Hidden patterns in data can often go undetected by traditional validation approaches, but anomaly detection can reveal subtle clues that signal deeper issues. Anomaly detection at the egress point, where data flows to decision-makers, presents unique challenges and opportunities, including maximum business impact, rich context availability, and cross-system pattern recognition. Data composition capabilities have made these patterns more common and critical to detect, and modern low-data-movement tools provide unprecedented visibility into novel combinations. Building adaptive systems with anomaly detection as a continuous feedback mechanism can identify semantic inconsistencies, temporal anomalies, business rule violations, and data quality issues. The AI dimension brings new challenges, such as integrating data from across the enterprise and maintaining high-quality standards for GenAI initiatives. Ultimately, effective anomaly detection requires clear ownership and collaboration across the organization, progressive disclosure principles, and human expertise in data governance and critical thinking.