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EventLogging WorkshopDario Taraborelli      Ori Livneh         Maryana Pinchukdario@wikimedia.org   ori@wikimedia.org   mp...
Program1.30-3.00. EventLogging tutorial   • Intro and rationale (Dario)   • From an idea to a data model: Designing a sche...
Service announcementsIRC channel  #wikimedia-e3 @ freenode.netStreaming/recording   Youre on the air (1.30-3pm)  http://ww...
Introduction and rationale
What we do
Typical problemsHow many new users complete a funnel?How many users fail to complete an edit?Is the conversion rate of a n...
open source      usable     scalable   transparent Web analyticsfor rapid prototyping   and A/B testing
High-quality, granular data tomeasure how users interact withfeatures and the UI.         client-side events:             ...
Third-party solutions
Legacy solutions
Legacy solutions
ClickTracking: issues1. scalability
ClickTracking: issues2. flexibility
ClickTracking: issues              2. flexibilityevent namesext.articleFeedbackv5@4-option3-cta_learn_more-button_click-ov...
ClickTracking: issues     3. data modelingNo mechanism for data validation
ClickTracking: issues     3. data modelingLimited support for datadocumentation and provenance
ClickTracking: issues4. data-centric collaboration     Product managers     Data analysts     Engineers     UX designers
EventLogging: design principlesSupport getting desired metrics, not simply all available metricsProvide data provenance/ e...
Next • From an idea to a data model: Designing a schema (Maryana) • Instrumenting code, collecting and validating EventLog...
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EventLogging Workshop

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Intro slides for the EventLogging Workshop, introducing a new infrastructure built by the Wikimedia Foundation for web analytics and collaborative data modeling.

http://www.mediawiki.org/wiki/EventLogging/Workshop

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Transcript of "EventLogging Workshop"

  1. 1. EventLogging WorkshopDario Taraborelli Ori Livneh Maryana Pinchukdario@wikimedia.org ori@wikimedia.org mpinchuk@wikimedia.org Wikimedia Foundation March 7, 2013 http://bit.ly/ELWorkshop
  2. 2. Program1.30-3.00. EventLogging tutorial • Intro and rationale (Dario) • From an idea to a data model: Designing a schema (Maryana) • Instrumenting code, collecting and validating EventLogging data (Ori) • Accessing, QAing and analyzing EventLogging data (Dario)3.00-3.30 Coffee break • Coffee, cookies and snacks3.30-5.00 Hacking session • Hands-on session to learn how to use EventLogging
  3. 3. Service announcementsIRC channel #wikimedia-e3 @ freenode.netStreaming/recording Youre on the air (1.30-3pm) http://www.youtube.com/watch?v=ZBuTjl0lh9YMailing list Sign up at http://bit.ly/EL-News
  4. 4. Introduction and rationale
  5. 5. What we do
  6. 6. Typical problemsHow many new users complete a funnel?How many users fail to complete an edit?Is the conversion rate of a new experimentalfeature significantly higher than a control?What is the breakdown by user segment inthe adoption of a feature?
  7. 7. open source usable scalable transparent Web analyticsfor rapid prototyping and A/B testing
  8. 8. High-quality, granular data tomeasure how users interact withfeatures and the UI. client-side events: clicks impressions bucketing events errors server-side events: transactions
  9. 9. Third-party solutions
  10. 10. Legacy solutions
  11. 11. Legacy solutions
  12. 12. ClickTracking: issues1. scalability
  13. 13. ClickTracking: issues2. flexibility
  14. 14. ClickTracking: issues 2. flexibilityevent namesext.articleFeedbackv5@4-option3-cta_learn_more-button_click-overlayadditional datae90RMgpyyOjKArMiyFUadnI1LgqkPXSU|http://en.wikipedia.org/wiki/Main_Page|Foo
  15. 15. ClickTracking: issues 3. data modelingNo mechanism for data validation
  16. 16. ClickTracking: issues 3. data modelingLimited support for datadocumentation and provenance
  17. 17. ClickTracking: issues4. data-centric collaboration Product managers Data analysts Engineers UX designers
  18. 18. EventLogging: design principlesSupport getting desired metrics, not simply all available metricsProvide data provenance/ enable long-term use of dataSupport data-centric collaboration: using a wiki for data modelingSupport versioning/permit data sets to be comparedSupport transparency/community scrutinyDo not capture every action on site, but rich, well-documented data that fit thequestionEnforce lightweight client and server-side validationCreates a contract between data analyst, product manager, engineer arounddata definitionsSimplify access to data, QA and data analysis
  19. 19. Next • From an idea to a data model: Designing a schema (Maryana) • Instrumenting code, collecting and validating EventLogging data (Ori) • Accessing, QAing and analyzing EventLogging data (Dario)
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