Big Data in Media

“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.

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1979-1981 Hardware, Programming

1981-1992

Applied AI, CMU, IBM

1993-2004 Equity Research, Banking,

Brokerage, Investing

2004-Present Advisor, Entrepreneur, Researcher, Publisher,

Programmer

Kris Tuttle

Related Work

http://www.autonomy.com/

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Eddie Obeng: After Midnight

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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

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Flyover – Evolution of what’s News

Source: Monday Note


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Flyover – Artist Control

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Flyover – “Amateur” Content Development

The Kickstarter model is disrupting the way things are created, produced and initially adopted.

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Flyover – Sourcing & Distribution

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Flyover – Sourcing & Distribution

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Flyover – Data Analysis as Content

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Flyover – Mobile

Big Data Applications

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How Big Data Looks


Smörgåsboard

Size Speed

Sloppy

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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

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Tools are Immature


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New DB Technologies


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New Language Technology


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Data for Targeting & Conversion


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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


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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


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Data-driven Editorial & Contributions


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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


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Getting Started – Education

© SoundView Technology Group 2012 http://bigdatauniversity.com/

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Getting Started – Data Sources


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Getting Started – Vendors


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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

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Kris Tuttle

[email protected]

+1-617-934-1877 (US)

+33(0)6.7439.8593 (France)

mailto:[email protected]