Hi there @ewan_klein
Yes, we have done lots of work on Data Quality, mostly via our Frictionless Data specifications and related tools.
In particular, we have a Python library called goodtables that produces detailed, granular quality reports from tabular data, or collections of tabular data (e.g.: we can scan a public CKAN API and build quality reports for every tabular data file published on the instance).
We are about to launch a full continuous data validation service called goodtables.io that makes this trivially easy to do.
The Data Quality Spec has been extracted out of our work on goodtables to enable reuse, and we've got a beta version of a Data Quality Dashboard that can display quality results for large sets of data (think: visual interaction with quality results for each tabular data file on a CKAN instance, or any other public data collection).