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Disrupt the static nature of BI with Predictive Anomaly Detection
by Uri Maoz Head of Product and US Business, Anodot
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What is the problem?
Delayed business insights cost companies millions of dollars
Real Time Business Insight
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What is the problem?
While you were
sleeping, Best buy
was selling $200 gift
cards for $15
How one fraud site netted 161 million video ad impressions in one week
Target’s website
misses the mark on
Cyber Monday
NYSE Halts trading for nearly 4 hours
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Getting Business Insights using traditional BI tools? Monitoring Systems?
Maintenance, not
automated
False Positive
No Real time
Millions of metrics
%
0 1 0 1 1 0 1 0 1 0 1 0
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Using Traditional BI tools
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So How do we get Real Time Business Insight?
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How to Track the Millions and Get the Insights?
Automated Anomaly DetectionDisrupting the static nature of BI
Aggregate, Detect, Group and Alert
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Best Buy Example with Anomaly Detection
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What is Anomaly Detection?
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Real Time Alert
1 2 3 4 5
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Detection, scoring and
grouping anomalies
1 2 3 4 5
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Metrics Collection
Metric collection should be Universal and scale to millions of metrics
Number of Purchases
Product Store Geo Device
Revenue
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Detection, scoring and
grouping anomalies
1 2 3 4 5
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Normal Behavior Automatic Learning
Normal Behavior Learning should take into account seasonality, different signal types
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Learning the normal behavior: Not all metrics are created equal
Smooth Irregular sampling
Multi Modal Sparse
Discrete “Step”
Step 1 Classify
Signals to Category
Step 2Match
Category with
Baseline Distribution
and Algorithm
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Metric types distribution
Based on 20,000,000 metrics sampled from dozens of companiesNearly
constant, 2% Discrete,
15%
Sparse, 3%Multi Modal,
5%
Smooth, 38%
Irregular sampling, 37%
All Industrie
s
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Distribution of metric types per industry
Different Industries will have different metric distribution Discrete, 8%
Sparse, 2%Multi Modal,
8%
Smooth, 50%
Irregular sampling, 32%
Ad-Tech
Steps, 2% Discrete, 12%
Sparse, 3%Multi Modal,
3%
Smooth, 33%
Irregular sampling, 47%
E-Commerce
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Static Thresholds versus Anomaly Based Alert
Anomaly Based Alert will find the problems hours before the static based one
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Detection, scoring and
grouping anomalies
1 2 3 4 5
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Abnormal Behavior Learning
Anomaly Score to enable correct prioritization of problems
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Detection, scoring and
grouping anomalies
1 2 3 4 5
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Behavioral Topology Learning and Correlation
Viewing correlated metrics in context enables correct problem identification
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Getting accurate anomalies using anomaly scoring, grouping
Number is average over the past week for all 20,000,000 metrics.
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Automatic Anomaly Detection in five Steps
Metrics Collection – Universal, scale to millions
Normal behavior learning
Abnormal behavior learning
Behavioral Topology Learning
Real Time Alert
1 2 3 4 5
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Problem gets identified in real time
Receiving Real Time correlated
alert enables quick
resolution
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Problem is solved after 30 minutes versus 7 hours
BestBuy fixed the
problem too quickly – I
missed my opportunity
to buy $200 gift card for $15
THANK YOU
