The document discusses the concept of the 'quantified self,' which refers to individuals engaging in self-tracking of biological and behavioral data through technology. It outlines the evolution and significance of big data in personal health, including various measurement strategies and the integration of wearable devices, while highlighting a growing global community focused on personal knowledge through data collection. Additionally, it explores implications for health care, data privacy, and the increasing relevance of big data in understanding human biology.

1. Big Data and the
Quantified Self
October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga
Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe

2. About Melanie Swan





Founder DIYgenomics, science and
technology innovator and philosopher
Singularity University Instructor, IEET
Affiliate Scholar, EDGE Contributor
Education: MBA Finance, Wharton; BA
French/Economics, Georgetown Univ
Work experience: Fidelity, JP Morgan, iPass,
RHK/Ovum, Arthur Andersen
Sample publications:
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Swan, M. Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public
Health Research Ecosystem. J Med Internet Res 2012, Mar;14(2):e46.
Swan, M. Scaling crowdsourced health studies: the emergence of a new form of contract research organization.
Personalized Medicine 2012, Mar;9(2):223-234.
Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704.
Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced
preventive medicine research. J Participat Med 2010, Dec 23; 2:e20.
Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010,
May;12(5):279-88.
Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized
medicine and quantified self-tracking. Int J Environ Res Public Health 2009, 2, 492-525.
October 28, 2013
QS Big Data
Source: http://melanieswan.com/publications.htm
2

3. Conceptualizing Big Data Categories
Personal Data
Tension: Individual vs Institution
Group Data
Sense of data belonging to a group
October 28, 2013
QS Big Data
3

4. Agenda
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Personal Data
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

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Group Data


Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data
Conclusion
October 28, 2013
QS Big Data
4

5. What is the Quantified Self?

Individual engaged in the selftracking of any kind of biological,
physical, behavioral, or
environmental information

Data acquisition through
technology: wearable sensors,
mobile apps, software interfaces,
and online communities

Proactive stance: obtain and act
on information
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
5

6. QS Sensor Mania! Wearable Electronics
Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre)
Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API
(Sano Intelligence), Continuous Monitors (Medtronic)
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.
6

7. Wearable Personal Information Ecosystem
Smart Gadgetry Creates Continuous Personal Information Climate
New Wearable Categories:
Smartwatch and AR/Glass
Smartphone
PC/Tablet/Cloud
October 28, 2013
QS Big Data
AR = Augmented Reality
7

8. Next-gen Mini: BioSensor Electronic Tattoos
Wearable Electronics: Detect External BioChemical
Threats and Track Internal Vital Signs
Electrochemical Sensors
Chemical Sensors
October 28, 2013
QS Big Data
Disposable Electronics
Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos
Tactile Intelligence:
Haptic Data Glove
8

9. Quantified Self Worldwide Community
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
Goal: personalized knowledge through
quantified self-tracking
‘Show n tell’ meetups

What did you do? How did you do it? What
did you learn?
Videos, Conferences, Meetup Groups
October 28, 2013
QS Big Data
Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.
9

10. October 28, 2013
QS Big Data
Source: http://www.meetup.com/Quantified-Self-Biohacking-Finland/
10

11. Quantified Self Project Examples

Food consumption (1 yr)1 and the Butter Mind study2
Study
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Low-cost home-administered blood, urine, saliva tests

Cholestech LDX
home cholesterol test
October 28, 2013
QS Big Data
1
2
OrSense continuous non-invasive
glucose monitoring
Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized
Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment
ZRT Labs dried
blood spot tests
11

12. Quantified Self Measurements…
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Physical Activities
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Diet and Nutrition
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Location, architecture, weather, noise, pollution, clutter, light, season
Situational Variables
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Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence
IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,
verbal fluency, patience, creativity, reasoning, psychomotor vigilance
Environmental Variables
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Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,
satiety, portions, supplement doses, tastiness, cost, location
Psychological, Mental, and Cognitive States and Traits


Miles, steps, calories, repetitions, sets, METs1
Context, situation, gratification of situation, time of day, day of week
Social Variables

October 28, 2013
QS Big Data
Influence, trust, charisma, karma, current role/status in the group or social network
METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-thatperfect-quantified-self-app-notes-to-developers-and-qs-community-html/
1
12

