2013 the power of social data: dec transforming big data into decisions andreas weigend · 2020. 7....
TRANSCRIPT
The Power of Social Data: Transforming Big Data
into Decisions
Andreas Weigend bit.ly/weigenditalia
1
Mila
no
, 04
Dec
20
13
Agenda 1. Data and Decisions
Value of Data?
2. Amazon as Data Refinery
Equation of Business
3. Implications of Social Data Revolution
Audience Connected Individuals and Context
4. Summary Questions via Twitter, use @aweigend 2
15 years ago: Connecting Pages (Google)
10 years ago: Connecting People (FB)
5 years ago: Connecting Apps (Apple)
Now: Connecting Data
3
Today, in a single day,
we are creating more data
than mankind did
from its beginning
through 2000
4
5
Mobile
Context: Many sensors
Identity: Proxy for person
Easy for advertiser to reach
user, but high cost of
interrupt if inappropriate
Easy for user to contribute
Social Data: Two Meanings
1. Relationships between people (“social graph”,
e.g., on Facebook or LinkedIn)
2. Data people share (or “socialize”, e.g., check-
in, purchase, book review, picture) -------------------------------------------------------------------------------------------------------------------------------------------------------------
Note: Social Media differs from Social Data (e.g., GPS) 6
• Google has changed the way a billion people
think about information
• Facebook has changed the way a billion people
think about identity
• Amazon has changed the way a billion people
think about purchases
7
Social Data Revolution
1. Transport energy Industrial Revolution
Production
2. Transport bits Information Revolution
Communication
3. Create (and share) bits Social Data Rev
8
Data and Decisions
Rule #1:
Start with a question, not with the data
E.g., Which route do I take?
E.g., Who do I work with?
9
Mindset
Skillset
Toolset
Dataset
10
Big Data: Mindset
to turn Mess into Decisions
11
<when>2013-05-28T00:17:08.341-07:00</when> <gx:coord>11.0955646 47.4944176 0</gx:coord> <when>2013-05-28T00:46:14.410-07:00</when> <gx:coord>11.0894932 47.4880099 0</gx:coord> <when>2013-05-28T00:47:14.425-07:00</when> <gx:coord>11.1069126 47.5154249 0</gx:coord>
12
Stanford
Berkeley
SF Home
Imagine…
13
…you had your geolocation from the last
decade readily available at your fingertips
• What question would you ask?
• How would knowing that it is recorded 24/7
change your behavior?
London 1854
14
google.com/history
17
15,317 searches
What data would you pay for most?
1. Geolocation: Where did a customer go?
2. Search history: What did she search for?
3. Purchase history: What did she buy?
4. Social graph: Who are her friends?
5. Demographics and similar attributes 18
Big Data = Mindset
to turn Mess into Decisions
19
• External (facing the outside)
• Internal (within the company)
The Journey of Amazon
What changed?
20
The Journey of Amazon
What changed?
• Algorithms Data
• AI
• BI
• CI
• DI
21
The Journey of Amazon
What changed?
• “Ask for forgiveness, not for permission”
• True customer-centricity
• Recommendations and Discovery
What did not change?
• Algorithms Data
• AI
• BI
• CI
• DI
22
Agenda 1. Data and Decisions
Value of Data?
2. Amazon as Data Refinery
Equation of Business
3. Implications of Social Data Revolution
Audience Connected Individuals and Context
4. Summary Questions via Twitter, use @aweigend 23
Goal: Help people make better decisions
Data strategy: Make it trivially easy to
Contribute
Connect
Collaborate 24
Amazon as Data Refinery
Equation of Business
• Expresses business strategy, values etc.
• Needed for evaluation of experiments
Rule #2:
Base the equation of your business on
metrics that matter to your customers
25
Rule #3:
Focus on decisions and actions, and design
for feedback
26
Equation of Business
5 Stages of Amazon Recommendations
1. Manual (Experts)
2. Implicit (Clicks, Searches)
3. Explicit (Reviews, Lists)
4. Situation (Local, Mobile)
5. Social graph (Connections)
27
Social Commerce
Amazon’s Share the Love
The 4 C’s
• Content
• Context
• Connection
• Conversation
29
Markets are Conversations
Conversations are Markets
30
2000
2013
Company
Consumers
Where are the Conversations?
Agenda 1. Data and Decisions
Value of Data?
2. Amazon as Data Refinery
Equation of Business
3. Implications of Social Data Revolution
Audience Connected Individuals and Context
4. Summary Questions via Twitter, use @aweigend 32
Data sources for marketing a new phone product
Social Graph
(Who called whom?)
Segmentation
(Demographics, Loyalty)
Social Graph Segmentation
0.28%
Adoption rate
1.35%
4.8x
Non-Social: Audience
Social: Connected Individual
35
Shift in Mindset
“On the Internet, nobody knows you’re a dog”
1993
“On the Internet, everybody knows you’re a dog”
2013
Shift in Identity
Non-social: Attributes
Social: Relationships
38
Shift in Business Models
Non-social: hotels.com, craigslist
Social: airbnb, lyft, relay rides,
39
E, Me, We! 1. Digitize: E-commerce
Focus on company and products
2. Share: Me-commerce
Focus on consumer and attributes
3. Connect: We-c0mmerce
Focus on connection between consumers 40
Rule #4:
Embrace transparency: Make it trivially easy
for people to connect, contribute, and
collaborate
41
Connected Individuals
Agenda 1. Data and Decisions
2. Amazon as Data Refinery
3. Implications of Social Data Revolution
4. Outlook and Summary
Last chance to tweet questions, @aweigend 42
GLΛSS
43
The 4 Data Rules 1. Start with a question, not with the data
2. Base the equation of your business on metrics that
matter to your customers
3. Focus on decisions and actions, design for
feedback
4. Embrace transparency: Make it trivially easy for
people to connect, contribute, and collaborate
45
Some Data Beliefs 1. Let people do what people are good at, and
computers do what computers are good at
2. Build stuff that enables a future you want to live in
3. Give data to get data
46
Questions for you
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?
47
Data Scientist • Data literate
• Able to handle large data sets
• Understands domain and modeling
• Want to communicate and collaborate
• Curious with “can-do” attitude 49
Data Science vs Business Intelligence
50
Data Science vs Business Intelligence
51