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In evaluating the basics of data validation, generalizations can be made regarding the different types of validation, according to the scope, complexity, and purpose of the various validation operations to be carried out.

For example, many database systems allow the specification of the following primitive data types: 1) integer; 2) float (decimal); or 3) string.

Similarly, telephone numbers are routinely expected to include the digits and possibly the characters (plus, minus, and parentheses).

A more sophisticated data validation routine would check to see the user had entered a valid country code, i.e., that the number of digits entered matched the convention for the country or area specified.

A validation process involves two distinct steps: (a) Validation Check and (b) Post-Check action.

In computer science, data validation is the process of ensuring that a program operates on clean, correct and useful data.

It uses routines, often called "validation rules" "validation constraints" or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system.

The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic.Data validation is intended to provide certain well-defined guarantees for fitness, accuracy, and consistency for any of various kinds of user input into an application or automated system.Data validation rules can be defined and designed using any of various methodologies, and be deployed in any of various contexts.Data validation rules may be defined, designed and deployed, for example: Definition and design contexts: Data that does not conform to these rules will negatively affect business process execution.Therefore, data validation should start with business process definition and set of business rules within this process.Rules can be collected through the requirements capture exercise.