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

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 - ? ?