13. The Quantified Self is Mainstream
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Self-tracking statistics
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60% US adults track weight, diet, or exercise
33% US adults monitor blood sugar, blood pressure,
headaches, or sleep patterns
9% receive text message health alerts
40,000 smartphone health applications
QS thought leadership
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Press : BBC, Forbes, and Vanity Fair
Electronics show focus at CES 2013
Health 2.0: “500+ companies making
self-management tools; VC funding up 20%”
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
13

14. Hype Curves per Google Trends
2011
October 28, 2013
QS Big Data
2013
2011
2013
14

15. QS Experimentation Motivation and Features

DIYgenomics QS Study (n=37)

Desired outcome: optimality and
improvement (vs pathology resolution)
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Personalized intervention for depression,
low energy, sleep quality, productivity, and
cognitive alertness
Rapid experimental iteration through
solutions and kinds of solutions
Resolution point found within weeks
Pragmatic problem-solving focus, little
introspection
October 28, 2013
QS Big Data
Source: DIYgenomics Knowledge Generation through Self-Experimentation Study
http://genomera.com/studies/knowledge-generation-through-self-experimentation
15

16. History of the Quantified Self
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Sanctorius of Padua 16th c: energy
expenditure in living systems; 30
years of QS weight/food data
QS Philosophers


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Epicureans, Heidegger, Foucault): ‘care
of the self’
‘Self’: recent concept of modernity
QS: contemporary formalization using
measurement, science, and
technology to bring order and control
to the natural world, including the
human body
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
16

17. Sensor Mania!
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the
Quantified Self 2.0. J Sens Actuator Netw 2012.
17

18. Wireless Internet-of-Things (IOT)
Image credit: Cisco
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0.
J Sens Actuator Netw 2012.
18

19. 6 bn Current IOT devices to double by 2016
October 28, 2013
QS Big Data
Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T
19

20. IOT World of Smart Matter

IOT Definition: digital networks of
physical objects linked by the Internet
that interact through web services

Usual gadgetry (e.g.; smartphones,
tablets) and now everyday objects:
cars, food, clothing, appliances,
materials, parts, buildings, roads

Embedded microprocessors in 5%
human-constructed objects (2012)1
October 28, 2013
QS Big Data
Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012.
http://singularitysummit.com/schedule
1
20

21. IOT Contributing to Explosion of Big Data

Big Data: data sets too large and
complex to process with on-hand
database management tools
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Examples
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Walmart : 1 million transactions/hr
transmitted to 3 PB database
BBC: 7 PB video served/month from
100 PB physical disk space
Structured and unstructured data
(not pre-defined)
October 28, 2013
QS Big Data
Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
21

22. Defining Trend of Current Era: Big Data
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Annual data creation on the order of zetabytes
90% of the world’s data created in the last 2 years
Fastest growing segment: human biology-related data
2 year doubling cycle
October 28, 2013
QS Big Data
Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends
http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf
22

23. QS is inherently a Big Data problem
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Data collection, processing, analysis
Cloud computing for consumer processing
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Local computing tools are not available to store,
query, and manipulate QS data sets
Cloud-based analysis: Predictive modeling,
natural-language processing, machine learning
algorithms over very-large data sets of
heterogeneous data
Rapid growth in QS data sets


Manually-tracked ‘small data’ is now
automatically-collected ‘big data’
Examples: heart rate monitor data - 250
samples/second (9 GB/person/month);
personal health ‘omics’ files
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
23

24. QS Big Data: Personal Health ‘Omics’
DNA:
SNP mutations
DNA: Structural
variation
RNA expression
profiling
Health 2.0:
Personal Health
Informatics
Proteomics
Microbiomics
Epigenetics
Metabolomics
October 28, 2013
QS Big Data
Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012
Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.
24

25. Big Data: Integrated QS Data Streams
Omics Data Streams
Genome
SNP mutations
Structural variation
Epigenetics
Microbiome
Traditional Data Streams
Personal and Family
Health History
Proteome
Self-reported data:
health, exercise,
food, mood
journals, etc.
Prescription History
Transcriptome
Metabolome
Quantified Self Data
Streams
Mobile App Data
Lab Tests: History
and Current
Demographic Data
Quantified Self
Device Data
Standardized
Instrument Response
Biosensor Data
Objective Metrics
Diseasome
Environmentome
October 28, 2013
QS Big Data
Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory
Biocitizen. J Pers Med 2012, 2(3), 93-118.
Legend: Consumer-available
25

