Hi Leigh,

I have created some linked data statistical datasets though, and tried
to think of useful approaches, but  I  don't think I've ever written
an app that consumed Data Cube data. So these are just my opinions
from that perspective:

The verbosity of RDF for representing multi-dimensional data makes me
think that RDF stores might not be the most efficient way of storing
and querying large volumes of stats, so I think expectation of a
SPARQL endpoint might be lower (though it's possible something like
D2RQ might work better?).

When (at Talis) we had to produce a 'triplification' of the European
Central Bank stats for the LATC project for instance, we decided that
doing a complete conversion and putting it into a triple store would
be completely impractical, even with the resources we had available to
us. We hit upon a hybrid approach of making the metadata about the
datasets (and perhaps some geo data) available through SPARQL + Linked
Data API, and making  URIs of the datasets themselves deref to a
script that fetched the original data and converted it to RDF on the
fly.

basic Linked Data  - dereffing CBDs of all the observations for
instance, could be pretty tiresome.
SPARQL is more efficient in that regard, but it would be nice to
combine the fyn discovery of linked data, and SPARQL's ability to
slice in different ways and calculate aggregates.
Linked Data API can let you provide these different views, but, for
statistical data, it would be nice to be able to also have calculated
properties - the average, max, min values for a slice (for instance) -
the areas dimension with the highest/lowest value for a
measureproperty and time dimension - that kind of thing.

At root, statistics are interesting because they let you compare
things, so I think there's a lot of value in trying to surface and
facilitate those comparisons in their publication.

Best

Keith