Tips on how to deal with cleaning up old, messy member data.
Stale. Messy. Inconsistent. Dirty. Call it what you will, but a lot of data collected over the years is not clean. Herein lies one of the biggest challenges for associations, says Peter Houstle, CEO of Mariner Management and Marketing. “Messy data means you can’t compare analytics,” he says. “That makes the data management process more difficult.”
What exactly is messy data? It’s data that suggests the impossible (for instance, that a member is 150 years old, or that somebody completed her residency the year before she graduated from medical school); data that is formatted incorrectly or inconsistently (such as Maryland spelled out versus abbreviated MD); outdated addresses; and duplicate records.
Often, messy data results from a lack of rules: What’s a current member, what’s a lapsed member, what’s a prospect, and what constitutes an individual versus a company membership?
If these rules aren’t documented, staff members will do things differently. “You need standards, you need to reinforce them, and you need an audit mechanism,” says Elizabeth Engel, CAE, CEO and chief strategist of Spark Consulting.
When you find that colleagues are making mistakes—and they will, because less frequent users might not know the rules as well as membership staff—you want an audit trail, Engel says, so you can see which users are having trouble with which rules.
Often, when association executives discover the extent of their messy data, they get discouraged. “Associations will just throw their hands up at that point, before they even get started,” says Engel. But she cautions not to give up so easily. Taken in small doses, and starting with the biggest problems, cleaning your house of data shouldn’t be so scary. “The biggest mistake associations make,” she says, “is saying it’s too hard.”