Validity refers to
|“||whether the data actually represent what you think is being measured. For example, if we are interested in analyzing job performance and a field in the database is labeled “annual evaluation score,” we need to know whether that field seems like a reasonable way to gain information on a person’s job performance or whether it represents another kind of evaluation score.||”|
U.S. patent law
See Patent validity.
"Traditionally, this has been referred to as data quality. In the Big Data security scenario, validity refers to a host of assumptions about data from which analytics are being applied. For example, continuous and discrete measurements have different properties. The field gender can be coded as 1=Male, 2=Female, but 1.5 does not mean halfway between male and female. In the absence of such constraints, an analytical tool can make inappropriate conclusions. There are many types of validity whose constraints are far more complex. By definition, Big Data allows for aggregation and collection across disparate datasets in ways not envisioned by system designers."