Outlier detection helps companies determine when something changes in their normal business patterns. When done well, it can give a company the insight it needs to investigate the root cause of the change, make decisions, and take actions that can save money (or prevent losing it) and potentially create new business opportunities. High-velocity online businesses need real-time outlier detection; waiting for days or weeks after the outlier occurs is simply too late to have a material impact on a fast-paced business. This puts constraints on the system to learn to identify outliers quickly, even if there are a million or more relevant metrics and the underlying data patterns are complicated. Automated outlier detection is a technique of machine learning, and it is a tremendously complex endeavor. In this series of white papers, Anodot aims to help people understand some of the sophisticated decisions behind the algorithms that comprise an automated outlier detection system for large scale analytics, especially in the monitoring of time series data.