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

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@aweigendIBM Mexico 2014.06.11

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Government

IndividualBusiness

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Transforming Big Data…

… into Decisions

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• 1970’s: Building Computers• 1980’s: Connecting Computers• 1990’s: Connecting Pages• 2000’s: Connecting People• 2010’s: Connecting Data

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Today, in a single day,we are creating more data

than mankind did from its beginning

through 2000

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...you had all the data in the world…

Imagine…

… what would you do to delight your

customers?

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Questions1. What is abundant?2. What is scarce?3. What are the constraints?4. What is the bottleneck?

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Data Insight Know-ledge Wisdom

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Last century:Physical

Interactions

This century:Human

Interactions

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Stanford

Berkeley

Google

Facebook

SF Home

google.com/history

15,317searches

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

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Value of Data?

Value of Data=

Impact on Decisions

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Data Rules

1. Start with a question, not with the data

2. Focus on decisions and actions, design for feedback

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O2O

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Whoa!!May Ikeepit?

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SeattleJune 18

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O2O: Mobile• Identity: Proxy for person• Context: Many sensors

Easy for user to contribute Easy to reach user, but

high cost if inappropriate

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The Journey of AmazonWhat changed?

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The Journey of AmazonWhat changed?

• Algorithms Data

• AI• BI• CI• DI

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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

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Data Scientist• Data literate• Able to handle large data sets• Understands domain and modeling• Wants to communicate and

collaborate• Curious with “can-do” attitude

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Goal: Help people make better decisionsData Strategy: Make it trivially easy to

Contribute Connect Collaborate

Amazon = Data Refinery

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Customers who bought this item also bought

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amazon.co.uk

amazon.com

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Amazon: Recommendations1. Manual (Experts)2. Implicit (Clicks, Searches)3. Explicit (Reviews, Lists)4. Situation (Local, Mobile)5. Connections (Social graph)

An Experiment in Marketing

Amazon’s Share the Love

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Amazon: The C’s of Marketing• Content• Context• Connection• Conversation

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Markets are Conversations

Conversations are Markets

2000

2014

Company

Consumers

Where are the Conversations?

Data sources for marketinga new phone product

Social Graph(Who called

whom?)

Segmentation (Demographics,

Loyalty)

Social Graph

Segmentation

0.28%

Adoptionrate

1.35%

4.8x

Non-Social: Audience

Social: Connected Individual

Shift in Mindset

Fitness Function

• Also called the equation of business

• Expresses your beliefs, mission, values• Needed for the of evaluation of experiments

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Focus• Audience• Associate• Basket• Country• Customer• Household• Lawyer

• Manufacturer• Product• Register• Shelf• Store• Supplier• Truck

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Focus• Audience• Associate• Basket• Country• Customer• Household• Lawyer

• Manufacturer• Product• Register• Shelf• Store• Supplier• Truck

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Focus• Audience• Associate• Basket• Country Customer• Household• Lawyer

= Connected Individual

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Data Rule #31. Start with a question, not with

the data2. Focus on decisions and actions3. Base your fitness function on

metrics that matter to your customers

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Data Ecosystem

Create > > Consume

data.taobao.com

RefineDistribute

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Data Ecosystem

data.taobao.com

Users: 420 kPrice per day: 10 元 = USD 2 Revenues per

year:1.5 B 元 = USD 250 M

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New Business ModelsShare Economy “Access trumps possession” Airbnb,… Uber, Sidecar, Lyft,… Relayrides, Getaround,…

Innovation enabled by data

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Getaround requires Facebook to login. We use Facebook to ensure trust and safety to our community.

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What is the Essence of Facebook?

1. Content creation2. Content distribution and

consumption3. Identity management

“On the Internet, nobody knows you’re a dog”

1993

“On the Internet, everybody knows you’re a dog”

2014

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Shift in IdentityNon-social: Attributes

Social: Relationships

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• Trust is distributed (across the network)

• History is traceable (via blockchain) Digital title for your house Digital contracts, signatures…

Innovation enabled by data

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Summary: Data Rules1. Start with a question, not with

the data2. Focus on decisions and actions3. Base your fitness function on

metrics that matter to your customers

4. Embrace transparency

Summary: Commerce1. E-commerce: Digitize

Focus on company and products2. Me-commerce: Share

Focus on customer and attributes3. We-c0mmerce: Connect

Focus on connections between individuals

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)?

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… 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?

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Government

IndividualBusiness

Thank you

@aweigend+1 650 906-5906

[email protected]

weigend.com/files/speakingyoutube.com/socialdatarevolution

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A Brief History of Privacy1. No PrivacySome inventions (Chimneys, Cities)

2. PrivacyMore inventions (Facebook, Glass)

3. Illusion of Privacy

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Framework for Privacy Decisions

Expected Unexpected

Good - ? ?

Bad - ? ?