Explain filtering data and validating data

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The difference between data validity and accuracy can be illustrated with a trivial example.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.For example, a US phone number should have 10 digits and no letters or special characters.Code and cross-reference validation includes tests for data type validation, combined with one or more operations to verify that the user-supplied data is consistent with one or more external rules, requirements, or validity constraints relevant to a particular organization, context or set of underlying assumptions.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.

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.For example: Data type validation is customarily carried out on one or more simple data fields.The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types; as defined in a programming language or data storage and retrieval mechanism as well as the specification of the following primitive data types: 1) integer; 2) float (decimal); or 3) string.An e-mail address might require at least one @ sign and various other structural details.Regular expressions are effective ways of implementing such checks.

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