When working with data quality and master data management at the same time you are constantly met with the challenge that data quality is most often defined as data being fit for the purpose of use, but master data management is about using the same data for multiple purposes at the same time.
Finding the right solution to such a challenge within an organization isn’t easy, because it despite all good intentions is difficult to find someone in the business with an overall answer to that kind of problems as explained in the blog post by David Loshin called Communications Gap? Or is there a Gap between Chasms?
An often used principle for overcoming these issues may be seen as “survival of the fittest”. You negotiate some survivorship rules between “competing” data providers and consumers and then the data being the fittest measured by these rules wins. All other data gets the KISS of death. Most such survivorship rules are indeed simple often based on a single dimension as timeliness, completeness or provenance.
Recently the phrase “survival of the fittest” in evolution theory has been suggested to be changed to “survival of the fit enough” because it seems that many times specimens haven’t competed but instead found a way into empty alternate spaces.
It seems that master data management and related data quality is going that way too. Data that is fit enough will survive in the master data hub in alternate spaces where the single source of truth exists in perfect symbioses with multiple realities.