Validating data in datagridview

An e-mail address might require at least one @ sign and various other structural details.

Regular expressions are effective ways of implementing such checks.

(See also data type checks below)Checks for missing records.

Numerical fields may be added together for all records in a batch.

A company has established a Personnel file and each record contains a field for the Job Grade. An entry in a record may be valid and accepted by the system if it is one of these characters, but it may not be the correct grade for the individual worker concerned.

Whether a grade is correct can only be established by clerical checks or by reference to other files.

For example a numeric field may only allow the digits 0–9, the decimal point and perhaps a minus sign or commas.

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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.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.This can only be achieved through the use of all the clerical and computer controls built into the system at the design stage.The difference between data validity and accuracy can be illustrated with a trivial example.For example, an experienced user may enter a well-formed string that matches the specification for a valid e-mail address, as defined in RFC 5322 but that well-formed string might not actually correspond to a resolvable domain connected to an active e-mail account.Structured validation allows for the combination of any of various basic data type validation steps, along with more complex processing.In computer science, data validation is the process of ensuring that data have undergone data cleansing to ensure they have data quality, that is, that they are both correct and useful.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, 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.

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