Roger Magoulas keynote

1. Data and Culture for Publishers
Roger Magoulas
Research Director
O’Reilly Media
roger@oreilly.com

2. Data at O’Reilly
 O’Reilly Research
 Data
– BookScan-based Retail POS Mart (Computers)
– Ebooks / Ecommerce
• O’Reilly only
– Conferences
– Job Post DB
– Facebook and MySpace application usage
– Apple iPhone AppStore ranks
• iBook Ranks since iPad release
– Top Twitter Users and Usage
– US Government Data Analysis
• CTO Jobs Studies / HHS Jobs Trends
 Analysis / Access / Communications
– Research Portal
– Sync’d

3. What’s New in Data
 Stats and Analysis as the ‘sexy’ job of the coming era
 More data, more types of data and big data tools
 Increased skills integration
 Cross-Discipline
 Machine Learning / Natural Language Processing
 O’Reilly Strata Conference

4. The Why of Data
 Tell Stories
– Communicate results
• make vivid, memorable, social
 Input to Decision Processes
– Provide relevant information, not decisions
 Real-Time Integration
– Integrating data / analysis / modeling / predictions into real-
time processes
• Feedback for users
• Self-tuning algorithms stay relevant
– Support database of expectations

5. Data Science
 Data Management  Analysis / Insight
– Loading – Exploration
– Big Data – Visualization
– Parallelism – Collective Intelligence
• Teasing Insights from Crowd Behavior
– Sandboxes
• Crowdsourcing / Mechanical Turks
– Integration with Analysis
– Machine Learning
• Classifying / Deduplication
 Data Collection
• Clustering
– Scraping / Feeds / APIs • Summarizing
– Parsing • Sentiment
• Human Review
 Data Integration – Natural Language Processing
– Identification / Association • Entity Extraction
– Deduplication / Conditioning • Disambiguation
– Statistics / Probability
 Data Organization – Predictive Modeling
 Culture / Organizational Behavior
– Quantitative Culture
– Organize to Learn / Experiment

6. Data Culture at O’Reilly
 Key Components:

7. Data Culture at O’Reilly
 Key Components:

8. Data Culture at O’Reilly
 Key Components:

9. Data Culture at O’Reilly
 Key Components:

10. Data Culture at O’Reilly
 Key Components:

11. Data Culture at O’Reilly
 Key Components:

12. Data Culture at O’Reilly
 Key Components:

13. Taxonomies
 To make sense of data:
– Categorize
– Orthogonal Dimensions
– Hierarchical
• Drill Up / Drill Down
– Dynamic
 BISAC for books
– Not enough for dynamic topics like computers / technology
 Taxonomies are hard!
– Resources, Concentration, Ambiguity, Vigilance, Time,
Madness
– Maintaining Multiple Rollups
– A Messy Process

14. Publishing Data
 Research Portal
– Expose Taxonomy
– Summary / Detail
– Exceptions

15. Research Portal
 Treemap / Topic Summary / Book Detail

16. Publishing Data
 Sync’d Newsletter
– Data + Narrative
– Anomalies
– Special Studies

17. Presenting Data - A Digression
 Magnitude Matters
 Context Matters
87,000
Computer Topic Unit Sales
86,000
85,000
84,000
83,000
82,000
81,000
80,000
79,000
w40 w41 w42 w43 w44 w45 w46

18. Presenting Data - A Digression
 Magnitude Matters
 Context Matters
200,000
Computer Topic Unit Sales
150,000
100,000
50,000
w18 w20 w22 w24 w26 w28 w30 w32 w34 w36 w38 w40 w42 w44 w46

19. – SAS
– Hadoop
External Data - Jobs
 Data Technology
– Machine Learning
20 20
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hadoop
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All Job Trends
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20. – SAS
– Hadoop
External Data - Jobs
 Data Technology
– Machine Learning
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All Job Trends
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21. Publishing Data - Supply Side Analytics Example
 Best Seller Share - Top 5 Books
– Sustained Change Since Holiday Sales Season
– Hypothesis: Less Retail Shelf Space Focuses (Impulse)
Demand to Fewer Titles or Perfect Storm
15%
Top 5 Books % Share
10%
5%
0%
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09
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09
10
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22. Publishing Data - Supply Side Analytics Example
 Best Seller Share - Top 5 Books
– Sustained Change Since Holiday Sales Season
– Hypothesis: Less Retail Shelf Space Focuses (Impulse)
Demand to Fewer Titles or Perfect Storm
15%
Top 5 Books % Share 2009
2010
2011
2012
10%
5%
0%
w01
w03
w05
w07
w09
w11
w13
w15
w17
w19
w21
w23
w25
w27
w29
w31
w33
w35
w37
w39
w41
w43
w45
w47
w49
w51

23. Publishing Data - Supply Side Analytics Example
 Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
50%
Javascript YoY
25%
0%
-25%
big pub huge pub O'Reilly All

24. Publishing Data - Supply Side Analytics Example
 Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• Dominant Share
30
Javascript Books 2010
2011
20
50%
Javascript YoY 10
0
25% 80% big pub huge pub O'Reilly
Javascript Units
60% 2010 share
2011 share
0% 40%
20%
-25%
0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly

25. Publishing Data - Supply Side Analytics Example
 Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
2,000
50% Javascript Units/Book
Javascript YoY
1,500
25%
1,000
0%
500
-25% 0
big pub huge pub O'Reilly All big pub huge pub O'Reilly

26. Publishing Data - Supply Side Analytics Example
 Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
200%
50% Javascript Units/Book
Javascript YoY
150%
25%
100%
0%
50%
-25% 0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly

27. Publishing Data - Supply Side Analytics Example
 Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
– Not Saturated
200%
50% Javascript Units/Book
Javascript YoY
150%
25%
100%
0%
50%
-25% 0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly

28. What Can You Do
 Get Data Savvy
– Find a Ben, Math Club
 Keep Analysis Close to Data
 Go Outside
 Encourage Collaboration / Critical Vetting
– Internal and External
 Experiments as Fundamental Business Process
– New Risk: Measuring cost of what you won’t learn
 Supply-Side Analytics
– Sandbox
 Communicate with Stories
 Scale Up Decision Making to Match Data

29. not a black box

30. How
Not To Be
A Black
Box
Roger Magoulas
of O’Reilly Media

31. not a magic bullet

32. innumeracy

33. hard work

35. enlightenment

36. bliss

38. Quantitative Culture
 Functionally Integrated Teams
– Responsible for all steps of analysis:
• Data Management / Munging
• Analysis / Visualization / Story Telling
 Encourage collaborative development
– Cross-Function Coordination (e.g., via Google Docs)
– Technical Cross-Training
• Use Agile and Extreme Programming Methods
 Share processes, techniques, tool knowledge, results
– Encourage integrated approach
– Open source philosophy
 Experimentation as Fundamental Process
 Supply-Side Analytics
 Analytic Sandbox
– Provide access to large, flexible, high performance data management
systems
 Scale Up Decision Making to Match Data

39. O'Reilly Media
 Publishing / Conferences / On-line / Radar / Research
 Changing the world by spreading the knowledge of
innovators
 We’re essentially story-tellers
 Democratizing Innovation
 “The Future is here, it’s just not evenly distributed”
– William Gibson

40. – Python
– Javascript
External Data - Jobs
 Programming Languages
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All Job Trends
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41. – Java
– Python
– Javascript
External Data - Jobs
 Programming Languages
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2000
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4000
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java
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python
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javascript
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All Job Trends
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