In community economics you have two terms called
- Partitive accuracy and
- Holistic accuracy
In short, partitive accuracy is the accuracy of a single measure being part of a model while holistic accuracy is the accuracy of the model structure and its use. More information here.
I find these terms being very useful in data quality and master data management as well.
The distinction between partitive accuracy and holistic accuracy resembles the distinction between data quality and information quality.
One problem with the term information quality is that it implies a certain context of use, which makes it hard to prepare data for having high data quality for multiple uses other than assuring the accuracy of the single data elements – being similar to the term partitive accuracy.
One clue for assuring better information quality is looking at the model structure of data – being similar to the term holistic accuracy. Here I am thinking beyond traditional data modeling, which is anchored in the technical world, and into how end users of master data hubs are able to build structures of data (with partitive accuracy) that fits the daily business use.
Examples of such holistic information capabilities in master data management will be building flexible product hierarchies and hierarchies of party master data that at the same time reflects hierarchies in the real world as households and company family trees and hierarchies of related accounts and addresses used within the enterprise.
While a single data element as an address component like a postal code may be partitive accurate, the holistic accuracy is seen as how data elements contribute to a holistic accuracy as a part of a data structure that fits multiple purposes of use.