Building a Monitoring System That Actually Works

Building an effective monitoring system for key performance indicators (KPIs) is crucial for ensuring product health and requires balancing sensitivity to avoid both false positives and negatives. A practical statistical approach involves using historical data to calculate a confidence interval based on the mean and standard deviation. Key parameters like the time window, historical days used for comparison, and the coefficient for the standard deviation must be carefully chosen to fit the data's seasonality. The guide also reviews various out-of-the-box frameworks like Prophet and Merlion and demonstrates the statistical method with a Python example using NYC taxi ride data.