Your SlideShare is downloading. ×
0
1
@aweigend
IBM Mexico 2014.06.11
2
Government
Individual
Business
3
Transforming Big Data…
… into Decisions
• 1970’s: Building Computers
• 1980’s: Connecting Computers
• 1990’s: Connecting Pages
• 2000’s: Connecting People
• 2010’...
Today, in a single day,
we are creating more data
than mankind did
from its beginning
through 2000
5
...you had all the data in the world…
6
Imagine…
… what would you do
to delight your customers?
7
Questions
1. What is abundant?
2. What is scarce?
3. What are the constraints?
4. What is the bottleneck?
Data Insight
Know-
ledge
Wisdom
8
9
Last century:
Physical Interactions
This century:
Human Interactions
10
11
Stanford
Berkeley
Google
Facebook
SF Home
google.com/history
12
15,317
searches
Which data would you pay most for?
1. Geolocation:Where did he go?
2. Search history: What did he search for?
3. Purchase ...
Value of Data?
Value of Data
=
Impact on Decisions
14
Data Rules
1. Start with a question,
not with the data
2. Focus on decisions and actions,
design for feedback
15
16
O2O
17
18
Seattle
June 18
19
O2O: Mobile
• Identity: Proxy for person
• Context: Many sensors
 Easy for user to contribute
 Easy to reach user, bu...
The Journey of Amazon
What changed?
20
The Journey of Amazon
What changed?
• Algorithms  Data
• AI
• BI
• CI
• DI
21
What changed, what didn’t?
Changed
• Ask for forgiveness,
not for permission
• Customer-centricity
• Helping people make
b...
Data Scientist
• Data literate
• Able to handle large data sets
• Understands domain and modeling
• Wants to communicate a...
Goal: Help people make better decisions
Data Strategy: Make it trivially easy to
 Contribute
 Connect
 Collaborate
24
A...
Customers who bought this item
25
also bought
26
amazon.co.uk
amazon.com
Amazon: Recommendations
1. Manual (Experts)
2. Implicit (Clicks, Searches)
3. Explicit (Reviews, Lists)
4. Situation (Loca...
An Experiment in Marketing
Amazon’s Share the Love
Amazon:The C’s of Marketing
• Content
• Context
• Connection
• Conversation
29
Markets are Conversations
Conversations are Markets
30
2000
2014
Company
Consumers
Where are the Conversations?
Data sources for marketing
a new phone product
Social Graph
(Who called whom?)
Segmentation
(Demographics, Loyalty)
Social GraphSegmentation
0.28%
Adoption
rate
1.35%
4.8x
Non-Social: Audience
Social: Connected Individual
34
Shift in Mindset
Fitness Function
• Also called the equation of business
• Expresses your beliefs, mission, values
• Needed for the of eval...
Focus
• Audience
• Associate
• Basket
• Country
• Customer
• Household
• Lawyer
• Manufacturer
• Product
• Register
• Shel...
Focus
• Audience
• Associate
• Basket
• Country
• Customer
• Household
• Lawyer
• Manufacturer
• Product
• Register
• Shel...
Focus
• Audience
• Associate
• Basket
• Country
Customer
• Household
• Lawyer 38
= Connected Individual
Data Rule #3
1. Start with a question, not with the data
2. Focus on decisions and actions
3. Base your fitness function o...
Data Ecosystem
Create > > Consume
40
data.taobao.com
Refine
Distribute
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
New Business Models
Share Economy “Access trumps possession”
 Airbnb,…
 Uber, Sidecar, Lyft,…
 Relayrides, Getaround,…
...
43
Getaround requires Facebook to login.
We use Facebook to ensure trust and safety to our community.
What is the Essence of Facebook?
1. Content creation
2. Content distribution and consumption
3. Identity management
44
“On the Internet, nobody knows you’re a dog”
1993
“On the Internet, everybody knows you’re a dog”
2014
Shift in Identity
Non-social: Attributes
Social: Relationships
47
• Trust is distributed (across the network)
• History is traceable (via blockchain)
 Digital title for your house
 Digit...
Summary: Data Rules
1. Start with a question, not with the data
2. Focus on decisions and actions
3. Base your fitness fun...
Summary: Commerce
1. E-commerce: Digitize
 Focus on company and products
2. Me-commerce: Share
 Focus on customer and at...
Questions?
1. Do your customers understand the value they
get when they give you data?
2. Does your product or service get...
… 1984 – 1994 – 2004 – 2014 …
• How has data (connectivity, cloud, refineries)
changed you in the past years?
• How will d...
54
Government
Individual
Business
Thank you
55
@aweigend
+1 650 906-5906
andreas@weigend.com
weigend.com/files/speaking
youtube.com/socialdatarevolution
A Brief History of Privacy
1. No Privacy
Some inventions (Chimneys, Cities)
2. Privacy
More inventions (Facebook, Glass)
3...
Framework for Privacy Decisions
57
Expected Unexpected
Good - ? ?
Bad - ? ?
Upcoming SlideShare
Loading in...5
×

