Autonomous Analytics
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Transcript of Autonomous Analytics
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“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
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Historical Real Time Prediction
Trend #2:The need for speed
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Autonomous Analytics
Autonomous analytics enables you to perform any type of analytics (past, real-time and predictive) on practically everything with minimal configuration
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Let’s go through an exampleapplication
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SO YOU’VE CREATED THIS MOBILE APP…
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TIME TO MAKE SOME MONEY
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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/
ONLY 16% OF
USERS WILL TRY A
CRASHING APP
MORE THAN TWICE
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WHAT HAPPENED?
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You can't control what you can't measure.Tom DeMarco in Controlling Software Projects
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WHAT TO MEASURE? MEASURE WHATEVER BROKE
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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
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EACH KPI HAS DOZENS OF OTHERS IT RELATES TO
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SO MANY THINGS CAN CAUSE BREAKDOWNS/ SLOWDOWNS… OR OPPORTUNITIES
Partner integration Data
format
OS updateNew 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
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YOU NEED ANOMALY DETECTION
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AUTOMATED ANOMALY DETECTION
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NORMAL BEHAVIOR LEARNING FOR ANY TIME SERIES
◎ Stationary / non stationary◎ Regularly Irregular
sampling◎ Discrete/Real valued◎ …
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◎ 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
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ABNORMAL BEHAVIORAL LEARNING: RANKING, SCORING
ABNORMAL BEHAVIOR MODELP(ANOMALY SIGNIFICANCE | ANOMALY PATTERN)
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ABNORMAL BEHAVIORAL LEARNING: CLASSIFYING ANOMALIES
TRANSIENT ANOMALY
ANOMALY CLASSIFICATION MODELP(ANOMALY TYPE| ANOMALY PATTERN )
LEVEL CHANGETREND CHANGESEASONAL PATTERN CHANGE
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BEHAVIORAL TOPOLOGY LEARNING
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THE VALUE OF THE STEPS: WEEKLY STATS
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WORKING WITH AN ANOMALY DETECTION SYSTEM
Alert Open Investigation Remediation Alert Close: Back to Normal
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ANOMALY DETECTION SYSTEM ARCHITECTURE
