Statistical anomalies are data points or patterns that deviate significantly from the norm, falling into three categories: point anomaly, contextual anomaly, and collective anomaly. These anomalies can indicate external factors impacting usual operations, requiring further investigation. Effective management of statistical anomalies is crucial for maintaining data integrity and operational efficiency, involving detection, diagnosis, handling, and mitigation. Various techniques are used in statistical anomaly detection, including outlier detection, deviation analysis, pattern recognition, and anomaly scores. These techniques help identify unusual patterns that could indicate potential issues, risks, or opportunities, enabling businesses to take timely action and maintain operational efficiency across different sectors such as finance, healthcare, retail, and cybersecurity. By leveraging anomaly detection with a cutting-edge data observability platform like Acceldata, businesses can gain actionable insights, drive efficiency, and mitigate risks, ultimately transforming their data strategy.