0
DATA ANDDISILLUSIONMENTSOLVEforINTERESTINGOTHERWISE LIFE IS DULL.
(Shamelessly: buy this book.)
“We gotthe Internetexactlybackwards.”
http://www.flickr.com/photos/jenny-pics/3239638494/sizes/l/                            Breadcrumb trail
Big Data:It’s people.
A technology shift.
Volume              (the “big”                 part)              Pick              any Velocity              two         ...
Relational             BIG                   Statistical
“All your friends are poor” isan awkward conversation.
Ward off disease.            Pinpoint disasters.A force     Reveal corruption.for good.            Make cities smarter.    ...
Big healthcare
Big philanthropy
Big commuting
Erode our privacy.           Justify prejudices.A force    Polarize groups.for bad.           Leak private truths.
Big prejudice
Audience participation time!
How amusing.
“…nobody notices offers they do notget. And if these absent opportunitiesstart following certain social patterns(for exampl...
Personalization looks a lot     like prejudice.
Big radio
Times a song in “heavy rotation”is played each day30                           Every 55m15        Every 4h 0          2007...
Do   Ev n’t  ha   en fee     d a Ein l ba         the stein d.            rap                ist                    .Don’t...
24 months ago, the averageperson was still afraid of IT.
Today, the average person isterrified of being without it.
So we have a lot of data. Now we’re a smarter    species, right?
“Anyone whovalues truthshould stopworshippingreason.”    (AKA the real world.)
We prefer false positives.
Wooly mammothhttp://www.flickr.com/photos/pong/172438102/sizes/o/
Sun templehttp://www.flickr.com/photos/30787002@N02/3298693694/sizes/l/
Polarizing through tone
Pew and political  polarization
We’re bad at this Mistake correlation for causality Seek truthiness rather than fact Find patterns where they don’t exist ...
Athenian swimming pools
What will be normal   tomorrow.
“All truth passes through threestages. First it is ridiculed.Second, it is violentlyopposed. Third, it is acceptedas being...
Saturday morning    cartoons
Saturday morning    cartoons
Saturday morning    cartoons
Saturday morning    cartoons
Saturday morning    cartoons
Our rotation about the sunThe immorality of slaveryA woman’s right to vote               ... were once heresy.
Four big bets.
23andme
This explains so much.
How long until it’s cruel not  to scan your baby?
Minority report
How long until it’s unethical  not to predict mass        murder?
Look at my feed, yemighty, and despair.
How long until our feed isn’t amatter of record like our Social     Insurance Number?
Google Glass and prosthetic          brains
How unfair will it be?
How long until we have aprosthetic brain from birth?
What if, tomorrow,
genetic mapping
predictive arrests
a state-sanctioned life feed
and birth-issued  prosthetics
aren’t just normal...
...it’s immoral not to have           them?
(Phew.)
“A subjective degree of belief should rationally change to   account for evidence.”                    (AKA Bayes’ Theorem.)
Photo by Jeff Pang on Flickr. http://www.flickr.com/photos/jeffpang/3165283767/expectations.  Pretty high
Are they being met?
What would be a perfectindustry to capitalize on       Big Data?
Tons of information.
Public and private.
Data collection is inherent.
What’s collected identifies   people uniquely.
Structured and unstructured.
Ubiquitous and mobile.
Consumer-facing, tied to loyalty.
Enabled by sensors  and interfaces.
The best test:  An industry where “the right information in the right place    just changes your life.”                 (w...
Photo by Garysan97 on Flickr. http://www.flickr.com/photos/16983197@N06/7808610268/
The travel industry is theposter child for Big Data       innovation.
(Show of hands?)
Photo by James Vaughan on Flickr (http://www.flickr.com/photos/x-ray_delta_one/4567365854/)
http://www.flickr.com/photos/sodaniechea/7418759618/Welcome to LA.
Instead: have a free room!
(Admittedly, these are first-world problems.)
