SAP Business Data Cloud and Enterprise AI Data Quality Assurance Framework

Enterprise AI depends on trusted data, but organizations often manage data quality separately from AI assurance and business semantics. SAP Business Data Cloud creates opportunities to unify governed SAP and non-SAP data products, semantic context, analytics, and AI consumption. This paper proposes a SAP Business Data Cloud and Enterprise AI Data Quality Assurance Framework that connects data quality dimensions, semantic governance, lineage, ownership, data contracts, observability, AI readiness, and business outcomes. The framework covers completeness, accuracy, consistency, timeliness, validity, uniqueness, integrity, semantic alignment, and access quality. It defines quality rules, scorecards, certification, issue management, root-cause analysis, AI-use-case thresholds, and continuous monitoring across source, transformation, data-product, semantic, and consumption layers. The proposed approach enables enterprises to improve trust in SAP Business Data Cloud data products and reduce AI errors caused by poor or misunderstood enterprise data.