The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.
Administratively incorrect, inconsistent data can lead to false conclusions and misdirect investments on both public and private scales.
For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services.
In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions.
For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers.
Part of the data cleansing system is a set of diagnostic filters known as quality screens.
The latter option is considered the best solution because the first option requires, that someone has to manually deal with the issue each time it occurs and the second implies that data are missing from the target system (integrity) and it is often unclear what should happen to these data.