Data Quality Assessment (DQA) Results as Decision-Relevant Enriched Metadata for Decision Support Systems

Trustworthy and impactful decision support systems (DSS) critically depend on data that are fit for their intended use. Here, data quality assessments should be understood not merely as a validation step, but as a systematic metadata enrichment process that produces structured, machine-actionable descriptors of data plausibility, and correctness. Building on established data quality concepts in medical research, this talk generically conceptualises data quality assessment as a crucial pre-assessment layer that is analytically distinct from decision logic and compares distinct approaches to handle metadata.