Even for ad-hoc reporting, the relational model has limits. For some requirements specialized structures are a better choice; thus we have mdolap.
But mdolap is far easier to work with if your feed is from a relational model. Otherwise you are left with a lot of effort getting the data transformed into an appropriate model.
Relational math + fixed schemas means flexible output.
I don't think that overlap is as big as you do.
Any line of business app had better start with a relational model then, as would any case where you are selling business intelligence. The other data stores work best as adjuncts to, rather than replacements for, a relational store.
Edit: Also it occurs to me that MDOLAP is only partly ad hoc. You have to set up your cubes ahead of time and that means deciding on what you are going to report. I imagine that adding a new reporting dimension to a large data set would be painful.
But mdolap is far easier to work with if your feed is from a relational model. Otherwise you are left with a lot of effort getting the data transformed into an appropriate model.
Relational math + fixed schemas means flexible output.
I don't think that overlap is as big as you do.
Any line of business app had better start with a relational model then, as would any case where you are selling business intelligence. The other data stores work best as adjuncts to, rather than replacements for, a relational store.
Edit: Also it occurs to me that MDOLAP is only partly ad hoc. You have to set up your cubes ahead of time and that means deciding on what you are going to report. I imagine that adding a new reporting dimension to a large data set would be painful.