Data virtualization is an approach that lets applications access and query data from many different sources as if it lived in a single place, without physically moving or copying it into a central store. A virtualization layer sits between the consumers and the sources and resolves queries in real time.
Instead of building pipelines that replicate data, the layer federates queries out to the original systems and combines the results on the fly. For example, a dashboard can join customer records from a CRM with orders from a warehouse without either dataset being duplicated first.
Data virtualization changes how data is accessed, not whether it is ready to use. Whether a virtualized dataset is usable and reproducible inside a specific AI run remains a separate question.