Disrupt the static nature of BI with Predictive Anomaly Detection

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

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Some Anodot Customers…

Uri Maoz, uri@anodot.com

THANK YOU