Building Competitive Moats With Data

Pete Skomoroch@peteskomorochDataLeadOct 1, 2014 - Berkeley

About Me

• Ex Principal Data Scientist @ LinkedIn • Entrepreneur, Advisor at Data Collective

Competitive Moats

Data as Competitive Moat

Why the current obsession with Big Data?

The rise of Hadoop

What is Big Data?

Big Data: Myths

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

If software is eating the world…

… it is replacing it with data

Startups are moving offline life to online data

• Restaurants => Yelp• Resume + Rolodex => LinkedIn• Powerpoint => SlideShare• Yearbook + Photos => Facebook• Real Estate => RedFin• Interior Design => Houzz

The Data Factory Revolution

Source: http://www.linkedin.com/channels/disrupt

2013 Steve Jennings/Getty Images Entertainment

Early Data Factory: del.icio.us

User Generated Data Moats

User entered data has Gravity

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

Reputation based Data Moats

Network Based Data Moats

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

Real scientists make their own data

Build distinct, defensible datasets

This sounds great, how do I build a data moat?

http://xkcd.com/802/

A new occupation: data scientist

source: data from http://www.linkedin.com/skills

What do data scientists actually do?

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”

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

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

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

LinkedIn Skills: a moat built by data products

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

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

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