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The rules may be implemented through the automated facilities of a data dictionary, 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.
For example, many database systems allow the specification of the following l (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.
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.
An e-mail address might require at least one @ sign and various other structural details.
Regular expressions are effective ways of implementing such checks.
This can only be achieved through the use of all the clerical and computer controls built into the system at the design stage.
For example a numeric field may only allow the digits 0–9, the decimal point and perhaps a minus sign or commas.
A text field such as a personal name might disallow characters such a markup-based security attack.
Structured validation allows for the combination of any of various basic data type validation steps, along with more complex processing.
Such complex processing may include the testing of conditional constraints for an entire complex data object or set of process operations within a system.