We know that effectively using elluminate to govern your data can make a big difference in your daily tasks. That’s why we’d love to hear your tips, tricks, and data governance techniques for modules like Validator, Data Quality, Standards, Specifications, Metadata, the Protocol Deviation Assessment Plan (PDAP), and Tasks! Whether you’ve discovered shortcuts, best practices, or innovative ways to integrate our software into your data governance workflow, your insights can help others enhance their experience.
Let’s build a resourceful community where we can all learn from each other. Please share your advice in the comments below!
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Are you noticing that data values appear truncated from the original field sizes after a data import?
We recommend you use Validator and Standards, which work in conjunction, to define conformance checks of datasets against your own established standards. For example, you could create an “external data” standard by which all domains in the External Data datamart should conform to. In addition to that, you could also define a “Labs Only” standard for all the external lab vendor files. The validation overrides capability offers the opportunity to apply another defined standard definition to the validation check in place of the selected standard/terminology. Continuing with this example, knowing lab imports are going to be part of the external vendor data, you could then set up a validation check to run all new imports targeted for the External Data datamart against the “external data” standard definition, but override the definition for the external data lab domains to use the "Labs Only" standard instead. So, to paraphrase, the validation check says “the indicated domains in this datamart should meet X standard, except for this one, which should meet Y.” X would be the "external data" standards/terminology and Y is the "Labs only" override in this example.
To continue the conversation, please comment and share other examples of use cases involving Validator and/or Standards you've found valuable for upholding data quality and how you’re identifying potential data truncation issues.
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