In a data warehouse, information from multiple operational sources is consolidated, transformed, and related through keys, joins, and business rules. When a Business Analyst defines processes and procedures that describehow data sets interrelate, they are primarily controlling the risk created bydata aggregation. Aggregation risk arises when combining multiple datasets produces a new, richer dataset that can change the meaning, sensitivity, or trustworthiness of the information. If relationships and transformation rules are poorly defined or inconsistently applied, the warehouse can generate misleading analytics, incorrect roll-ups, duplicated records, or invalid correlations—directly harminginformation integritybecause decisions are made on inaccurate or improperly combined data.
Well-defined interrelation procedures specify authoritative sources, master data rules, key management, referential integrity expectations, transformation and reconciliation steps, and data lineage. These controls help ensure the warehouse preserves correctness when data is integrated across systems with different formats, definitions, and update cycles. They also support governance by enabling validation checks (for example, balancing totals to source systems, exception handling, and data-quality thresholds) and by making it clear which dataset should be trusted for specific attributes.
Unauthorized access and confidentiality are important warehouse risks, but they are addressed mainly through access controls and encryption. Cross-site scripting is a web application vulnerability and is not the core issue in describing dataset relationships. Therefore, the correct answer isData Aggregation.
