Data validation: Difference between revisions

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{{Short description|Process 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]] havehas 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>

Note that theThe 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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* [[Check digit]]. To detect transcription errors in which digits have been altered or transposed, the last digit of a pre-2007 ISBN must match the result of a mathematical formula incorporating the other 9 digits ([[International Standard Book Number#ISBN-10 check digits|ISBN-10 check digits]]).

==Validation methodstypes==

;Allowed character checks

:Checks to ascertain that only expected characters are present in a field. For example a numeric field may only allow the digits 0–9, the decimal point and perhaps a minus sign or commas. A text field such as a personal name might disallow characters used for [[Markup language|markup]]. An e-mail address might require at least one @ sign and various other structural details. [[Regular expression]]s can be effective ways to implement such checks.

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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]]