Control charts, developed by Dr. Walter Shewhart in the 1920s, are utilized in manufacturing and business processes to detect when a process goes "out of control" by identifying deviations from the mean beyond normal variation. These charts are simple and effective tools for anomaly detection, and their modern implementation can be enhanced using Elasticsearch's pipeline aggregations. The text demonstrates how to smooth data using moving averages, specifically an Exponentially-Weighted Moving Average (EWMA), to better visualize trends and detect anomalies, such as a sudden spike in data. By incorporating a moving average on both the mean and standard deviation, a control chart can dynamically set an "upper control limit," allowing real-time detection of outliers that could indicate a problem, such as in the hypothetical scenario of monitoring a nuclear reactor's coolant temperature. The text also hints at further applications of control charts for more complex data patterns, such as linear trends and cyclic behavior, and suggests future integration with Watcher for automated alerts.