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(Data) Anomalies....  I always seem to misspell the word, but I digress.





Well, we all wish data was perfect, clean, and always correct.   But that's just not the reality.   If you are a GS Admin and do not yet know about the Data Anomalies page, please continue to read on.  





1.   Please check out this page in the product documentati>  https://support.gainsight.com/hc/en-us/articles/202959153-Fixing-Data-Anomalies-in-Customer-Info-Rec...





2.  Sign up for the email notification.





I have really found this feature/function helpful to keep account data clean throughout our Gainsight instance.  There are many other mechanisms to add to your arsenal in keeping data clean and current, and hopefully this one will help as well.  Over time, I know I have avoided a large amount of frustration using this feature to proactively keep account data as clean as possible.
We had an issue where a few emails weren't sending and traced it back to data anomalies.





Suggestions for Gainsight:


1. Don't make this a hidden page.  If I had seen this in Administration I would have signed up for the emails earlier.  You might even want to consider a Gainsight intitial setup that populates that admin's email on this automatically.


2. Can you please make another pass at if these two lists are even required.  You may be able to avoid these data anomalies with a tweak to customer load processing or have a script after the fact if it can't be avoided to clean these up.


2a. The list of “Accounts with no CustomerInfo field value” seems like it


could be solved with a daily script if situation can’t be avoided because the


relationship already exists on CustomerInfo but just wasn’t tagged on Account.


2b. The list of “Account with multiple CustomerInfo records could be


potentially be prevented on load where you stop the load of a second Customer


Info to the same account.  This one is a little fuzzy to me because if you


are doing an upsert, I don’t see how multiple records could be created if you


have the correct base design.
These are great suggestions!
I'm forking this comment into a new Idea post to track it as a feature suggestion/feedback.





Please reference the new conversation here: Data Anomalies suggestions

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