Presentation from a talk given at Boston Big Data Innovation Summit, September 2012. Summary: The Data Science team at LinkedIn focuses on 3 main goals: (1) providing data-driven business and product insights, (2) creating data products, and (3) extracting interesting insights from our data such as analysis of the economic status of the country or identifying hot companies in a certain geographic region. In this talk I describe how we ensure that our products are data driven -- really data infused at the core -- and share interesting insights we uncover using LinkedIn's rich data. We discuss what makes a good data scientist, and what techniques and technologies LinkedIn data scientists use to convert our rich data into actionable product and business insights, to create data-driven products that truly serve our members.

1. Data Infused Product Design & Insights
at LinkedIn
Yael Garten
Senior Data Scientist at LinkedIn,
Team Lead for Mobile Data Analytics
Big Data Innovation Summit, Boston September 13-14, 2012

2. Our mission: Connect the world’s
professionals to make them more productive
and successful
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3. Whatever you do, wherever you are
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4. LinkedIn at a Glance
• Founded in 2003
• 175M+ members
• 2 new signups per second
• Executives from all Fortune 500 companies
• 80% are “decision makers”
• Average Household income in US: $86,000
• ~4B annual people searches
• Over 200 countries & territories
• 17 different languages
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5. Your Professional Identity
Amazing dataset that we can slice and
dice. By seniority, by job function – we
can ask many interesting questions.
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6. So what data do we have?
175M+ professional
profiles
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7. What does the Data Science team at
LinkedIn do with the data?
• Product and Business Insights
• Build Data Products
• Extract Insights we Share Externally
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8. da·ta noun pl but singular or pl in constr, often attributiveˈdā-tə, ˈda- also ˈdä-
Information in numerical form that can be digitally transmitted or
processed Source : http://www.merriam-webster.com
Normalized Data =
Web Logs = Data
Information
Parse,
Normalize,
Standardize
From data to  Information

9. Knowledge: What’s going on?

10. If you can’t measure
it, you can’t fix it.
Measure everything.
Know thyself: What’s going on?
In the form of reporting, knowing the numbers, understanding usage of
products, patterns in the data, segments of users, tracking the growth and
health of the ecosystem.
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11. Insights: what can we improve?

12. Rethinking our Mobile App: what do people on this
page?
Where do they go next?
How many drop off?
What is the stickiest product?
What works, what doesn’t?
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13. At what times in the day are people using different
devices?
Desktop usage
iPad device accessing
linkedin.com via browser
Hours of the day
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14. data information  knowledge  insights  wisdom
Wisdom: What’s the next needle
mover?

15. Strategic Analyses:
Using data to drive the business.
• What is the value of an action that a user takes on the
site?
• What early behavior on the site is predictive of future
engagement?
• What is the value of a user?
• What is mobile’s impact on social actions?
• How does mobile usage impact desktop site
engagement?
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16. What is Data Science?
Using (multiple) data elements in clever ways to solve
iterative or auxiliary data problems that when combined
solve a data problem that might otherwise be intractable.
What makes a data scientist?
Data Scientist = Curiosity + Intuition + Product & Business
Sense +
Data gathering + Standardization+ Statistics
+ Modeling + Visualization +
Communication
analytics &
data science
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17. What does the Data Science team at
LinkedIn do with the data?
• Product and Business Insights
• Build Data Products
• Extract Insights we Share Externally
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18. Data Products on your LinkedIn homepage
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19. Alumni Data Product
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20. Our Newest Data Product : Skills
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21. How do we do it?
Extract
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22. Skills: Assigning Skills to People
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23. Skills: Who are the Experts?
Have Hadoop as an inferred skill
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24. Standardized data is the key to building compelling
products
Hadoop skill displays +43% y/y growth
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31. Revenue improvement
Hard Worker
Attentive to detail

32. What does the Data Science team at
LinkedIn do with the data?
• Product and Business Insights
• Build Data Products
• Extract Insights we Share Externally
32

33. Data Stories: Growing and Shrinking
Industries

34. Where did all the people go from the collapsed
financial institutions in 2008?

35. Fun stuff: CEO Names
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36. If your name is Chip, you are likely in sales!
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37. The 10 Most attractive start ups to Bay Area
Engineers
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38. The Power of Aggregation
Before
employees worked at
Yahoo! (169)
Google (96)
Oracle (78)
Microsoft (72)
IBM (43)
Before
employees worked at
Google(475)
Microsoft (448)
LinkedIn (169)
Apple, Inc.
(154)
ebay (133)
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39. Market Research Insights through polls
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40. Insights: Data Stories
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41. Big Data that scales.
What Technologies do we use?
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42. Some of our Homegrown Solutions
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43. It’s all about the people who do end-to-end data
science
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44. Our partners to make it all happen
• Product and Design: use data to influence the design of the
product, and user experience & interaction
• Marketing: build models to predict members’ propensity to act on an email
campaign "call to action". When is the best time to message that user and
what does it depend on?
• Business Operations: e.g. How is transition to mobile impacting
ads, subscription upsells
• Executive team: on strategic questions
• Engineering: understanding how data is tracked and implemented
• Data Services: how do we build tools &infrastructure to democratize the
data?
Above all – maintaining the mindset of a data-driven
company. 44

45. Questions? ygarten@linkedin.com

46. Yael Garten
ygarten@linkedin.com