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For business applications, data validation can be defined through [[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> Data that does not conform to these rules will negatively affect business process execution. Therefore, data validation should start with business process definition and set of business rules within this process. Rules can be collected through the requirements capture exercise.<ref>Arkady Maydanchik (2007), "Data Quality Assessment", Technics Publications, LLC</ref>
==Different kinds
In evaluating the basics of data validation, generalizations can be made regarding the different types of validation, according to the scope, complexity, and purpose of the various validation operations to be carried out.
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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. These additional validity constraints may involve cross-referencing supplied data with a known look-up table or directory information service such as [[LDAP]].
For example, an experienced user may enter a well-formed string that matches the specification for a valid e-mail address, as defined in RFC 5322
===Structured check===
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