26. APIs and Multi-QS Data Stream Integration
October 28, 2013
QS Big Data
26

27. Fluxstream Unified QS Dashboard
October 28, 2013
QS Big Data
Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/
27

28. Sen.se Integrated QS Dashboard
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‘Mulitviz’ display: investigate correlation between coffee
consumption, social interaction, and mood
October 28, 2013
QS Big Data
Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-intosomething-useable-and
28

29. Wholly different concept and relation to data

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Formerly everything signal, now 99% noise
Medium of big data opens up new methods:

Exception, characterization, variability, pattern recognition,
correlation, prediction, early warnings
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Allows attitudinal shift to active from reactive
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Two-way communication: translate biometric variability in the
personal informatics climate to real-time recommendations
Example: degradation in sleep quality and hemoglobin A1C levels
predict diabetes onset by 10 years1

October 28, 2013
QS Big Data
Source: Heianza et al. High normal HbA(1c) levels were associated with
impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
1
29

30. Big Data opens up new Methods
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
Google: large corpora and simple algorithms
Foundational characterization (previously unavailable)
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Longitudinal baseline measures of internal and external daily
rhythms, normal deviation patterns, contingency adjustments,
anomaly, and emergent phenomena
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New kinds of Pattern Recognition (different structures)

Analyze data in multiple paradigms: time, frequency, episode, cycle,
and systemic variables
New trends, cyclicality, episodic triggers, and other elements that
are not clear in traditional time-linear data


Multi-disciplinarity

Turbulence, topology, chaos, complexity, etc. models
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
30

31. Opportunity: QS Data Commons

Common repository for personal informatics
data streams
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Fitbit, Jawbone UP, Nike, Withings, myZeo,
23andMe, Glass, Pebble, Basis, BodyMedia
Architecting consumer-friendly models

Open-access databases, developer APIs, frontend web services and mobile apps

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(Precedent: public genotype/phenotype data)
Accommodate multi-tier privacy standards
Ecosystem value propositions: service providers,
research community, biometric data-owners
Role of public and private service providers
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
31

32. Github: de facto
QS Data
Commons
October 28, 2013
QS Big Data
Source: https://github.com/beaugunderson/genome
32

33. QS Frontier: Mental Performance Optimization
Mood Management Apps from
Mobilyze and M. Morris
PTSD App
‘Siri 2.0’ Personal Virtual Coach
from DIYgenomics
Source:
http://www.ptsd.va.gov/pu
blic/pages/ptsdcoach.asp
Sources: http://cbits.northwestern.edu and
http://quantifiedself.com/2009/03/a-few-weeks-ago-i
October 28, 2013
QS Big Data
Source: DIYgenomics Social Intelligence Study
http://diygenomics.pbworks.com/w/page/48946791/social_intelligence
33

34. Next-gen QS Services: Quality of Life
QS Aspiration Apps:
Happiness, Emotive
State (personal and
group), Well-being,
Goal Achievement
October 28, 2013
QS Big Data
Category and Name
Website URL
Happiness Tracking
Track Your Happiness
http://www.trackyourhappiness.org/
Mappiness
http://www.mappiness.org.uk/
The H(app)athon Project
http://www.happathon.com/
MoodPanda
http://moodpanda.com/
TechurSelf
http://www.techurself.com/urwell
Emotion Tracking and Sharing
Gotta Feeling
http://gottafeeling.com/
Emotish
http://emotish.com/
Feelytics
http://feelytics.me/
Expereal
http://expereal.com/
Population-level Emotion Barometers
We Feel Fine
http://wefeelfine.org/
moodmap
http://themoodmap.co.uk/
Pulse of the Nation
http://www.ccs.neu.edu/home/amislove/twittermood/
Twitter Mood Map
http://www.newscientist.com/blogs/onepercent/2011/09/twitt
er-reveals-the-worlds-emo-1.html
Wisdom 2.0
http://wisdom2summit.com/
Personal Wellbeing Platforms
GravityEight
http://www.gravityeight.com/
MindBloom
https://www.mindbloom.com/
Get Some Headspace
http://www.getsomeheadspace.com/
Curious
http://wearecurio.us/
uGooder
http://www.ugooder.com/
Goal Achievement Platforms
uMotif
http://www.uMotif.com/
DidThis
http://blog.didthis.com/
Schemer
https://www.schemer.com/ (personalized recommendations)
Pledge/Incentive-Based Goal Achievement Platforms
GymPact
http://www.gym-pact.com/
Stick
http://www.stickk.com/
Beeminder
https://www.beeminder.com/
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
34

