Data verification

Data verification is a process in which different types of data are checked for accuracy and <a href="/facts/Data_consistency/WUxG1d41">inconsistencies</a> after <a href="/facts/Data_migration/Eh3EE9Kg">data migration</a> is done. In some domains it is referred to Source Data Verification (SDV), such as in <a href="/facts/Clinical_trials/q8LMr832">clinical trials</a>.
Data verification helps to determine whether data was accurately translated when data is <a href="/facts/Data_transfer/1yAhaANd">transferred</a> 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 <a href="/facts/Data_loss/oGGosFHt">data loss</a>.
Methods for data verification include <a href="/facts/Double_data_entry/EDd1uzyV">double data entry</a>, <a href="/facts/Proofreading/5gTPjRUr">proofreading</a> 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.

Data verification open-in-new

Data verification