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How do I view scheduled validations in Elevate Data Hub?

Updated

Validations are jobs that check data for accuracy, quality, and security against pre-defined rules. Instances may have scheduled validations.

If you cannot access the areas shown in this article, or complete all the steps, you may not have the necessary permissions.

Open Instance

Open Instance

Click the name of the instance.

Open Validations

Open Validations

In the Instance Navigation menu, click the Validations link.

View Validations

View Validations

Locate the validation you want to view details for, and click the Details icon.

View Validation Details

View Validation Details

The Validation Details tray displays overview information for the validation, including the Validation Job ID [1], Start/Stop Dates for the job [2], and the managing account [3].

To manually run the job, click the Run Validation Now button [4].

Filter Validations

Filter Validations

You can use the filtering options on the Validations page to easily find a validation.

To search for a validation by name, type the name in the Search by Name field [1].

To filter validations by status, select an option from the Status drop-down menu [2].

To filter validations by errors, select an option from the Errors drop-down menu [3].

To clear all applied filters, click the X button [4].

View Validation Status

View Validation Status

Each validation includes a status indicating the progress of the validation. Status can be Complete, Running, or Failed.

View Validation Warnings

View Validation Warnings

Each validation includes a count of warnings that occurred in the flow of data. The error level indicates issues that impair the accuracy, quality, and/or security of the data, as defined by the validation rules. The error level of the returned data is determined in each individual rule. Examples of error levels include:

  • Fatal [1]: Indicates a critical error in the data. Validations with fatal errors cannot be marked complete until the errors are corrected.
  • Special Warning [2]: Indicates a potential discrepancy in the data that must be examined carefully and corrected as needed. Special Warnings do not prohibit a validation from being completed.
  • Warning [3]:¬†Indicates a possible error or inconsistency in the data, which should be reviewed for accuracy. Warnings do not prohibit a validation from being completed.

To view details for the warning level, locate the desired error level column, and click the [Number] link [4].

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