Since data is constantly transferred from vendors with different schedules based on the type of data, organizations must be proactive to control the acceptance of each data transfer. With each data transfer, organizations must be confident that all data received is correct by confirming full scope of all variables, attributes and records against the data transfer specifications. Often, missing variables or data truncation can occur due to faulty coding or values being set to missing.
The examples in this presentation are essential clinical data metadata for effective data transfers. The dictionary datasets, TABLES and COLUMNS, contain much of this information and can easily be accessed. Any metadata information outside of TABLES and COLUMNS data sets can be created as customized checks using SAS.
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Computer and phone connections are required. Webinar will be recorded for on-demand viewing.
This training webinar is sponsored by TalentMine.
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