There is a reason XML, the semantic web, linked data failed to really change the data world, whereas hadoop did. The reason is computation.
The problem isn't data interchange formats and ideal representations, the problem is being able to compute with data. Distributed computation can then be used to solve all the other problems.
Case in point: Slurping data out of databases. Apache Sqoop leverages the primitives provided by Hadoop, in terms of partitioning and fault tolerance, to make it easier to do massive data transfers out of existing databases.
Another example of a solution coming from the hadoop perspective: Avro. It beats the pants of off XML as a data interchange format, precisely because it makes computing with the data (which is the ultimate point) easier.
Now, there is a reason I called Hadoop the integration point. It is becoming a general purpose computation system, which at the same time is also the datawarehouse for organizational data. So rather than dealing with the details of proprietary commercial systems, programmers can target applications to the open-source hadoop ecosystem, and have those solutions be reusable and customizable on a large scale.
The "publishing solution" would of course deal with access control, business logic, freshness, etc. That is exactly what I'm advocating be built.
Individual pieces of data may not be big data, but the aggregate problem still is. In fact this is exactly the Wolfram Alpha case: tons and tons of little datasets that add up to a lot of headache.