How can I fix Account addresses that have the full address into the billing street addresses?
Brief description:
A misfielded value happens when the values are entered into the wrong field. These values might still be formatted correctly, which makes them harder to catch if you aren’t careful. For example, you might have a dataset with columns for cities and countries. These are the same type of data, so they are easy to mix up. But if you were trying to find all of the instances of Spain in the country column, and Spain had mistakenly been entered into the city column, you would miss key data points. Making sure your data has been entered correctly is key to accurate, complete analysis.
This feels like a scenario where you should export it into Excel, clean it up the best you can, and reimport it.
I would, of course, use the transform option on the import to clean up things like State (abbreviation vs written out), Country Match (abbreviation vs written out), and Propercase.
Hi @cj_malwarebytes, are you able to provide an example of how the full address is formatted within that field? And can you please confirm if this is at all consistent or is it different between records? This will help us better determine whether or not we can find a formula to assist with this. Thank you!
Hi @JonG_Validity Thanks for yor reply. It is different between records. Like if I run a report on account addresses, some users included the Full Address into the BillingStreet address, instead of separating the Billing Street, Billing City, Billing State, Billing PostalCode, Billing Country into different fields.
It's all over the place, meaning there's no consistency nor there's a trend. It's just a few users creating accounts from lead conversions (most likely) and they're always rushing without taking the time to care about the proper way to create a new account record. Basically, we have been getting to fix this either manually, an/or from an excel spreadsheet to re-align the account addresses correctly then doing a mass update. This is extremely time consuming.
This feels like a scenario where you should export it into Excel, clean it up the best you can, and reimport it.
I would, of course, use the transform option on the import to clean up things like State (abbreviation vs written out), Country Match (abbreviation vs written out), and Propercase.
