The deck I've pulled together to explain my thought around self tracking and the quantified self movement. For more information, visit the QuantifiedSelf.com home page or the author's blog at www.thatdatathing.com/blog/

1. By: Leon van der Walt
Deriving Meaning From QS Data

2. • Why engage in QS – Just about navel gazing?
– Self Knowledge?
– Memory Augmentation?
– Behaviour Change?
• The QS Data Problem
• From data to meaning in a QS context
– Potential Frameworks
– What is our hypothesis?
• So what?
Contents

3. Why Engage in QS?

4. Self Knowledge?
•Source:
•http://en.wikipedia.org/wiki/Johari_window

5. Memory Augmentation?
•Source:
•http://en.wikipedia.org/wiki/Memory
•http://en.wikipedia.org/wiki/Visuospatial_Sketchpad
Words, Emotions &
Meaning?

6. Behavioural Change?
* Source: http://www.BehaviourModel.org

7. Behavioural Change?
Increase Ability /
Simplify
Time
Money
Effort
Social Deviance
Provide Triggers
Facilitator
Spark
Signal
Increase
Motivation
Pain / Pleasure
Hope / Fear
Social Acceptance / Rejection
* Source: http://www.BehaviourModel.org

8. Example: Memoto Camera
•Source:
•http://blog.memoto.com/2013/07/the-memoto-lifelogging-camera-tested-and-
proven/

9. The Problem
Disparate data sources in a variety of
formats, time periods, measuring
different attributes, passive / active,
ownership, ...

10. Internet of Things
* Source: FirstMark Capital

11. Devices

12. Understanding measurement

13. Understanding measurement
•Source:
•http://well.blogs.nytimes.com/projects/2014/03/accelerometers.html

14. Potential Frameworks

15. Contextual Data Generation
GPS
Base Station
Photo
Temperature
SMS
Phone Call
Screen status
Magnetic
Compass
Acceleromete
r
Bluetooth
WiFi
Location contexts
Activity context
Time context
Environment context
Social context
Location
Weather service
Activities
Time
Relationship
Environment
Physical sensors Virtual sensors Semantic Context
* Source: SenseSeer - http://doras.dcu.ie/16881/
Emotional /
Psychological
Context?

16. * Source: Storica

17. * Source: DataShaka White Paper: Solving the Variety Problem: Unlocking Big Data Insight Through Unification

18. What Is Our Hypothesis?
Falsifiable
“A hypothesis must also be falsifiable. That is, there must be a possible negative answer.
For example, if I hypothesize that all green apples are sour, tasting one that is sweet will
falsify the hypothesis.”
* Source: The Process of Science - NCSU
Verifiable
“To meet the standards of science, a hypothesis must be verifiable Some potential
hypotheses clearly cannot be tested. For example, I might propose that my colds are the
result of someone practicing voodoo on me. Science can only measure that which is part of
nature, so supernatural phenomena could never be studied or predicted by scientific
methodology.

19. So What?

20. * Source: http://www.slideshare.net/erik.duval/20130703-lasi-stanforderik
"We shall not cease from
exploration. And the end of all
our exploring will be to arrive
where we started and know the
place for the first time."
- TS Eliot

21. "If you can not measure it, you can
not improve it.“
– Lord Kelvin
“Statistics are like a bikini. What
they reveal is interesting. But what
they hide is vital.”
– Aaron Levenstein
(Famous) Last Thoughts
* Source: XKCD

22. • Visit Quantified Self home page:
www.quantifiedself.com
• Author’s blog: www.thatdatathing.com/blog/
Further information