Is this a lackof data?
No, lack of outcomes.
Change is hard.(habits don’t change easily)
“Most organizational changeefforts still underperform, fail,   or make things worse.”   Walter McFarland, This is your bra...
“A person’s reaction to organizational change ‘can be soexcessive and immediate, that someresearchers have suggested it ma...
Disillusioned?
Maybe disruption requires having nothing to lose?
Amazon & e-books.
Netflix & videos.
Paypal & online payment.
Über & taxi services         Not a car service.A supply chain optimization platform.
Tomorrow’s best ideas are obvious in hindsight.
But to create them,companies need to change radically.
In fact, they need to change     how they change.
Legacycompanieshave all thecards. http://www.flickr.com/photos/locosphotos/6608106173/
Photo by Paul Falardau on Flickr (http://www.flickr.com/photos/pfala/4189061616/)                                          ...
Alistair Croll                          @acroll                          www.solveforinteresting.comTHANKS!               ...
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
Infopresse montreal feb 6   big data
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Transcript of "Infopresse montreal feb 6 big data"

  1. 1. DATA ANDDISILLUSIONMENTSOLVEforINTERESTINGOTHERWISE LIFE IS DULL.
  2. 2. (Shamelessly: buy this book.)
  3. 3. “We gotthe Internetexactlybackwards.”
  4. 4. http://www.flickr.com/photos/jenny-pics/3239638494/sizes/l/ Breadcrumb trail
  5. 5. Big Data:It’s people.
  6. 6. A technology shift.
  7. 7. Volume (the “big” part) Pick any Velocity two Variety(the “fast” (the part) “anything” part)
  8. 8. Relational BIG Statistical
  9. 9. “All your friends are poor” isan awkward conversation.
  10. 10. Ward off disease. Pinpoint disasters.A force Reveal corruption.for good. Make cities smarter. Improve how we teach.
  11. 11. Big healthcare
  12. 12. Big philanthropy
  13. 13. Big commuting
  14. 14. Erode our privacy. Justify prejudices.A force Polarize groups.for bad. Leak private truths.
  15. 15. Big prejudice
  16. 16. Audience participation time!
  17. 17. How amusing.
  18. 18. “…nobody notices offers they do notget. And if these absent opportunitiesstart following certain social patterns(for example not offering them tocertain races, genders or sexualpreferences) they can have a deep civilrights effect.” Anders Sandberg, Oxford University
  19. 19. Personalization looks a lot like prejudice.
  20. 20. Big radio
  21. 21. Times a song in “heavy rotation”is played each day30 Every 55m15 Every 4h 0 2007 2012
  22. 22. Do Ev n’t ha en fee d a Ein l ba the stein d. rap ist .Don’t feel bad.
  23. 23. 24 months ago, the averageperson was still afraid of IT.
  24. 24. Today, the average person isterrified of being without it.
  25. 25. So we have a lot of data. Now we’re a smarter species, right?
  26. 26. “Anyone whovalues truthshould stopworshippingreason.” (AKA the real world.)
  27. 27. We prefer false positives.
  28. 28. Wooly mammothhttp://www.flickr.com/photos/pong/172438102/sizes/o/
  29. 29. Sun templehttp://www.flickr.com/photos/30787002@N02/3298693694/sizes/l/
  30. 30. Polarizing through tone
  31. 31. Pew and political polarization
  32. 32. We’re bad at this Mistake correlation for causality Seek truthiness rather than fact Find patterns where they don’t exist Easily swayed by tone Side with our tribes Dig in and ignore new evidence
  33. 33. Athenian swimming pools
  34. 34. What will be normal tomorrow.
  35. 35. “All truth passes through threestages. First it is ridiculed.Second, it is violentlyopposed. Third, it is acceptedas being self-evident.” Arthur Schopenhauer, philosopher (1788-1860)
  36. 36. Saturday morning cartoons
  37. 37. Saturday morning cartoons
  38. 38. Saturday morning cartoons
  39. 39. Saturday morning cartoons
  40. 40. Saturday morning cartoons
  41. 41. Our rotation about the sunThe immorality of slaveryA woman’s right to vote ... were once heresy.