35. Next-gen QS Services: Behavior Change
October 28, 2013
QS Big Data
Source: http://askmeevery.com/
35

36. Next-gen QS Services: Behavior Change


Shikake: Sensors embedded
in physical objects to trigger
a physical or psychological
behavior change
Examples:




Transparent trash cans
Trash cans playing an
appreciative sound to
encourage litter to be deposited
Stairs light up on approach
Appreciative ping/noise from
QS gadgetry
October 28, 2013
QS Big Data
Source: http://mtmr.jp/en/papers/taai2013v2.pdf
36

37. Next-gen QS Services: 3D Quantification
BodyMetrics and Poikos:
Fitness and Clothing
Customization Apps
OMsignal: Smart Apparel
24/7 Biometric Monitoring
October 28, 2013
QS Big Data
37

38. Continuous Information Climate

Fourth-person perspective: Immersed in infinite data
flow, we shed bits of information to the data flow, the
data flow responds by sending information to us
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
38

39. Building Exosenses for the Qualified Self
Extending our senses in new ways to perceive data as sensation
Magnetic Sense: Finger and Arm Magnets
North Paw Haptic Compass Anklet and Heart Spark
http://www.youtube.com/watch?v=D4shfNufqSg
http://sensebridge.net/projects/heart-spark
October 28, 2013
QS Big Data
Serendipitous Joy: Smiletriggered EMG muscle sensor
with an LED headband display
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.
39

40. Exosenses as Quantified Intermediates




Networked quantified intermediates for
human senses: smarter, visible, sharable
through big data processing
Vague sense of heart rate variability, blood
pressure; haptically-available exosenses
make the data explicit
Haptics, audio, visual, taste, olfactory
mechanisms to make metrics explicit: heart
rate variability, blood pressure, galvanic skin
response, stress level
Skill as exosense: technology as memory,
self-experimentation as a form of exosense
October 28, 2013
QS Big Data
Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html
Nose-on-a-chip
Gut-on-a-chip
Lung-on-a-chip
40

41. Neural Tracking: QS Big Data Frontier
24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses
Consumer EEG Rigs
Augmented Reality Glasses
1.0
2.0
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens
Actuator Netw 2012.
41

42. QS Big Data: Biocitizen Volition
1. Continuous health information climate
Automated digital health monitoring, self-tracking devices,
and mobile apps providing personalized recommendations
2. Peer collaboration and
health advisors
Individual
Health social networks, crowdsourced
studies, health advisors, wellness
coaches, preventive care plans,
boutique physicians, genetics coaches,
aestheticians, medical tourism
3. Public health system
Deep expertise of traditional health system
for disease and trauma treatment
October 28, 2013
QS Big Data
Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer
personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.
42

43. Conceptualizing Big Data Categories
Personal Data
Group Data
October 28, 2013
QS Big Data
43

44. Agenda

Personal Data




Group Data


Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data
Conclusion
October 28, 2013
QS Big Data
44

45. Group Data: Smart City, Future City
October 28, 2013
QS Big Data
Image: http://www.sydmead.com
45

46. Global Population: Growing and Aging
October 28, 2013
QS Big Data
Source: UN Habitat – 2010
http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html
46

47. 3 billion new Internet users by 2020
October 28, 2013
QS Big Data
Source: Peter Diamandis Singularity University
47

48. Human Urbanization: Living in Cities



Over 50% worldwide population in 2008
5 billion in 2030 (estimated)
Megacity: (>10 million and possibly 2,000/km 2)
October 28, 2013
QS Big Data
48

49. Megacity
Growth
Rates
October 28, 2013
QS Big Data
Source: Wikipedia
49

50. Big Urban Data: Killer Apps


Adaptive lighting, smart waste, pest control, hygiene
management, eTolls, public transportation, traffic management,
smart grid, asset tracking, parking
Flexible services responding in real-time to individual and
community-level demand
October 28, 2013
QS Big Data
Source: MIT Senseable City Lab
50

51. Data Signature of Humanity
MIT SENSEable City Lab – the Real-Time City
October 28, 2013
QS Big Data
Source: http://senseable.mit.edu/signature-of-humanity/
51

