Data completeness is an important aspect of data quality, which refers to the absence of missing information in a dataset. It has significant implications for business operations and decision-making processes. Incomplete data can lead to missed opportunities or incorrect conclusions that could negatively impact the organization. Ensuring data completeness involves measuring it against a complete mapping, tracking null values, satisfying constraints, and validating input mechanisms. Anomaly detection is one method to identify missing data in real-time, helping organizations maintain high levels of data quality.