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I use Alteryx to prepare survey data for Tableau. I also have a workflow that combines data from many (near identical) surveys together to create a benchmark database.
The surveys are live and also subject to change. The workflow is run monthly. So It isn't a case of getting the workflow working and then forgetting about it. As surveys are modified or new surveys come on line, errors can creep in. So I want to build something that is robust.
Can you please point me to posts / resources that show workflows that incorporate error checking e.g. "Have I lost any fields through the workflow in the attached screenshot?". I go the idea from an excellent training session at Inspire17 in London but I can't find the reference in the recorded materials.
The place to start is with a field info tool. Given a read of new data, are the incoming fields what you expect? Join it to a known source and see if there are new fields present and if you've lost any data along the way. What comes next is bespoke. Do you want to check for:
Data types - Is it still the same?
Domain values - Are values missing or new values present?
Content distribution - Is there a skew to the data?
Expected values - Is the date or other content what you're expecting for new data?
You might want to run these checks in a workflow and conditionally (CReW) execute the staging of the data.
Something like this will check to see what fields have changed.
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How would I put a check into the existing workflow that takes the field count at the beginning of a series of transformations and then again at the end; compares them and returns an alert (ideally an error message or warning).
Would I create a new field = count(id) at the beginning, another at the end, compare them?