Disrupt the static nature of BI with Predictive Anomaly Detection
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The static nature of BI today results in business insight latency, that cost companies millions of dollars. Data-centric companies like web-based businesses, digital advertising, fintech and IoT need real time business detection to optimize their business performance. In this presentation, Nir Kalish, Sr. Director of Solution Engineering, explains how this can be achieved using Predictive Anomaly Detection. Presented at ODSC West, November 2016.
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Disrupt the static nature of BI with Predictive Anomaly Detection
- 2. Disrupt the static nature of BI with Predictive Anomaly Detection by Nir Kalish Senior Director, Solution Engineering, Anodot
- 3. What is the problem? Delayed business insights cost companies millions of dollars Real Time Business Incident Detection 3
- 4. Business Incidents Use Cases 4
- 5. Getting Business Insights using traditional BI tools? Monitoring Systems? Maintenance, not automated False Positive No Real timeMillions of metrics % 0 1 0 1 1 0 1 0 1 0 1 0 5
- 6. 6 Find the Anomaly… Using traditional BI tools Dashboards
- 7. So How do we get Real Time Business Insight? 7
- 8. How to Track the Millions and Get the Insights? Automated Anomaly Detection Disrupting the static nature of BI Aggregate, Detect, Group and Alert 8
- 9. 9 What is Anomaly Detection?
- 10. Automatic Anomaly Detection in five Steps Metrics Collection – Universal, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback Based Learning 1 2 3 4 5 10
- 11. Automatic Anomaly Detection in five Steps Metrics Collection – Granular, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback Based Learning 1 2 3 4 5 11
- 12. Anomaly Detection in every granularity Number of Purchases Product Categor y Geo Device OS Revenue 12 $ gift card TV model Phone model Gift cards Cell Phones Electronics US EMEA APJ iOS Windows Android
- 13. Large Scale Anomaly Detection System Architecture Kafka Events Queue Anomaly Grouping Signals Correlation Map Real-Time Rollups Store Cassandra Anodotd REST WebApp Online Base Line Learning Aggregator Elasticsearch DWH S3 HADOOP/ Spark HIVE Offline Learning Management & Portal Anodot-Web User Mgmt RDBMS Customer DS Agent • 5.4 billion daily samples • 120,000,000 metrics • 240,000,000 models • updated with each sample • 500,000,000 correlation links • Updated daily • 14,000,000 seasonal models • Updated daily • 30 types of learning algorithms • Metric classification, seasonality detection, trend, baseline models, clustering algos, LSH, … • And counting…
- 14. Automatic Anomaly Detection in five Steps Metrics Collection – Universal, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback based learning 1 2 3 4 5 14
- 15. Static Thresholds versus Anomaly Based Alert Anomaly Based Alert will find the problems hours before the static based one 15
- 16. Normal Behaviour Modelling: Not so Simple… 16 Signal TypeSeasonal pattern Adapt to changes
- 17. 17 Seasonality
- 18. Learning the normal behavior: Not all signals are created equal Smooth Irregular sampling Multi Modal Sparse Discrete “Step” 18 Step 1 Classify signal to Category Step 2 Match Category with Baseline Distribution and Algorithm
- 20. Distribution of metric types per industry 20
- 21. Update rate with adaptive online models: Avoiding pitfalls 21 What should be the learning rate? Too Slow Too Fast
- 22. Update rate with adaptive online models: Avoiding pitfalls 22 What should be the learning rate? “Al Dente” Auto tuning required!
- 23. Automatic Anomaly Detection in five Steps Metrics Collection – Universal, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback Based Learning 1 2 3 4 5 23
- 24. Abnormal behavior model P(Anomaly Significance | Duration, Deviation) 90 70 50 30 2010 Abnormal Behaviour Learning
- 25. Abnormal Behaviour Learning Anomaly Score to enable correct prioritization of problems 25
- 26. Automatic Anomaly Detection in five Steps Metrics Collection – Universal, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback Based Learning 1 2 3 4 5 26
- 27. Behavioral Topology Learning and Correlation Viewing correlated metrics in context enables correct problem identification 27 Price glitch – increase in purchases / decrease in revenue
- 28. Automatic Anomaly Detection in five Steps Metrics Collection – Universal, scale to millions Normal behavior learning Abnormal behavior learning Behavioral Topology Learning Feedback Based Learning 1 2 3 4 5 28
- 29. Feedback Based Learning Enables continuance improvement of the machine learning process 29
- 30. Weekly anomaly stats: The importance of all steps Based on 120,000,000 metrics Normal behavior learning Abnormal behavior learning Behavioral Topology Learning
- 31. 31 Anomaly Detection is every where Social Fintech IT Ad-Tech E-commerce IOT Business Incident Detection
- 32. 32 Current Anodot Customers – Partial List - Pedro Silva, Senior product, Credit Karma It used to take us up to several days to identify an issue on a specific page, offer, or service that was draining our revenues. Anodot identifies when a metric increases or decreases in real time, so we can resolve it quickly, before business suffers or revenue is lost.
- 33. THANK YOU
Hinweis der Redaktion
- Focus on Metric collection
- Talking points: Ad tech tends to have KPIs that measure all the time and are smoother ---- a lot less irregular. Probably due to much heavier activity (with exchanges and ads – and end users that are a lot more). E-commerce KPIs are driven by users in the site/mobile/app --- so tends to be more of metrics that are not “smooth”. (17% difference from ad-tech and 5% less than all companies. Both have at least 50% metrics that are not “smooth” but require various other algos.