The Data Vault methodology also addresses a common limitation that relates to the dimensional model approach. There are many good things to say about dimensional modelling, it’s a perfect fit for doing analytics, it’s easy for business analysts to understand, it’s performant over large sets of data, the list goes on.
That said, the data vault methodology addresses the limitations of having a “fixed” model. Dimensional modelling’s resilience to change or “graceful extensibility”, as some would say, is well documented. It’s capable of handling changing data relationships which can be implemented without affecting existing BI apps or query results. For example, facts consistent with the grain of an existing fact table can be added by creating new columns. Moreover, dimensions can be added to an existing fact table by creating new foreign key columns, presuming they don’t alter the fact table’s grain.
The most interesting thing to me about Data Vault is that it’s very popular in Europe and almost unheard-of in North America. That’s the impression I get, at least.