The text discusses the importance of data quality in financial services (FinServ) and the potential risks associated with poor data management. It highlights the need for a unified, metadata-driven architecture that enables context-specific validation at the point of consumption. The AI-powered supergraph is proposed as a solution to address these challenges by federating domains under unified governance, preventing data leakage, leveraging metadata for consistency and evolution, automating data quality validation, transforming quality feedback into actionable intelligence, democratizing access to data and quality insights, and elevating governance through context-aware quality checks. The text also provides practical guidance on getting started with the supergraph architecture, including identifying critical domains, focusing on high-value use cases, implementing, measuring, and iterating, introducing data agents incrementally, and exploring a referenced implementation.