Gartner (the analyst firm), represented by Saul Judah, takes data quality back to basics in the recent post called Data Quality Improvement.
While I agree with the sentiment around measuring the facts as expressed in the post I have cautions about relying on that everything is good when data are fit for the purpose for business operations.
Some clues lies in the data quality dimensions mentioned in the post:
Accuracy (for now):
As said in the Gartner post data are indeed temporal. The real world changes and so does business operations. When you got your data fit for the purpose of use the business operations has changed. And when you got your data re-fit for the new purpose of use the business operations has changed again.
Furthermore most organizations can’t take all business operations into account at the same time. If you go down the fit for purpose track you will typically address a single business objective and make data fit for that purpose. Not at least when dealing with master data there are many business objectives and derived purposes of use. In my experience that leads to this conclusion:
“While we value that data are of high quality if they are fit for the intended use we value more that data correctly represent the real-world construct to which they refer in order to be fit for current and future multiple purposes”
Existence – an aspect of completeness:
The Gartner post mentions a data quality dimension being existence. I tend to see this as an aspect of the broader used term completeness.
For example having a fit for purpose completeness related to product master data has been a huge challenge for many organizations within retail and distribution during the last years as explained in the post Customer Friendly Product Master Data.