Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.

1. Building Competitive Moats With Data
Pete Skomoroch
@peteskomoroch
DataLead
Oct 1, 2014 - Berkeley

2. About Me
• Ex Principal Data Scientist @ LinkedIn
• Entrepreneur, Advisor at Data Collective

3. Competitive Moats

4. Data as Competitive Moat

5. Why the current obsession with Big Data?

6. The rise of Hadoop

7. What is Big Data?

8. Big Data: Myths

9. Big Data: Reality
• Science, theory, and reason are not being replaced
• Big Data is different: for some problems, big data produces
better results than we find with smaller samples
• Data storage and logging are increasingly cheap, so err on the
side of collecting data to process later if you think it may be
valuable
• Large, differentiated data assets are the foundation for
defensible products and better decisions

10. If software is eating the world…

11. … it is replacing it with data

12. Startups are moving offline life to online data
• Restaurants => Yelp
• Resume + Rolodex => LinkedIn
• Powerpoint => SlideShare
• Yearbook + Photos => Facebook
• Real Estate => RedFin
• Interior Design => Houzz

13. The Data Factory Revolution
Source:
http://www.linkedin.com/channels/disrupt
2013 Steve Jennings/Getty Images
Entertainment

14. Early Data Factory: del.icio.us

15. User Generated Data Moats

16. User entered data has Gravity

17. Behavioral history is a moat: life is easier when
apps remember you

18. Reputation based Data Moats

19. Network Based Data Moats

20. Don’t build on top of someone else’s moat

21. Real scientists make their own data

22. Build distinct, defensible datasets

23. This sounds great, how do I build a data moat?
http://xkcd.com/802/

24. A new occupation: data scientist

25. What do data scientists actually do?
source: data from
http://www.linkedin.com/skills

26. Two species of data scientist*
Type I: Traditional BI
• Question-driven
• Interactive
• Ad-hoc, post-hoc
• Fixed data
• Focus on speed and
flexibility
• Output is embedded into a
report, dashboard, or in-database
scoring engine
Type II: Data Products
• Metric-driven
• Automated
• Systematic
• Fluid data
• Focus on transparency and
reliability
• Output is a production
system that makes
customer-facing decisions
*Slide adapted from Josh Wills “From the Lab to the Factory”

27. Data Products: automated systems that make
customer facing decisions and collect data

28. Data Product pre-history: Data Aggregators
• 1972: Vinod Gupta forms American Business
Information, Inc., a database initially built via
manual data entry of Yellow Pages
information
• 1973: LEXIS full text legal search launches
publicly
• 1986: Bloomberg reaches 5,000 terminal
subscribers
• 1994: Jerry Yang & David Filo compile and
maintain a hand curated set of categorized
links on the World Wide Web known as the
Yahoo! Directory

29. The Rise of Algorithmic Data Products
• Google: Web Search, PageRank, AdWords
• Netflix: Movie Recommendations
• Pandora: Music Recommendations
• eBay: Product Search, Fraud Detection, Advertising
• Amazon: Similar Items, Book Recommendations
• LinkedIn: People You May Know, Who Viewed My Profile

30. LinkedIn Skills: a moat built by data products

31. Data Product investment and ROI
• Skill Extraction and Standardization Pipeline
• Skill Pages
• Skills Section on member profiles
• Suggested Skills Algorithm and email > 20M members
• Skill Endorsements > 60M members, 3B+ Edges
• Big product wins: engagement, recall, relevance
• SkillRank & Reputation Algorithm R&D
• LinkedIn is now the definitive source for information
on skills & expertise
*Statistics as of 2013

32. How leaders can drive data growth
• Accountability: Who defines the data vision &
roadmap in your organization? Who is accountable for
building and expanding your moat?
• Invest in data infrastructure, training, logging, & tools
for rapid iteration. Build a data lake.
• Invest in exploration and innovation, including user
facing data product and algorithm development
• Define a framework for trading off data quality and
quantity metrics
• Ask “How does this increase our data moat?” when
evaluating any new project, incentivize it

33. Twitter: @peteskomoroch
LinkedIn: linkedin.com/in/peterskomoroch