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

2. 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?

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

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

5. 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)

6. 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)

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

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

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

10. Data sense
data sense
digital sensors
human senses
sense-making

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

12. 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)

13. Participant shows how he gets ready for a ride

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

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

16. 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

17. 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)

18. 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