivating – ‘external validation’ knowing speed ‘makes me work harder’ distance travelled ‘gives a sense of achievement’ seeing heart rate ‘tells me how much work I’m doing’ can only tell you so much about a ride (can’t access the ‘internal battles’ or incorporate traffic or weather conditions or impact of different bikes)

Preliminary findings Self-tracked data … value of data can mean less caution about data privacy makes you more aware of parts of the ride (e.g. Strava ‘segments’) assists riding technique (noticing speed, anticipating gear changes) can change the way you feel about your body helps explain why you felt a certain way about a ride reminds you of how you felt during the ride