Data Quality Purpose Guide the design and operation of data quality management programs for financial services firms. Covers the six dimensions of data quality (accuracy, completeness, timeliness, consistency, validity, uniqueness) applied to financial data domains, golden source architecture and master data management, data lineage and provenance tracking, validation rule design for security prices, client data, transaction data, and position data, data profiling and anomaly detection, exception management workflows, data quality governance frameworks, and regulatory requirements for data ac…