Ads
related to: how to verify data migration
Search results
Results From The WOW.Com Content Network
Post-migration: After data migration, results are subjected to data verification to determine whether data was accurately translated, 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 .
Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. [1] In some domains it is referred to Source Data Verification (SDV), such as in clinical trials .
In this case, the source actor is asked to verify that this data is what they would really want to enter, in the light of a suggestion to the contrary. Here, the check step suggests an alternative (e.g., a check of a mailing address returns a different way of formatting that address or suggests a different address altogether).
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Data discovery is the first step in the data transformation process. Typically the data is profiled using profiling tools or sometimes using manually written profiling scripts to better understand the structure and characteristics of the data and decide how it needs to be transformed.
Data conversion can also suffer from inexactitude, the result of converting between formats that are conceptually different. The WYSIWYG paradigm, extant in word processors and desktop publishing applications, versus the structural-descriptive paradigm, found in SGML , XML and many applications derived therefrom, like HTML and MathML , is one ...