Data verification: Difference between revisions

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'''Data verification''' is a process in which different types of data are checked for accuracy and [[data consistency|inconsistencies]] after [[data migration]] is done.<ref>{{Cite web|url=http://www.datacap.com/products/features/verify/|title=What is Enterprise Content Management}}</ref> In some domains, it is referred to Source Data Verification (SDV), such as in [[clinical trials]].<ref name="pmid25327707">{{cite journal| author=Andersen JR, Byrjalsen I, Bihlet A, Kalakou F, Hoeck HC, Hansen G | display-authors=etal| title=Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials. | journal=Br J Clin Pharmacol | year= 2015 | volume= 79 | issue= 4 | pages= 660–8 | pmid=25327707 | doi=10.1111/bcp.12531 | pmc=4386950 }} </ref>

Data verification helps to determine whether data was accurately translated when data is [[data transfer|transferred]] from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous [[data loss]].<ref>{{Cite web |date=2022-07-19 |title=Data Verification and Data Validation Techniques |url=https://leadgenspecialists.co.uk/ |access-date=2023-04-17 |website=Intone Networks |language=en-US}}</ref>

Methods for data verification include [[double data entry]], [[proofreading]] and automated verification of data. Proofreading data involves someone checking the data entered against the original document. This is also time-consuming and costly. Automated verification of data can be achieved using one way hashes locally or through use of a SaaS based service such as Q by SoLVBL to provide immutable seals to allow verification of the original data.