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Developing Data ProductsUber Tech TalkPete Skomoroch @peteskomorochDecember 5 2012

Developing Data ProductsExamples, Techniques, & Lessons Learned

Our MissionConnect the world’s professionals to make them

more productive and successful.

Our VisionCreate economic opportunity for every

professional in the world.

Members First!

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LinkedIn is the leading professional network site

Worldwide Workforce

3,300M+2

Worldwide Professionals

640M+2

LinkedIn Members187M+

1

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LinkedIn profiles represent our professional identity

187MMembers 187M MemberProfiles

1 2

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We have a lot of data.

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We have a lot of data.And (like everyone else), we store it in Hadoop.

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We have a lot of data.And (like everyone else), we store it in Hadoop.And people build awesome things with that data.

What do we mean by data products?

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Building products from data at LinkedIn

A few examples:

People You May Know Skills and Endorsements Year in Review Network Updates Digest InMaps Who’s viewed my profile Collaborative Filtering Groups You May Like and more…

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Collaborative Filtering: LinkedIn Skill Pages

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Classification: giving structure to unstructured data

Extract

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Clustering & Disambiguation

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De-duplication and Normalization

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Network Algorithms: Relevance & Ranking

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Prediction: Personalized Skill Recommendations

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Skill Endorsements

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Social Proof and the Skill Endorsement Graph

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The Economic Graph: Skills, Jobs, People, Locations…

TimeLocation

Lessons learned developing data products

Collect the right data at the right time

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Large amounts of data can reveal new patternsP

rob

ab

ilit

y of

Job

Tit

le

Months since graduation

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Be wary of “black-box” approaches

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Look at your data

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Aggregate statistics can be misleading

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Build a viewer app, “micro-listen”

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Algorithmic intuition: include data geeks in design

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OODA: Think like a jet fighter

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OODA: Observe, Orient, Decide, Act

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OODA: The speed you can move determines victory

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Red teaming: what can go wrong likely will

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Error data is super valuable, analyze it and adapt

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Conclusion: tips for developing data products

Collect the right data at the right time Large amounts of data can reveal new patterns Be wary of “black box” approaches Look at your raw data Aggregate statistics can be misleading Build and use viewer apps Include data geeks in design process OODA: Think like a jet fighter Red-teaming: anticipate edge cases Find opportunity in your error data

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Questions?

More info: data.linkedin.com@peteskomoroch