Data validation: Difference between revisions

Revision as of 17:41, 2 September 2021

Revision as of 17:44, 2 September 2021

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{{more citations needed|date=November 2016}}

In [[computer science]], '''data validation''' is the process of ensuring [[data]] has 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]], or by the inclusion of explicit [[application program]] validation logic of the computer and its application.

This is distinct from [[Formal_verification#:~:text=In%20the%20context%20of%20hardware,using%20formal%20methods%20of%20mathematics.|formal verification]], which attempts to prove or disprove the correctness of algorithms for implementing a specification or property.

==Overview ==

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==Validation and security==

Failures or omissions in data validation can lead to [[data corruption]] or a [[software security vulnerability|security vulnerability]].<ref>[http://www.cgisecurity.com/owasp/html/ch10.html Chapter10. Data Validation]</ref> Data validation checks that data are fit for purpose,<ref>[https://web.archive.org/web/20171201042621/https://spotlessdata.com/blog/more-efficient-data-validation-spotless More Efficient Data Validation with Spotless]</ref> valid, sensible, reasonable and secure before they are processed. Some risk management practices to protect companies from fraud, reduce operational cost and maintain compliance with varying regulatory policies are [[Know your customer]] (KYC) and [[Customer Identification Program]] (CIP). Know your customer verification is the verification of the customers which is done either before or at the time they started their relationship with a business.

== See also ==