Is Data Readability on Your Preflight Checklist?
By Heidi Tolliver-Walker on January 25th, 2011
What does it take to get the data right? It’s more than ensuring that all of the fields are filled in correctly and the data itself is accurate. In addition to all of the technical issues, you also have to look at the data in terms of how it will actually read on the page. That’s a whole different ballgame.
For example, you might buy a list that tells you how much money each recipient makes. But you aren’t going to send out a piece that says, “John, you make $156,000 per year, so you might be interested buying one of these . . . .” Instead, you’ll identify John as part of a group of people in a defined income range, then use the appropriate images and wording to address John as part of that group.
Also, just because data is accurate doesn’t mean it’s going to read naturally. Let’s say you are looking to sell graduate programs to an audience with specific undergraduate degrees. It would not be unusual for the data to come in abbreviated. Here is a list of the first few undergrad degrees offered by the university here in my backyard, Penn State:
- Actuarial Science (ACTSC)
- Advertising/Public Relations (AD PR)
- African and African American Studies (AASBA)
- African and African American Studies (AASBS)
- Agribusiness Management (AG BM)
Imagine dropping the raw data into a personalized piece! “Hi, Bob! Now that you’ve received your degree in Agribusiness Management (AG BM), you might be thinking about advancing your education.” Or, even worse, “Hi, Bob! Now that you’ve gotten your AG BM . . .”
Or it might not be an abbreviation. It might be the formal name of the program or institution that would never be used in everyday conversation. If you purchase the name of recent college grads, “Congratulations on graduating from Boston College!” will work just fine. But you don’t want to say “Congratulations on graduating from The Pennsylvania State University!” You’ll want to say “Penn State.”
Now imagine combining snafus! Nothing screams, “We just dropped raw data into this postcard” like sending out a communication that says, “Congratulations on graduating from The Pennsylvania State University with a degree in AASBA”!
The point is that you have to look at data as part of the larger communication. Sometimes the data will read fine as it comes arrives in the file. Other times, you have to massage it or be sensitive to setting it up in order for it to read correctly. Abbreviations and formal names not used in everyday language are just two examples of the many data faux pas that can plague even the most seasoned 1:1 practitioners.
Is data “readability” on your data preflight checklist?