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}}

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==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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;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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==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. However, correctly interpreting the data validation metric is crucial for the functionality and safety of digital services.<ref>{{Cite journal |last=Vavilis |first=Sokratis |last2=Zannone |first2=Nicola |last3=Petkovic |first3=Milan |date=2013-06 |title=Data reliability in home healthcare services |url=https://ieeexplore.ieee.org/document/6627820/ |journal=Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems |publisher=IEEE |pages=377–380 |doi=10.1109/cbms.2013.6627820}}</ref>

== 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]]