Plenary presentation at the Electronic Visualisation and the Arts Australasia (EVAA 2016) conference, 6 March 2016

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
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
1. Interview 1 (talk to participant about their
self-tracking and cycling practices)
2. Enactment of participant getting ready for a
ride and finishing a ride
3. Go Pro footage of ride
4. 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