The trust paradox: Why better data programs are still rebuilding from scratch 

Recent research reveals that even organizations with mature data programs are rebuilding assets instead of reusing them due to a fundamental lack of trust in existing data quality and governance. To scale AI effectively, companies must bridge this "trust gap" by implementing visible signals—such as lineage and quality metrics—that transform static data catalogs into transparent, trustworthy marketplaces.