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Data is the new sexy: perspectives from a VC

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I recently sat with Geoff Judge from iNovia Capital (full disclosure iNovia is one of the investors in Chango). As a VC, Geoff shared his perspectives on big data and opportunities for marketers.

I recently sat with Geoff Judge from iNovia Capital (full disclosure iNovia is one of the investors in Chango). As a VC, Geoff shared his perspectives on big data and opportunities for marketers.

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  • 1. The View from SiliconAlley: A Venture Capitalist’sview of The Evolution of Big Data Geoff Judge Partner, iNovia Capital April 23, 2012
  • 2. 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
  • 3. Early Days of Big Data IBM 360
  • 4. “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).”
  • 5. • Peter Norvig – The Unreasonable Effectiveness of Data More data beats better algorithms!
  • 6. The Effect of Data on accuracy Peter Norvig – The Unreasonable Effectiveness of Data
  • 7. Disruptive Technology• Massively parallel processing• Commodity hardware• Non-relational data models• Columnar data storage• Data compression• Analytics• Visualizations
  • 8. Competitive Advantage
  • 9. 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
  • 10. 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
  • 11. In Ad Tech -Disruptive Solutions for Audience TargetingData Sources: All Anonymous No PI• Web Page Content• Social Networking Connections• Ad Interaction• Search Queries• Mobile Devices• Internet Transactions• Networked Devices and SensorsData 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.
  • 12. 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
  • 13. 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
  • 14. Thank You

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