I’ve been working on a big database project over the past couple of weeks that is going to make our membership software work better for our staff, members, and volunteer leaders. During this project, I have (inevitably) discovered some additional data issues that need to be cleaned up. I’m continually improving at thinking through database projects and how a change in one area impacts other areas as well. Periodic data clean-up is a good practice for all organizations. Here are a few clean-up tips for recruiters:
- Garbage In, Garbage Out—Maybe this isn’t really a tip so much, but the fundamental thing to remember about all databases. There is no perfect product/solution/database, and always, always, always, your results are only as good as the data you feed it.
- Pay Attention to Your Fields—A lot of ATS products have custom or configurable fields. We have them in our association management software as well, and I have learned over time that field labeling (and sometimes help text) goes a long way towards getting better data. In this particular project, we’ve got a field label that sort of asks 2 questions in one, which means a good chunk of the data we’re capturing, while technically accurate, isn’t really helpful or what we wanted to collect. So we’ve changed the label and now we can start reviewing and cleaning that data. This is one of my most important tips for recruiters – review your fields, field names, field structure, field labels – to make sure you’re capturing the right data in the right places.
- Good Tech Partners are Invaluable—I’ve been so fortunate to stumble upon some really good tech partners who help us think through our ideas. They help us avoid causing issues in other areas, figure out how to use our development time efficiently and effectively, let us know how to avoid scope creep, and often come up with creative solutions that we never even realized were possible. And they also help us make sure we have good, clean data. I really enjoy working with the crew at MemberNova, our association management platform.
- Purge Outdated Data—Make sure you are setting aside time at least twice a year, if not quarterly, to purge old records. If you have candidates that have opted out, delete them. Candidate records that haven’t been updated in years (and don’t have a lot of notes) may be best recreated from scratch instead of keep old info that isn’t very helpful. If you’re sending emails, check your bounce list and make sure you are getting rid of records with no-good emails (or figure out how to update them).
- Standardize Data—To the extent it’s possible, use dropdown lists of pre-determined values, program data and time formats, and use validation to ensure that the right type of data gets entered. If you don’t collect bad data in the first place, you’ll have far less clean-up to do in the future. Make sure your critical fields are required, but don’t create data entry fatigue but have way too many fields, all of which are required. If a field *is* required, make sure the person supplying the data can actually fill in the field—all values are there, or there is an option the user can select if the specific value is missing. This is another of my favorite tips for recruiters: prevent as much bad data from entering your system as possible.
- Use Automation to Assist—There are a number of automation tools that can assist with data clean-up, including tools to find and remove duplicate records, find incomplete profiles, validate email addresses, and more. We recently learned about an affordable tool called Brite Verify that performs email valididation. For less than US $100 and in just a few minutes, we were able to cull several thousand invalid emails from our prospect list.
It’s a pretty safe bet to say that no one particularly enjoys data clean-up, but it is an integral part of good systems management. Do you have a favorite tip to share, or something you learned the hard way? Drop a comment below!