Data validation: Difference between revisions

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{{Short description|The processProcess of ensuring computer data is both correct and useful}}

{{redirect|Input validation||Improper input validation}}

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

In [[computer sciencecomputing]], '''data validation''' or '''input validation''' is the process of ensuring [[data]] has undergone [[data cleansing]] to ensureconfirm theyit havehas [[data quality]], that is, that theyit areis 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]], which attempts to prove or disprove the correctness of algorithms for implementing a specification or property.

==Overview ==

Data validation is intended to provide certain well-defined guarantees for fitness and [[data consistency|consistency of data]] in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts.<ref>[https://ec.europa.eu/eurostat/cros/system/files/methodology_for_data_validation_v1.0_rev-2016-06_final.pdf Methodology for data validation 1.0]</ref> Their implementation can use [[declarative programming|declarative]] [[data integrity]] rules, or [[imperative programming|procedure-based]] [[business rules]].<ref>[http://msdn.microsoft.com/en-us/library/aa291820(VS.71).aspx Data Validation, Data Integrity, Designing Distributed Applications with Visual Studio .NET]</ref>

The guarantees of data validation do not necessarily include accuracy, and it is possible for [[data entry]] errors such as misspellings to be accepted as valid. Other clerical and/or computer controls may be applied to reduce inaccuracy within a system.

==Different kinds==

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* Range and constraint validation;

* Code and cross-reference validation;

* [[Structure validation|Structured validation]]; and

* Consistency validation

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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 type|primitive data types]] as defined in a programming language or data storage and retrieval mechanism.

For example, an integer field may require input to use only characters 0 through 9.

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===Code and cross-reference check===

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Code and cross-reference validation includes operations to verify that data is consistent with one or more possibly-external rules, requirements, or collections relevant to a particular organization, context or set of underlying assumptions. These additional validity constraints may involve cross-referencing supplied data with a known look-up table or directory information service such as [[LDAP]].

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;Referential integrity

:Values in two relational [[database]] tables can be linked through foreign key and primary key. If values in the foreign key field are not constrained by internal mechanisms, then they should be validated to ensure that the referencing table always refers to a row in the referenced table.

;Spelling and grammar check

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;Log of validation

:Even in cases where data validation did not find any issues, providing a log of validations that were conducted and their results is important. This is helpful to identify any missing data validation checks in light of data issues and in improving the validation.

==Validation and security==

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== See also ==

* [[Data processing]]

* [[Data verification]]

* [[Triangulation (social science)]]

* [[Verification and validation]]

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{{DEFAULTSORT:Data Validation}}

[[Category:Data processing]]

[[Category:Data security]]

[[Category:Data quality]]