Thus a Data Mart corresponds to a statistical experimental population. While it could be done, it does not appear to be common to use sampling or statistical modeling such as factor analysis to build a Data Mart. A potentially useful tactic, at both the Mart and Package level, would be to maintain a random sample of data for exploratory studies and to test analytic code. Data Packages must be condensed considerably to fit into the constraints of workstation technology. Analysts will need to use sampling strategies as well as analytic techniques to produce more parsimonious representations of relevant phenomena.
Most companies maintain a small amount of high quality data and a large amount of poor data.
A lot of poor data does not generate good information. In statistics it is recognized that often a sample is better than the population. A good quality, representative sample is often much more useful than poor quality total database. While it is usually not feasible to edit and clean an entire warehouse database, it is often possible to do so with a sample. The data deposit is intended to be as complete as possible and serves as a sample frame from which the Mart is generated.