I am currently involved in a data management program dealing with multi-entity (multi-domain) master data management described here.
Besides covering several different data domains as business partners, products, locations and timetables the data also serves multiple purposes of use. The client is within public transit so the subject areas are called terms as production planning (scheduling), operation monitoring, fare collection and use of service.
A key principle is that the same data should only be stored once, but in a way that makes it serve as high quality information in the different contexts. Doing that is often balancing between the two ways data may be of high quality:
- Either they are fit for their intended uses
- Or they correctly represent the real-world construct to which they refer
Some of the balancing has been:
Customer Identification
For some intended uses you don’t have to know the precise identity of a passenger. For some other intended uses you must know the identity. The latter cases at my client include giving discounts based on age and transport need like when attending educational activity. Also when fighting fraud it helps knowing the identity. So the data governance policy (and a business rule) is that customers for most products must provide a national identification number.
Like it or not: Having the ID makes a lot of things easier. Uniqueness isn’t a big challenge like in many other master data programs. It is also a straight forward process when you like to enrich your data. An example here is accurately geocoding where your customer live, which is rather essential when you provide transportation services.
What geocode?
You may use a range of different coordinate systems to express a position as explained here on Wikipedia. Some systems refers to a round globe (and yes, the real world, the earth, is round), but it is a lot easier to use a system like the one called UTM where you easily may calculate the distance between two points directly in meters assuming the real world is as flat as your computer screen.
Liliendahl on Data Quality 