The only way to ensure that aggregate information in which to make critical decisions upon is accurate and precise is through semantic quality.
The silos of data quality metrics across individual systems and their applications present a usable, if not blinkered view but an omission or misstatement may be considered material. Whilst approximations are part and parcel of risk reporting and risk management,
a higher level of confidence, both qualitative and quantitative is needed to make critical decisions about risk.
The reconciliation of data quality across our disparate, loosely coupled systems naturally leads into the question of aggregation in which to mitigate and manage any omission or misstatement quickly, concisely and effectively.
The natural fit for data quality aggregation is against your semantic layer and the measure of semantic quality becomes a powerful holistic enabler, across all your financial domains.