Enabling AI-Driven Interoperability with Data Vault

Join us for a thought-provoking session with Dr. Peter Aiken as he frames AI-driven interoperability as a data-integration and data-management problem rather than a tool-only problem. Its central message is that enterprise AI needs context: shared understanding across business, technical, and system communities; managed metadata; architected cloud data; and a data warehouse layer that is structured enough to support analysis and AI use. Peter repeatedly identifies metadata as the bridge from data to meaning, shifting metadata from a narrow “technical detail” view toward a common vocabulary that provides context and clarity for people and AI.
For Data Vault enthusiasts, the strong alignment with Data Vault 2.1 is the emphasis on integration at the lowest workable level of granularity, preservation of business meaning, data architecture before platform migration, metadata management, lineage, impact analysis, and reducing ambiguity across sources. These themes are pertinent to Data Vault 2.1 because the methodology depends on stable business keys, source traceability, raw data capture, auditable historization, consistent naming, and the separation of raw integration from downstream business-rule interpretation. The presentation does not teach Data Vault 2.1 in detail; it uses Data Vault as one implementation pattern within a broader data-integration story.