Quality dimensions of a conceptual view

摘要

Data quality is usually associated with the quality of data values. But even perfectly correct data values are of little use if they are based on a deficient data model. The purpose of this paper is to present and discuss a list of characteristics (dimensions) that are crucial for data model quality. We single out 14 quality dimensions, organized into six categories: content, scope, level of detail, composition, consistency, and reaction to change. Two types of correlation among dimensions called “reinforcements” and “tradeoffs” are recognized and discussed as well.