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Winning the Big Data Revolution:
What Business Leaders Need to Know

Kenneth Cukier
Data Editor, The Economist
Co-author “...
first, some questions…
how would you…
-track economic growth
without measuring money?
-uncover drug side-effects
without a medical study?
- know ...
a story…
how
technology
data
mindset
value
data = resource
new raw material
vital economic input
BIG
DATA?
more is different
THEN
NOW
2003
10 years
$1 billion

2013
2-3 days
$2-3,000

Time and cost of sequencing 3 billion base-pairs
atoms to bits
atoms to bits
data to decision
atoms to bits
data to decision
engines; machines; etc.
information into insight
more
messy
correlations
more
messy
correlations
more more more more more
more more more more more
more more more more more
more more more more more
more more more more mo...
more
messy
correlations
messy messy messy messy
messy messy messi messy
messy messy messy messy
messy messy messy meeey
messy m ssy messy messy
me...
more
messy
correlations
more
messy
correlations
more
messy
correlations
more
messy
correlations
“preemies”
“preemies”
16 real-time streams of vital signs
1,000+ datapoints per second
“preemies”
16 real-time streams of vital signs
1,000+ datapoints per second
more | messy | correlations
datafication
datafication
location
datafication
posture
datafication
datafication
datafication
datafication
datafication
floors, gait, earbuds, etc
datafication
store, share, process, use
extract new value
value
who to hire?
value
reuse
value
reuse
asset
value
reuse
asset
eg, economic growth
value
skills, mindset, data
change
people & organisations
rivalry
rivalry
stuttgart vs. silicon valley
rivalry
big pharma vs. bing
Source: “Web-scale pharmacovigilance: listening to signals from the crowd.”
White RW, Tatonetti NP, Shah NH, Altman RB, Ho...
access to data
risks
risks
privacy
risks
privacy
propensity
risks
privacy
propensity
ownership
deification of data
deification of data
“data-ism”
next
legacy advantage
cultural advantage?
Pierre-Simon Marquis de
Laplace
Pierre-Simon Marquis de
Laplace
data scientist, 1749-1827
Charles Joseph Minard, 1869
merci
KennethCukier@Economist.com
@kncukier
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
Winning the big data revolution: what businesses leaders need to know
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Winning the big data revolution: what businesses leaders need to know

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Tout le monde parle des big data. Mais peu nombreux sont ceux qui savent véritablement ce qu’ils désignent et surtout comment les entreprises peuvent les utiliser pour révéler leur véritable potentiel. Comment donner du sens à ces données ? Comment en faire un avantage compétitif majeur ? Comment sortir vainqueur de cette révolution ? A l’occasion de la sortie de son dernier ouvrage, dans lequel il explore le phénomène big data et les nouveaux business model qui sont en train d’émerger, Kenneth Cukier nous livrera, lors d’une keynote inédite, les clefs pour réussir cette transformation fondamentale.

Speakers : Kenneth Cukier (The Economist)

Publié dans : Technologie
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  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (2019 Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
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Winning the big data revolution: what businesses leaders need to know

  1. 1. Winning the Big Data Revolution: What Business Leaders Need to Know Kenneth Cukier Data Editor, The Economist Co-author “Big Data: A revolution that will transform how we live, work and think” Microsoft TechDays, February 13, 2014
  2. 2. first, some questions…
  3. 3. how would you… -track economic growth without measuring money? -uncover drug side-effects without a medical study? - know when to buy a plane ticket?
  4. 4. a story…
  5. 5. how technology data mindset
  6. 6. value data = resource new raw material vital economic input
  7. 7. BIG DATA?
  8. 8. more is different
  9. 9. THEN NOW 2003 10 years $1 billion 2013 2-3 days $2-3,000 Time and cost of sequencing 3 billion base-pairs
  10. 10. atoms to bits
  11. 11. atoms to bits data to decision
  12. 12. atoms to bits data to decision engines; machines; etc.
  13. 13. information into insight
  14. 14. more messy correlations
  15. 15. more messy correlations
  16. 16. more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more more
  17. 17. more messy correlations
  18. 18. messy messy messy messy messy messy messi messy messy messy messy messy messy messy messy meeey messy m ssy messy messy messy messy messy messs
  19. 19. more messy correlations
  20. 20. more messy correlations
  21. 21. more messy correlations
  22. 22. more messy correlations
  23. 23. “preemies”
  24. 24. “preemies” 16 real-time streams of vital signs 1,000+ datapoints per second
  25. 25. “preemies” 16 real-time streams of vital signs 1,000+ datapoints per second more | messy | correlations
  26. 26. datafication
  27. 27. datafication location
  28. 28. datafication posture
  29. 29. datafication
  30. 30. datafication
  31. 31. datafication
  32. 32. datafication
  33. 33. datafication floors, gait, earbuds, etc
  34. 34. datafication store, share, process, use extract new value
  35. 35. value
  36. 36. who to hire?
  37. 37. value reuse
  38. 38. value reuse asset
  39. 39. value reuse asset eg, economic growth
  40. 40. value skills, mindset, data
  41. 41. change people & organisations
  42. 42. rivalry
  43. 43. rivalry stuttgart vs. silicon valley
  44. 44. rivalry big pharma vs. bing
  45. 45. Source: “Web-scale pharmacovigilance: listening to signals from the crowd.” White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E. J Am Med Inform Assoc. 2013 May 1;20(3):404-8.
  46. 46. access to data
  47. 47. risks
  48. 48. risks privacy
  49. 49. risks privacy propensity
  50. 50. risks privacy propensity ownership
  51. 51. deification of data
  52. 52. deification of data “data-ism”
  53. 53. next
  54. 54. legacy advantage
  55. 55. cultural advantage?
  56. 56. Pierre-Simon Marquis de Laplace
  57. 57. Pierre-Simon Marquis de Laplace data scientist, 1749-1827
  58. 58. Charles Joseph Minard, 1869
  59. 59. merci KennethCukier@Economist.com @kncukier

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