As a data programmer I am often asked what it is I do. The simple answer: I prepare databases for mailings with major charities and corporations. At Prime Data, we receive files in many forms: dBase, CSV, TXT, MDB, DAT and XML to mention a few. However, we often have to merge several different files into one common format prior to any prep work.
Names and addresses are our primary concern when the business relationship is very personal, such as a fundraising donor mailing. Nothing can damage a relationship faster than poorly addressed communications.
Here are a few examples of how we receive name data within a database prior to any clean up:
Within each of these examples the same basic information is provided however, each field needs to be consistent for comparison and formatting. It’s also not uncommon for us to receive names in all capitals, or in all lower case, proper case or combination of both such as MR. John SMITH. We also want to avoid addressing an offer or proposal to “Dear XYZ Company” when we might be able to improve upon that.
Another challenge we commonly face is more than one name within the record, such as:
This makes it difficult to determine who actually lives at an address. At Prime, we work with hundreds of thousands of names per day. It is humanly impossible to check each record manually in these quantities, so we have created processes to identify and reformat data to standards approved by our clients. We do name preparation prior to, and in addition to, running common off the shelf Address Correction software.
We do this because poor data can damage the existing positive relationships with a donor or client. Name clean-up and standardization improves merge purge outcomes and response rates of Fundraising and Direct Marketing campaigns and these principles apply equally to printed mail and email personalization.
So, I guess I could also say I am an expert in the identification, correction and reformatting of Name data. For more information about data cleansing, email me at: firstname.lastname@example.org.
In future posts I’ll describe other data clean-up craftsmanship we execute here including finding people who have moved, improving email addresses, geo-coding individuals, the fine art of identifying duplicates and correcting mailing address accuracy.