Data Sense: People’s Engagement with Their Personal

Digital Data

Deborah LuptonNews & Media Research Centre

Faculty of Arts & DesignUniversity 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 devicesmobile

ubiquitous

companions

co-habitants

embodied

The vitality of digital data

lively data

data about life

social lives of data

data impacts on life

data livelihood

s

Data and emotion

data visceralisation

data pleasure

data frustratio

n

data betrayal

data boredom

data mystery

data fear

Data sense

data sense

digital sensor

s

human senses

sense-making

Cycling Data Assemblages project

human

bicycle

digital device

digital datahuman senses

emotion

space/place

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 ride4. 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