Transforming Big Data into Decisions -- keynote at IBM/s 2014 Big Data Day

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Keynote at IBM event on what I have learned at Amazon and afterwards on how to turn data into decisions.

Keynote at IBM event on what I have learned at Amazon and afterwards on how to turn data into decisions.

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  • Bridging physical and digital
  • Plumbing
    Consumer mindset
  • Plumbing
    Consumer mindset
  • Skeptical
  • Again, turning costs into profits
  • Again, turning costs into profits
  • Again, turning costs into profits
  • Human mind is bottleneck
    Collaborative consumption
    (PR)ODUCT
  • Convers(at)ion
  • Who of you owns (or has owned) a bitcoin?
    Concrete action, understand the trade-offs
    Bitcoin: Should get nobel prize in economics!
  • Let people do what people are good at, and computers do what computers are good at

    Undergrads: answer questions
    Grads: ask questions

    not on analytics or reports
  • What is a purchase? Product space awareness
  • What is a purchase? Product space awareness
  • What is Data? Social Data? Big Data?


    30 yrs ago: what data?
    1984: CERN
    Data guy
     
     
    1994: China Inet (Does connecting pages work?)
    No mobile phones
    Planes perfectly well
    Foreigners different prices on seats from Chinese – fair?
     
    Me: Kiat
     
    2004: Jack Ma
     
    (MOBILE)
    How has data changed you in the last few years?
    What do you do differently now based on data?
  • Ok, it is an illusion. Then offer framework
  • 2x2 matrix
    As a step to privacy

