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on 12-14-201502:02 PM - edited on 07-27-202111:45 PM by APIUserOpsDM
Data Integrity refers to the accuracy and consistency of data stored in a database, data warehouse, data mart or other construct, and it is a fundamental component of any analytic workflow. In Alteryx, creating a macro to compare expected values to actual values in your data is quite simple and provides a quality control check before producing a visual report. Let me show you how to build this.
The two inputs represent the actual and expected values in your data. These data streams are passed through a Record ID tool to keep positional integrity and then passed on to the Transpose tool to create two columns. The first column contains the field names and the second column shows the values within each field. This data is then passed on to a join, matching on Record ID and the Name of the field, in order to compare each value. Lastly, if the data does not match from expected to actual, a custom message will appear in the results messages alerting the user where the mismatch happened within the dataset. The image below shows the error message produced if values differ across datasets.