A friend and colleague of mine changed jobs about a year ago, and he sent me the following note about his experiences. He gave me his permission to share this, and for his anonymity, we’ll call him John.
Here’s a slightly edited version of what John wrote me. My commentary follows his letter.
Believe it or not, I’m finishing my first year here at my new association. It has been crazy busy, but building the department and processes from scratch is what I love, so there’s a lot of joy in it, although it’s slow going.
What’s been most interesting for me is for the first time in my career I’m experiencing the kind of disastrous database that I hear about so much from my association colleagues.
On one side of the coin, I never realized how good I had it at my last two associations. There were great data managers before me and it enabled simple maintenance practices.
On the other side, I now understand what people mean when they say they have a horrible database. No one here has cleaned records in 12 years. There are so many things wrong, I don’t even know where to start.
As you can imagine, it’s been both a challenge and an opportunity. So far, our open rates are increasing since we’re emailing less “dead” emails. We are seeing less returned mail since addresses are getting fixed. Complaints are down since we’ve merged records. Turns out, members don’t like to receive recruitment materials (for their second nonmember record) when they have already paid. Pretty certain that I am guaranteed employment here forever.
Yours in data management,
John highlights issues that I encounter with many of my clients: lack of business rules; no one responsible for data integrity; collecting data that’s never used; not keeping email and other contact data up to date; not managing duplicate records.
As I’ve written elsewhere, when it comes to data management, you’re either improving (cycle of virtue) or data is getting worse (cycle of doom). If you’re not actively managing your data, you’ve chosen the cycle of doom. And if so, your organization could also wind up in the mess that John outlined above.
The choice is yours.