The View from Silicon

Alley: A Venture Capitalist’s

view of The Evolution of Big

Data

Geoff Judge

Partner, iNovia Capital

April 23, 2012

Context for today

• Direct & Database Marketing background

• First Start-up 1995, co-founder 24/7 Media

• iNovia Capital - Primarily Early Stage IT

investors

• Digital Media, eCommerce, Mobile, SaaS, &

Payments

• Our Portfolio -Big Data companies: Collective,

Chango, Resonate, 33 Across (Tynt), uKnow,

Media Armor, Lenddo, Milewise, Vizu, TagMan

Early Days of Big Data

IBM 360

“Big Data” What is it?

• New Storing and analyzing data that’s never

been collected before

• Novel Finding patterns by connecting orthogonal

sets of data

• Nebulous Large, unstructured, and real-time

“A simple way to describe a massive

problem…(or opportunity).”

• Peter Norvig – The Unreasonable Effectiveness of Data

More data beats better algorithms!

The Effect of Data on accuracy Peter Norvig – The Unreasonable Effectiveness of Data

Disruptive Technology

• Massively parallel processing

• Commodity hardware

• Non-relational data models

• Columnar data storage

• Data compression

• Analytics

• Visualizations

Competitive Advantage

Market Leadership

Amazon vs. Barnes & Noble

• “We’re moving to a world of data-centric decision making. You start with the

three V’s of Big Data: volume, velocity and variety. The real driver is the

collection and analysis of large amounts of data to create a competitive

advantage. This is a case where bigger is really better. Sometimes, with not

enough data, you can really make mistakes. On Amazon, the reason that

recommendations go wrong, it is often because there is not enough

information. Bigger is better. The more information you have, the better the

recommendations.”

- Werner Vogels, Amazon CTO

Market Leadership

LinkedIn vs. Monster.com

• “LinkedIn’s growth and that of other social networks is not just a matter of

having user data for the sake of having data. Numbers without context are

useless. What LinkedIn has is personally identifiable data. Corporations and

investors want to be able to track the consumer market as closely as

possible to signal trends that will inform their next product launches.

LinkedIn is a trove of data not just about people, but how people are making

their money and what industries they are working in and how they connect

to each other.”

-ReadWriteWeb

In Ad Tech -Disruptive Solutions for

Audience Targeting Data Sources: All Anonymous No PI

• Web Page Content

• Social Networking Connections

• Ad Interaction

• Search Queries

• Mobile Devices

• Internet Transactions

• Networked Devices and Sensors

Data Applications:

• Recommendation Engines, Sentiment Analysis, Risk Modeling, Fraud

Detection, Marketing Campaign Analysis, Customer Churn Analysis, Social

Graph Analysis, Customer Experience Analytics, Network Monitoring,

Research and Development.

Big Data Portfolio Companies

• Collective – The Audience Engine

• Chango – Search Retargeting

• 33 Across (Tynt) – Brand Graph™ technology, real-

time predictive targeting

• Resonate – Insight Driven Media/ Values Targeting

• TagMan – The Global Leader in Tag Management

Big Data users

• uKnow – Ad Inventory Intelligence

• Media Armor – Mobile retargeting & Acquisition with

measurable ROAS- (return on Ad spend)

• Lenddo – Helps people in Emerging Markets improve

their lives

• Milewise – Helps frequent flyers find free flights

Instantly with Points or Miles

• Vizu – Helps Brands measure campaign performance

Thank You