1. Autonomous analytics enables analyzing past, real-time, and predictive analytics on large amounts of data with minimal configuration. 2. An example application is given of a mobile app developer who wants to measure app usage and performance to understand why users started uninstalling the app. 3. Automated anomaly detection learns normal behavioral patterns and can detect and classify different types of anomalies, helping app developers identify and address issues quickly to improve the user experience.

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Autonomous Analytics
Ira Cohen, Chief Data Scientist
ira@anodot.com

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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 example
application

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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/

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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 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

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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
◎ …
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

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ABNORMAL BEHAVIORAL LEARNING: RANKING, SCORING
ABNORMAL
BEHAVIOR MODEL
P(ANOMALY SIGNIFICANCE | ANOMALY PATTERN)

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ABNORMAL BEHAVIORAL LEARNING:
CLASSIFYING ANOMALIES
TRANSIENT ANOMALY
ANOMALY CLASSIFICATION MODEL
P(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

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THANK YOU
www.anodot.com
Ira Cohen, Chief Data Scientist
ira@anodot.com