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

707

Published on

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

0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
707
On Slideshare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
28
Comments
0
Likes
4
Embeds 0
No embeds

No notes for slide
  • 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 of "Transforming Big Data into Decisions -- keynote at IBM/s 2014 Big Data Day"

    1. 1. 1 @aweigend IBM Mexico 2014.06.11
    2. 2. 2 Government Individual Business
    3. 3. 3 Transforming Big Data… … into Decisions
    4. 4. • 1970’s: Building Computers • 1980’s: Connecting Computers • 1990’s: Connecting Pages • 2000’s: Connecting People • 2010’s: Connecting Data 4
    5. 5. Today, in a single day, we are creating more data than mankind did from its beginning through 2000 5
    6. 6. ...you had all the data in the world… 6 Imagine… … what would you do to delight your customers?
    7. 7. 7 Questions 1. What is abundant? 2. What is scarce? 3. What are the constraints? 4. What is the bottleneck?
    8. 8. Data Insight Know- ledge Wisdom 8
    9. 9. 9 Last century: Physical Interactions This century: Human Interactions
    10. 10. 10
    11. 11. 11 Stanford Berkeley Google Facebook SF Home
    12. 12. google.com/history 12 15,317 searches
    13. 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. 14. Value of Data? Value of Data = Impact on Decisions 14
    15. 15. Data Rules 1. Start with a question, not with the data 2. Focus on decisions and actions, design for feedback 15
    16. 16. 16 O2O
    17. 17. 17
    18. 18. 18 Seattle June 18
    19. 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. 20. The Journey of Amazon What changed? 20
    21. 21. The Journey of Amazon What changed? • Algorithms  Data • AI • BI • CI • DI 21
    22. 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. 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. 24. Goal: Help people make better decisions Data Strategy: Make it trivially easy to  Contribute  Connect  Collaborate 24 Amazon = Data Refinery
    25. 25. Customers who bought this item 25 also bought
    26. 26. 26 amazon.co.uk amazon.com
    27. 27. Amazon: Recommendations 1. Manual (Experts) 2. Implicit (Clicks, Searches) 3. Explicit (Reviews, Lists) 4. Situation (Local, Mobile) 5. Connections (Social graph) 27
    28. 28. An Experiment in Marketing Amazon’s Share the Love
    29. 29. Amazon:The C’s of Marketing • Content • Context • Connection • Conversation 29
    30. 30. Markets are Conversations Conversations are Markets 30 2000 2014
    31. 31. Company Consumers Where are the Conversations?
    32. 32. Data sources for marketing a new phone product Social Graph (Who called whom?) Segmentation (Demographics, Loyalty)
    33. 33. Social GraphSegmentation 0.28% Adoption rate 1.35% 4.8x
    34. 34. Non-Social: Audience Social: Connected Individual 34 Shift in Mindset
    35. 35. Fitness Function • Also called the equation of business • Expresses your beliefs, mission, values • Needed for the of evaluation of experiments 35
    36. 36. Focus • Audience • Associate • Basket • Country • Customer • Household • Lawyer • Manufacturer • Product • Register • Shelf • Store • Supplier • Truck 36
    37. 37. Focus • Audience • Associate • Basket • Country • Customer • Household • Lawyer • Manufacturer • Product • Register • Shelf • Store • Supplier • Truck 37
    38. 38. Focus • Audience • Associate • Basket • Country Customer • Household • Lawyer 38 = Connected Individual
    39. 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. 40. Data Ecosystem Create > > Consume 40 data.taobao.com Refine Distribute
    41. 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. 42. New Business Models Share Economy “Access trumps possession”  Airbnb,…  Uber, Sidecar, Lyft,…  Relayrides, Getaround,… Innovation enabled by data 42
    43. 43. 43 Getaround requires Facebook to login. We use Facebook to ensure trust and safety to our community.
    44. 44. What is the Essence of Facebook? 1. Content creation 2. Content distribution and consumption 3. Identity management 44
    45. 45. “On the Internet, nobody knows you’re a dog” 1993
    46. 46. “On the Internet, everybody knows you’re a dog” 2014
    47. 47. Shift in Identity Non-social: Attributes Social: Relationships 47
    48. 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. 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. 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. 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. 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. 53. 54 Government Individual Business
    54. 54. Thank you 55 @aweigend +1 650 906-5906 andreas@weigend.com weigend.com/files/speaking youtube.com/socialdatarevolution
    55. 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. 56. Framework for Privacy Decisions 57 Expected Unexpected Good - ? ? Bad - ? ?
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.

    ×