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Autonomous Analytics

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Presented by Ira Cohen, Chief Data Scientist at Anodot, at the "Finding the needle in the haystack" Meetup in San Jose at the PayPal headquarters. The Meetup featured presentations from PayPal, Uber and Anodot. Participants learned about how anomaly detection automatically surfaces insights from huge amounts of data with high dimensionality and drives better and faster decision making.

Publicado en: Datos y análisis
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Autonomous Analytics

  1. 1. 1 Autonomous Analytics Ira Cohen, Chief Data Scientist ira@anodot.com
  2. 2. 2 “There were 5 exabytes of information created by the entire world between the dawn of civilization and 2003. Now That same number is created very two days.” Trend #1: Information Overload
  3. 3. 3 Historical Real Time Prediction Trend #2: The need for speed
  4. 4. 4 Autonomous Analytics Autonomous analytics enables you to perform any type of analytics (past, real- time and predictive) on practically everything with minimal configuration
  5. 5. 5 Let’s go through an example application
  6. 6. 6 SO YOU’VE CREATED THIS MOBILE APP…
  7. 7. 7 TIME TO MAKE SOME MONEY
  8. 8. 8 SOMETHING BROKE… TOO MANY PEOPLE STARTED UNINSTALLING https://techcrunch.com/2013/03/12/users-have- low-tolerance-for-buggy-apps-only-16-will-try-a- failing-app-more-than-twice/
  9. 9. 9 WHAT HAPPENED?
  10. 10. 10 You can't control what you can't measure. Tom DeMarco in Controlling Software Projects
  11. 11. 11 WHAT TO MEASURE? MEASURE WHATEVER BROKE
  12. 12. 12 KPIS CAN BE GROUPED per app, ad campaign, partners/affilates, store items, cross promotion… Per Geo, user segment, game,… Per Device Type, OS version, network,… BUSINESS: REVENUE BUSINESS GENERATION: DAU, MAU, RETENTION RATES APPLICATION : CRASHES, PERFORMANCE, ERRORS, USABILITY
  13. 13. 13 EACH KPI HAS DOZENS OF OTHERS IT RELATES TO
  14. 14. 14 SO MANY THINGS CAN CAUSE BREAKDOWNS/ SLOWDOWNS… OR OPPORTUNITIES Partner integration Data format OS update New devices Competitor bid strategy Media coverage Social media exposure New version deployment New game release New campaign type AB Tests PARTNER CHANGES DEVICE CHANGES OTHER EXTERNAL CHANGES COMPANY CHANGES
  15. 15. 15 YOU NEED ANOMALY DETECTION
  16. 16. 16 16 AUTOMATED ANOMALY DETECTION
  17. 17. 17 NORMAL BEHAVIOR LEARNING FOR ANY TIME SERIES ◎ Stationary / non stationary ◎ Regularly Irregular sampling ◎ Discrete/Real valued ◎ … 17 ◎ Single/Mixture models ◎ Symmetric/non-symmetric ◎ Continuous/discrete ◎ … ◎ Seasonal/non seasonal ◎ Single/multiple seasonal patterns ◎ Additive/Convolutional multi- seasonal patterns ◎ Optimal adaptation during normal times ◎ Optimal adaptation during anomalies ◎ Optimal adaptation following anomalies ADAPTATION
  18. 18. 18 ABNORMAL BEHAVIORAL LEARNING: RANKING, SCORING ABNORMAL BEHAVIOR MODEL P(ANOMALY SIGNIFICANCE | ANOMALY PATTERN)
  19. 19. 19 ABNORMAL BEHAVIORAL LEARNING: CLASSIFYING ANOMALIES TRANSIENT ANOMALY ANOMALY CLASSIFICATION MODEL P(ANOMALY TYPE| ANOMALY PATTERN ) LEVEL CHANGETREND CHANGESEASONAL PATTERN CHANGE
  20. 20. 20 BEHAVIORAL TOPOLOGY LEARNING
  21. 21. 21 THE VALUE OF THE STEPS: WEEKLY STATS
  22. 22. 22 WORKING WITH AN ANOMALY DETECTION SYSTEM Alert Open Investigation Remediation Alert Close: Back to Normal
  23. 23. 23 ANOMALY DETECTION SYSTEM ARCHITECTURE
  24. 24. 24 THANK YOU www.anodot.com Ira Cohen, Chief Data Scientist ira@anodot.com

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