How Netflix developed anomaly detection algorithm which has been applied in multiple contexts Robust to prior anomalies Handle high cardinality dimensions Handles seasonality Handle data which is not always normally distributed Challenge - more anomalies than we can handle from a human perspective

1. GLOBAL SCALE
ANOMALY DETECTION FOR

2. DIRECTOR, DATA ENGINEERING & ANALYTICS
PAUL ELLWOOD

3. 1
INTERNATIONAL
EXPANSION

5. 2
FINITE ANALYST
CAPACITY

6. UNREALISTIC EXPECTATIONS
TEST ANALYSIS DASHBOARD
▸ One test
▸ One report (of the five)
▸ Ignoring multi-select combinations
▸ Ignoring date range selections

7. 16,821,043,200
UNIQUE VIEWS

8. 3
ANOMALY
DETECTION

11. SMARTER ANALYTIC
PRODUCTS

12. SMARTER ANALYTIC PRODUCTS
▸ Statistical Anomalies
▸ Personalized
▸ Filtered & Ranked by Impact
▸ Usable
▸ Ability to Dig Deeper

13. CASE STUDY
PAYMENT PROCESSING
▸ Multiple dimensions
▸ High cardinality dimensions
▸ Hundreds of thousands of time series
▸ Increasing volumes
▸ High business impact

16. QUESTIONS?

17. DIRECTOR, DATA ENGINEERING & ANALYTICS
PAUL ELLWOOD
@pellwood
https://www.linkedin.com/in/pellwood