52. 3D Buildings + Population Density
October 28, 2013
QS Big Data
Source: ViziCities
52

53. 3D Tweet Landscape
October 28, 2013
QS Big Data
Source: http://vimeo.com/67872925
http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl
53

54. 3D Urban Data Viz: Decision-making Tool
October 28, 2013
QS Big Data
Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/
54

55. Group Data: Office Building Community
October 28, 2013
QS Big Data
Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010
55

56. Big Data 3D Printed Dwellings of the Future
Living Treehouses – Mitchell Joachim
Masdar, Abu Dhabi – Energy City of the Future
October 28, 2013
QS Big Data
Himalayas Water Tower

57. Urban Agriculture: Vertical Farms
San Diego, California
(planned)
October 28, 2013
QS Big Data
Singapore (existing)
57

58. Reconfiguration of Space: Seasteading
October 28, 2013
QS Big Data

59. Transportation Revolution
Solar Power: Tesla + Solar City
Personalized Pod Transport
October 28, 2013
QS Big Data
Self-Driving Car
Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commuting
http://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/
59

60. Crowdsourcing
October 28, 2013
QS Big Data
Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw
60

61. Pervasiveness of Crowd Models

Crowdsourcing: coordination of large numbers of
individuals (the crowd) through an open call on the
Internet in the conduct of some sort of activity







Economics: crowdsourced labor marketplaces, crowdfunding,
grouppurchasing, data competition (Kaggle)
Politics: flashmobs, organizing, opinion-shifting, data-mining
Social: blogs, social networks, meetup, online dating
Art & Entertainment: virtual reality, multiplayer games
Education: MOOCs (massively open online courses)
Health: health social networks, digital health experimentation
communities, quantified self
Digital public goods: Wikipedia, online health databanks, data
commons resources, crowdscience competitions
October 28, 2013
QS Big Data
61

62. Genomera – Crowdsourced Study Platform
October 28, 2013
QS Big Data
Source: http://genomera.com/studies/dopamine-genes-and-rapid-realityadaptation-in-thinking
62

63. Agenda

Personal Data




Group Data


Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data
Conclusion
October 28, 2013
QS Big Data
63

64. But wait…Limitations and Risks


Transition to access not ownership models
Data rights and responsibilities


Regulatory and policy tensions





Personal data and group data
Surveillance (top-down) vs souveillance (bottom-up)
Multi-tier privacy and sharing preferences
Digital divide accessibility, non-discrimination
Precedent = Uninformed Consumer: Lack of access
conferred (e.g.; health data, genomics, credit scoring)
Consumer non-adoption, ease-of-use, social
acceptance, meaningful value propositions
October 28, 2013
QS Big Data
64

65. Proliferation of New QS Big Data Flows

QS Device Data




Personal IOT Data



Cell phone, wearable electronics data
Smartphone digital identity & payment
Personal Urban Data



Biometric data (HRM), personal genomic data
Personal medical and health data
QS neural-tracking eye-tracking affect data
Smart home, smart car
Smart city data (e.g.; transportation)
Personal Robotics Data
October 28, 2013
QS Big Data
65

66. Top 10 QS Big Data Trends
Personal Data
Group Data
QS Device Ecosystem
Internet-of-Things (IOT)
Sensor Networks
3D Information
Visualization
Wearable Electronics
Smart City
Future City
Megacity
Growth
Urban Data
October 28, 2013
QS Big Data
Biocitizen
Self-Empowerment
DIY Attitude
Crowdsourcing
3 billion New
People Online
66

67. Heidegger and Big Data

Technology is not good or bad in
itself, technology is an enabler, not a
means to an end (Kant: end not
means)

Our attunement to the background
of technology as a capacity for
revealing the world could help us
away from our lostness in daily
projects to see the possibilities for
the true meaningfulness of our being
October 28, 2013
QS Big Data
Source: Heidegger, M. The Question Concerning Technology, 1954
67

68. QS Big Data Summary

Next-gen QS services




IOT continuous personal information climates
QS Big Data



Wholly different relation to data: 99% noise
Rights and responsibilities model of data access
Group Data


Wearable Electronics as the QS platform
Improve quality of life, facilitate behavior change
Megacity growth, urban data flow, 3 bn coming online
Personal Data

Technology-enabled biocitizen-consumer takes action
October 28, 2013
QS Big Data
68

69. Big Data and the
Quantified Self
kittos!
Questions?
October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga
Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe