Infopresse montreal feb 6   big data
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Infopresse montreal feb 6 big data

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Infopresse montreal feb 6   big data Infopresse montreal feb 6 big data Presentation Transcript

  • 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 Variety(the “fast” (the part) “anything” part)
  • 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. Improve how we teach.
  • 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 example not offering them tocertain races, genders or sexualpreferences) they can have a deep civilrights effect.” Anders Sandberg, Oxford University
  • Personalization looks a lot like prejudice.
  • Big radio
  • Times a song in “heavy rotation”is played each day30 Every 55m15 Every 4h 0 2007 2012
  • Do Ev n’t ha en fee d a Ein l ba the stein d. rap ist .Don’t feel bad.
  • 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 Easily swayed by tone Side with our tribes Dig in and ignore new evidence
  • 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 self-evident.” Arthur Schopenhauer, philosopher (1788-1860)
  • 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.” (which was what Stewart Brand said)
  • 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 brain on organizatinal change, October, 2012, Harvard Business Review
  • “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
  • 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/) how to play them. They just don’t know
  • Alistair Croll @acroll www.solveforinteresting.comTHANKS! alistair@solveforinteresting.comSOLVEforINTERESTINGOTHERWISE LIFE IS DULL.