Anomaly detection focuses on identifying unusual patterns, outliers, or unexpected changes in data that may indicate risks, opportunities, or operational issues. Articles under this tag explore statistical methods, machine learning techniques, and AI-driven tools used to detect anomalies in dashboards, reports, and time-series data. Readers will find practical use cases such as fraud detection, performance monitoring, and quality control, along with guidance on implementing anomaly detection within analytics platforms. Use this tag to understand how automated detection enhances insight accuracy and proactive decision-making.