DATA
David BrooksOp-Ed, February 13th 2013“What Data Can’t Do”
“Big Data has trouble with Big Problems”             - David Brooks
Advertising
Advertising   Prediction
DATA:SCIENCE
Jeff Hammerbacher!   DJ Patil!
Data Science  Kosinski et al PNAS 2013
Liking Curly Frieson Facebook istop predictor ofintelligence
DATA:SCIENCE
DATA:intelligence
Iraq:2007
Open Source Data Collection     War IED
IraqFrequency                            α = 2.31            xmin                   Attack Size
Iraq            PREDICT?Frequency                            α = 2.31            xmin                   Attack Size
Insurgent model: Nature 2009
Impact of increasing troop numbers                              Zhao et al, Phys. Rev. Lett. 2009
DATA:SCIENCE      vs.DATA:intelligence
Data:SCIENCE Data:IntelligenceImprovement       10%              10x
Data:SCIENCE Data:IntelligenceImprovement        10%               10xModel goal    Predict/Optimize   Create/Change
Data:SCIENCE Data:IntelligenceImprovement        10%               10xModel goal    Predict/Optimize   Create/ChangeDecisi...
Data:SCIENCE Data:IntelligenceImprovement        10%               10xModel goal    Predict/Optimize   Create/ChangeDecisi...
Data:SCIENCE Data:IntelligenceImprovement          10%               10xModel goal      Predict/Optimize   Create/ChangeDe...
Data:SCIENCE Data:IntelligenceImprovement          10%               10xModel goal      Predict/Optimize   Create/ChangeDe...
STRATEGIC TOOLKIT Google   +   Excel   + Powerpoint
“What is the most              effective way to allocate              capital to spur growth in                        K-1...
“What are the dominant                   narratives about                 climate change in                              I...
“What are my competitors             doing with advanced                   flexible display                      technology...
“What is the                      structure of the                    insurgent groups                             in Syri...
Five Heuristics          For Using Data to Solve                    Big Problems
1. Data needs to be designedfor human interaction
Human   Machine
Human Centered UI
2. Understand limits ofhuman processing.
900 ms         Source: Nanex
3. Data is messy,incomplete and biased.
…Deal with it…
4. Data needs theory
The future will look like the past…
Build model:Understand
5. Data needs stories…    stories need data
Equa%on	  governing	  Insurgent	  Dynamics	  Mathematical
Myth:Hydra
BIG DATA &BIG PROBLEMS
DATA:intelligence
Human + Machine         Interface
Human + Machine
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013
Upcoming SlideShare
Loading in …5
×

WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013

721
-1

Published on

Presentation from Sean Gourley, Quid
#dataconf
More at http://event.gigaom.com/structuredata/

Published in: Technology
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
721
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
19
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013

  1. 1. DATA
  2. 2. David BrooksOp-Ed, February 13th 2013“What Data Can’t Do”
  3. 3. “Big Data has trouble with Big Problems” - David Brooks
  4. 4. Advertising
  5. 5. Advertising Prediction
  6. 6. DATA:SCIENCE
  7. 7. Jeff Hammerbacher! DJ Patil!
  8. 8. Data Science Kosinski et al PNAS 2013
  9. 9. Liking Curly Frieson Facebook istop predictor ofintelligence
  10. 10. DATA:SCIENCE
  11. 11. DATA:intelligence
  12. 12. Iraq:2007
  13. 13. Open Source Data Collection War IED
  14. 14. IraqFrequency α = 2.31 xmin Attack Size
  15. 15. Iraq PREDICT?Frequency α = 2.31 xmin Attack Size
  16. 16. Insurgent model: Nature 2009
  17. 17. Impact of increasing troop numbers Zhao et al, Phys. Rev. Lett. 2009
  18. 18. DATA:SCIENCE vs.DATA:intelligence
  19. 19. Data:SCIENCE Data:IntelligenceImprovement 10% 10x
  20. 20. Data:SCIENCE Data:IntelligenceImprovement 10% 10xModel goal Predict/Optimize Create/Change
  21. 21. Data:SCIENCE Data:IntelligenceImprovement 10% 10xModel goal Predict/Optimize Create/ChangeDecision Algorithm Human
  22. 22. Data:SCIENCE Data:IntelligenceImprovement 10% 10xModel goal Predict/Optimize Create/ChangeDecision Algorithm HumanData Big/Clean Small/Messy
  23. 23. Data:SCIENCE Data:IntelligenceImprovement 10% 10xModel goal Predict/Optimize Create/ChangeDecision Algorithm HumanData Big/Clean Small/MessyCommunication Equations Stories
  24. 24. Data:SCIENCE Data:IntelligenceImprovement 10% 10xModel goal Predict/Optimize Create/ChangeDecision Algorithm HumanData Big/Clean Small/MessyCommunication Equations StoriesProblem Tactical Strategic
  25. 25. STRATEGIC TOOLKIT Google + Excel + Powerpoint
  26. 26. “What is the most effective way to allocate capital to spur growth in K-12 education technology”!**…and how will I know if we’re successful!
  27. 27. “What are the dominant narratives about climate change in India”!**…and how does it vary between age groups!
  28. 28. “What are my competitors doing with advanced flexible display technology”!**…should I compete or partner with them?!
  29. 29. “What is the structure of the insurgent groups in Syria”!**…what is the likely impact of peace keepers!
  30. 30. Five Heuristics For Using Data to Solve Big Problems
  31. 31. 1. Data needs to be designedfor human interaction
  32. 32. Human Machine
  33. 33. Human Centered UI
  34. 34. 2. Understand limits ofhuman processing.
  35. 35. 900 ms Source: Nanex
  36. 36. 3. Data is messy,incomplete and biased.
  37. 37. …Deal with it…
  38. 38. 4. Data needs theory
  39. 39. The future will look like the past…
  40. 40. Build model:Understand
  41. 41. 5. Data needs stories… stories need data
  42. 42. Equa%on  governing  Insurgent  Dynamics  Mathematical
  43. 43. Myth:Hydra
  44. 44. BIG DATA &BIG PROBLEMS
  45. 45. DATA:intelligence
  46. 46. Human + Machine Interface
  47. 47. Human + Machine
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×