Step one, monitor all the things. Step two, dedicate 90% of your analytics time and resources to analyzing data, deriving insights, and iterating on what metrics are being monitored and are being optimized. However, there is one small problem. Chances are, the amount of data produced by the instrumentation outpaces your ability to analyze, monitor, and correlate all the variations of the variables at play.This is where an anomaly detection algorithm, backed by a good statistical engine with access to the data can prove invaluable: it does not need to be perfect, but it should be able to alert you to significant outliers in the data. With the