  Data validation is the activity where one decides whether or not a particular
data set is fit for a given purpose. Formalizing the requirements that drive
this decision process allows for unambiguous communication of the requirements,
automation of the decision process, and opens up ways to maintain and
investigate the decision process itself. The purpose of this article is to
formalize the definition of data validation and to demonstrate some of the
properties that can be derived from this definition. In particular, it is shown
how a formal view of the concept permits a classification of data quality
requirements, allowing them to be ordered in increasing levels of complexity.
Some subtleties arising from combining possibly many such requirements are
pointed out as well.
