Big Data and the Quantified Self

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:       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  Personal Data     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   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  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…  Physical Activities   Diet and Nutrition    Location, architecture, weather, noise, pollution, clutter, light, season Situational Variables   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   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  Self-tracking statistics      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    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)     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   Sanctorius of Padua 16th c: energy expenditure in living systems; 30 years of QS weight/food data QS Philosophers    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  Examples    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    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   Data collection, processing, analysis Cloud computing for consumer processing    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  ‘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   Formerly everything signal, now 99% noise Medium of big data opens up new methods:  Exception, characterization, variability, pattern recognition, correlation, prediction, early warnings  Allows attitudinal shift to active from reactive  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   Google: large corpora and simple algorithms Foundational characterization (previously unavailable)  Longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly, and emergent phenomena  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   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     (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

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

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