Transcript

  • 1. 1 @aweigend IBM Mexico 2014.06.11
  • 2. 2 Government Individual Business
  • 3. 3 Transforming Big Data… … into Decisions
  • 4. • 1970’s: Building Computers • 1980’s: Connecting Computers • 1990’s: Connecting Pages • 2000’s: Connecting People • 2010’s: Connecting Data 4
  • 5. Today, in a single day, we are creating more data than mankind did from its beginning through 2000 5
  • 6. ...you had all the data in the world… 6 Imagine… … what would you do to delight your customers?
  • 7. 7 Questions 1. What is abundant? 2. What is scarce? 3. What are the constraints? 4. What is the bottleneck?
  • 8. Data Insight Know- ledge Wisdom 8
  • 9. 9 Last century: Physical Interactions This century: Human Interactions
  • 10. 10
  • 11. 11 Stanford Berkeley Google Facebook SF Home
  • 12. google.com/history 12 15,317 searches
  • 13. Which data would you pay most for? 1. Geolocation:Where did he go? 2. Search history: What did he search for? 3. Purchase history:What did he buy? 4. Social graph:Who are his “friends"? 5. Demographics 13
  • 14. Value of Data? Value of Data = Impact on Decisions 14
  • 15. Data Rules 1. Start with a question, not with the data 2. Focus on decisions and actions, design for feedback 15
  • 16. 16 O2O
  • 17. 17
  • 18. 18 Seattle June 18
  • 19. 19 O2O: Mobile • Identity: Proxy for person • Context: Many sensors  Easy for user to contribute  Easy to reach user, but high cost if inappropriate
  • 20. The Journey of Amazon What changed? 20
  • 21. The Journey of Amazon What changed? • Algorithms  Data • AI • BI • CI • DI 21
  • 22. What changed, what didn’t? Changed • Ask for forgiveness, not for permission • Customer-centricity • Helping people make better decisions • Recommendations Unchanged • Algorithms  Data • AI • BI • CI • DI 22
  • 23. Data Scientist • Data literate • Able to handle large data sets • Understands domain and modeling • Wants to communicate and collaborate • Curious with “can-do” attitude 23
  • 24. Goal: Help people make better decisions Data Strategy: Make it trivially easy to  Contribute  Connect  Collaborate 24 Amazon = Data Refinery
  • 25. Customers who bought this item 25 also bought
  • 26. 26 amazon.co.uk amazon.com
  • 27. Amazon: Recommendations 1. Manual (Experts) 2. Implicit (Clicks, Searches) 3. Explicit (Reviews, Lists) 4. Situation (Local, Mobile) 5. Connections (Social graph) 27
  • 28. An Experiment in Marketing Amazon’s Share the Love
  • 29. Amazon:The C’s of Marketing • Content • Context • Connection • Conversation 29
  • 30. Markets are Conversations Conversations are Markets 30 2000 2014
  • 31. Company Consumers Where are the Conversations?
  • 32. Data sources for marketing a new phone product Social Graph (Who called whom?) Segmentation (Demographics, Loyalty)
  • 33. Social GraphSegmentation 0.28% Adoption rate 1.35% 4.8x
  • 34. Non-Social: Audience Social: Connected Individual 34 Shift in Mindset
  • 35. Fitness Function • Also called the equation of business • Expresses your beliefs, mission, values • Needed for the of evaluation of experiments 35
  • 36. Focus • Audience • Associate • Basket • Country • Customer • Household • Lawyer • Manufacturer • Product • Register • Shelf • Store • Supplier • Truck 36
  • 37. Focus • Audience • Associate • Basket • Country • Customer • Household • Lawyer • Manufacturer • Product • Register • Shelf • Store • Supplier • Truck 37
  • 38. Focus • Audience • Associate • Basket • Country Customer • Household • Lawyer 38 = Connected Individual
  • 39. Data Rule #3 1. Start with a question, not with the data 2. Focus on decisions and actions 3. Base your fitness function on metrics that matter to your customers 39
  • 40. Data Ecosystem Create > > Consume 40 data.taobao.com Refine Distribute
  • 41. Data Ecosystem 41 data.taobao.com Users: 420 k Price per day: 10 元 = USD 2 Revenues per year: 1.5 B 元 = USD 250 M
  • 42. New Business Models Share Economy “Access trumps possession”  Airbnb,…  Uber, Sidecar, Lyft,…  Relayrides, Getaround,… Innovation enabled by data 42
  • 43. 43 Getaround requires Facebook to login. We use Facebook to ensure trust and safety to our community.
  • 44. What is the Essence of Facebook? 1. Content creation 2. Content distribution and consumption 3. Identity management 44
  • 45. “On the Internet, nobody knows you’re a dog” 1993
  • 46. “On the Internet, everybody knows you’re a dog” 2014
  • 47. Shift in Identity Non-social: Attributes Social: Relationships 47
  • 48. • Trust is distributed (across the network) • History is traceable (via blockchain)  Digital title for your house  Digital contracts, signatures… Innovation enabled by data 48
  • 49. Summary: Data Rules 1. Start with a question, not with the data 2. Focus on decisions and actions 3. Base your fitness function on metrics that matter to your customers 4. Embrace transparency 49
  • 50. Summary: Commerce 1. E-commerce: Digitize  Focus on company and products 2. Me-commerce: Share  Focus on customer and attributes 3. We-c0mmerce: Connect  Focus on connections between individuals 51
  • 51. Questions? 1. Do your customers understand the value they get when they give you data? 2. Does your product or service get better over time and with data (or worse)? 52
  • 52. … 1984 – 1994 – 2004 – 2014 … • How has data (connectivity, cloud, refineries) changed you in the past years? • How will data change you, your community, your business, society in the next few years? 53
  • 53. 54 Government Individual Business
  • 54. Thank you 55 @aweigend +1 650 906-5906 andreas@weigend.com weigend.com/files/speaking youtube.com/socialdatarevolution
  • 55. A Brief History of Privacy 1. No Privacy Some inventions (Chimneys, Cities) 2. Privacy More inventions (Facebook, Glass) 3. Illusion of Privacy 56
  • 56. Framework for Privacy Decisions 57 Expected Unexpected Good - ? ? Bad - ? ?