This survey of big data and advanced technology as it relates to media was given to an industry association in November of 2012. Rocket Fuel

1. “Big Data” for Media
November 20, 2012
This presentation was prepared by SoundView Technology Group and is being provided to the Danish Media Association to promote discussion and provide information. All
information contained herein is freely shareable by the association as well as anyone participating in the workshop. Attribution is appreciated but not required.

2. Kris Tuttle
1979-1981
Related Work Hardware, Programming
1981-1992
Applied AI, CMU, IBM
1993-2004
Equity Research, Banking,
Brokerage, Investing
2004-Present
Advisor, Entrepreneur,
Researcher, Publisher,
Programmer
2

3. Eddie Obeng:
After Midnight 3

4. Eddie Obeng:
After Midnight 4

5. Eddie Obeng:
After Midnight 5

6. Discussion Points
• Flyover of recent significant related events and developments
• Unpacking “Big Data” and looking at the pieces
• The intersection of big data and media:
• The obvious – advertising, targeting, conversions
• Data analysis = content?
• Content discovery, generation and ranking
• Hyper-personalization & attenuation
• Wrap up and transition to Q&A
© SoundView Technology Group 2012 6

7. Flyover – Evolution of what’s News
Source: Monday Note
© SoundView Technology Group 2012 7

8. Flyover – Artist Control
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9. Flyover – “Amateur” Content Development
The Kickstarter model is disrupting the way things are created,
produced and initially adopted.
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10. Flyover – Sourcing & Distribution
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11. Flyover – Sourcing & Distribution
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12. Flyover – Data Analysis as Content
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13. Flyover – Mobile
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14. How Big Data Looks
Size Speed
Big Data Applications
Smörgåsboard Sloppy
© SoundView Technology Group 2012 14

15. Unpacking Big Data
• Often not that big in terms of size often less than 1TB
• There is real big data and it’s sometimes enormous ~20 PB/day
• Tends to have additional special features:
o Multiple sources – transactional, log files, databases, geospatial,
o Semi or unstructured – clickstreams, html, sensor data
o Shorter duration – seconds to days versus weeks to months
o More flexible – sometimes scheme-less, and built for speed
• Today the default technology for most projects is Hadoop
• Some machine learning is becoming a common feature
© SoundView Technology Group 2012 15

16. Tools are Immature
© SoundView Technology Group 2012 16

17. New DB Technologies
© SoundView Technology Group 2012 17

18. New Language Technology
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19. Data for Targeting & Conversion
© SoundView Technology Group 2012 19

20. Data for Targeting & Conversion
• Integrating geospatial data
for local advertising
• Intersecting social data with
news and search for
customization
• Going beyond “A/B
testing” and using real-time
data analysis to improve
content on the fly
• Saved data opens the door
to machine learning and
better algorithms
© SoundView Technology Group 2012 20

21. Data Analysis as Content
• Increased building of
proprietary data and surveys
– no brainer
• In-house data scientists and
coding capabilities will help
• There are quite a few good
text analysis and processing
tools out there – NLTK,
Python
• NoSQL and Network DB
tecnology can help
© SoundView Technology Group 2012 21

22. Data-driven Editorial & Contributions
© SoundView Technology Group 2012 22

23. Using Data to Hyper-Personalize
• The future of reader relationships is about more than content quality
– readers will grow to expect highly personalized content
• Managing, distributing and “learning” from collected data requires a
much more sophisticated view of how content is represented, managed
and distributed
© SoundView Technology Group 2012 23

24. Getting Started – Education
© SoundView Technology Group 2012 http://bigdatauniversity.com/ 24

25. Getting Started – Data Sources
© SoundView Technology Group 2012 25

26. Getting Started – Vendors
© SoundView Technology Group 2012 26

27. Getting Hygge with Big Data
• We are still in the very early days of implementation – nothing is precluded
• Appoint or hire a “data czar” - more than a point person, the one who can
translate the technology into implementation opportunities
• For each line of business map revenue growth and profit margins to variables
that might be improved with more data and better analysis
• In-house custom coding capability is a strategic advantage – data integration
remains a big challenge
• Start building some proprietary data – surveys, histories, aggregations
• Are any ideas worth implementing in the local market? (Zite, HuffingPost,
NYT/Nate Silver)
• Experiment, experiment, experiment
© SoundView Technology Group 2012 27

28. 28

29. Kris Tuttle
kris@soundviewadvisory.com
+1-617-934-1877 (US)
+33(0)6.7439.8593 (France)