  42. 42. Four big bets.
  43. 43. 23andme
  44. 44. This explains so much.
  45. 45. How long until it’s cruel not to scan your baby?
  46. 46. Minority report
  47. 47. How long until it’s unethical not to predict mass murder?
  48. 48. Look at my feed, yemighty, and despair.
  49. 49. How long until our feed isn’t amatter of record like our Social Insurance Number?
  50. 50. Google Glass and prosthetic brains
  51. 51. How unfair will it be?
  52. 52. How long until we have aprosthetic brain from birth?
  53. 53. What if, tomorrow,
  54. 54. genetic mapping
  55. 55. predictive arrests
  56. 56. a state-sanctioned life feed
  57. 57. and birth-issued prosthetics
  58. 58. aren’t just normal...
  59. 59. ...it’s immoral not to have them?
  60. 60. (Phew.)
  61. 61. “A subjective degree of belief should rationally change to account for evidence.” (AKA Bayes’ Theorem.)
  62. 62. Photo by Jeff Pang on Flickr. http://www.flickr.com/photos/jeffpang/3165283767/expectations. Pretty high
  63. 63. Are they being met?
  64. 64. What would be a perfectindustry to capitalize on Big Data?
  65. 65. Tons of information.
  66. 66. Public and private.
  67. 67. Data collection is inherent.
  68. 68. What’s collected identifies people uniquely.
  69. 69. Structured and unstructured.
  70. 70. Ubiquitous and mobile.
  71. 71. Consumer-facing, tied to loyalty.
  72. 72. Enabled by sensors and interfaces.
  73. 73. The best test: An industry where “the right information in the right place just changes your life.” (which was what Stewart Brand said)
  74. 74. Photo by Garysan97 on Flickr. http://www.flickr.com/photos/16983197@N06/7808610268/
  75. 75. The travel industry is theposter child for Big Data innovation.
  76. 76. (Show of hands?)
  77. 77. Photo by James Vaughan on Flickr (http://www.flickr.com/photos/x-ray_delta_one/4567365854/)
  78. 78. http://www.flickr.com/photos/sodaniechea/7418759618/Welcome to LA.
  79. 79. Instead: have a free room!
  80. 80. (Admittedly, these are first-world problems.)
  81. 81. Is this a lackof data?
  82. 82. No, lack of outcomes.
  83. 83. Change is hard.(habits don’t change easily)
  84. 84. “Most organizational changeefforts still underperform, fail, or make things worse.” Walter McFarland, This is your brain on organizatinal change, October, 2012, Harvard Business Review
  85. 85. “A person’s reaction to organizational change ‘can be soexcessive and immediate, that someresearchers have suggested it may be easier to start a completely new organization than to try to change an existing one.’” Kenneth Thompson and Fred Luthans
  86. 86. Disillusioned?
  87. 87. Maybe disruption requires having nothing to lose?
  88. 88. Amazon & e-books.
  89. 89. Netflix & videos.
  90. 90. Paypal & online payment.
  91. 91. Über & taxi services Not a car service.A supply chain optimization platform.
  92. 92. Tomorrow’s best ideas are obvious in hindsight.
  93. 93. But to create them,companies need to change radically.
  94. 94. In fact, they need to change how they change.
  95. 95. Legacycompanieshave all thecards. http://www.flickr.com/photos/locosphotos/6608106173/
  96. 96. Photo by Paul Falardau on Flickr (http://www.flickr.com/photos/pfala/4189061616/) how to play them. They just don’t know
  97. 97. Alistair Croll @acroll www.solveforinteresting.comTHANKS! alistair@solveforinteresting.comSOLVEforINTERESTINGOTHERWISE LIFE IS DULL.
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