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. DataInsight 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 Graph Segmentation 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 . 2.Me-commerce . 3.We-c0mmerce . 50

51. 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

52. 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

53. … 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

54. 54 Government Individual Business

55. Thank you 55 @aweigend +1 650 906-5906 andreas@weigend.com weigend.com/files/speaking youtube.com/socialdatarevolution

56. A Brief History of Privacy 1. No Privacy Some inventions (Chimneys, Cities) 2. Privacy More inventions (Facebook, Glass) 3. Illusion of Privacy 56

57. Framework for Privacy Decisions 57 ExpectedUnexpected Good - ?? Bad - ??