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Data Sense: People's Engagement with Their Personal Digital Data

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Deborah Lupton
Deborah Lupton

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

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Data Sense: Peoples 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 peoples 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 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 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)
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 Im doing can only tell you so much about a ride (cant 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

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Data Sense: People's Engagement with Their Personal Digital Data

  • 1. Data Sense: Peoples 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 peoples data? What are the intersections of lively devices, lively data and human life itself?
  • 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
  • 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 Im doing can only tell you so much about a ride (cant 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