Big Data for Media. November 20, 2012

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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 Related Work Hardware, Programming Applied AI, CMU, IBM 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 6

7 Flyover Evolution of what s News Source: Monday Note 7

8 Flyover Artist Control 8

9 Flyover Amateur Content Development The Kickstarter model is disrupting the way things are created, produced and initially adopted. 9

10 Flyover Sourcing & Distribution 10

11 Flyover Sourcing & Distribution 11

12 Flyover Data Analysis as Content 12

13 Flyover Mobile 13

14 How Big Data Looks Size Speed Big Data Applications Smörgåsboard Sloppy 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 15

16 Tools are Immature 16

17 New DB Technologies 17

18 New Language Technology 18

19 Data for Targeting & Conversion 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 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 21

22 Data-driven Editorial & Contributions 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 23

24 Getting Started Education 24

25 Getting Started Data Sources 25

26 Getting Started Vendors 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 27

28 28

29 Kris Tuttle (US) +33(0) (France)

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