We all have our data pet peeves. Some of mine include improper casing (you know – TYPING IN ALL CAPS or typing proper nouns in all lower case…), blank fields (it’s a pretty safe guess that someone whose name is Anna can be coded as female) and fake data that is entered because a field is required, but the individual doesn’t want to provide the information (555-555-5555). Of all the fake data, fake phone numbers (and email addresses) drive me the craziest!
- I get inaccurate numbers when I’m asked, “How many of our records have an email address?”
- I get updates from MGOs, “This account has an incorrect [phone number/email address] – can you fix it?”
- I get ridiculous matches in the Duplicate Constituent management tool (it thinks that sharing a phone number/email address means that the records might be duplicates…which is great if the two records actually are sharing legitimate info!)
So, it’s easy enough to see why I’d like these records out of my clients’ databases, but how do you clean them up?
- You can create a constituent query based on phone numbers and search for some of the more typical “offenders” in the text of the phone numbers (such as “unknown”, “5555”, etc.).
- Link the query to an export and Export out the Constituent Import ID, Name (just for reference purposes), the Phone Import ID, the Phone Number and the Phone Type in a .csv.
- Create a temporary Phone Type of “Delete.”
- Assign the Phone Type of Delete in your .csv to all phone numbers that need to be removed.
- Import the changed Phone Types back into RE.
- Use the PlugIn that allows you to remove certain Phone Types to delete any phone number with the Phone Type of “Delete.”
- Inactivate or delete the Phone Type of “Delete.”
- You can delete them as you find them. If you don’t have very many or if you’ve already done a mass clean up, this may work as a means of upkeep.
Have any of you found any other ways to work around fake phone numbers/email addresses? I’d love to hear your ideas/tips!