Subscribe to my newsletter EDM News
You can’t just plan, you must also “do”

There’s a book by David Maister, one of the preeminent consulting gurus, entitled “Strategy and the Fat Smoker: Doing What’s Obvious But Not Easy.”

The premise of the book is simple. Most of us know what to do (e.g., don’t smoke, eat better) but most of us don’t actually DO what we know should be done.

I’ve found the same holds true for associations and how they manage data. The vast majority of associations that I meet and work with have a fairly good idea of what they need to do in order to improve how they manage their organization’s data. Yet so many of them don’t actually do what they know to be the right thing. Here are some reasons why that happens, and what you can do to avoid falling into the same trap.

  1. Fear of doing “it” wrong. I’ve worked with over 200 associations in my consulting career, and I’d guess that 75% of them expressed some concern about doing “it” wrong, whatever the “it” was that we were discussing. In fact, that’s often why associations hire me. I have tons of experience that will ensure they don’t do it wrong.

    But what if you can’t or won’t hire a consultant to help? Then you’ll have to figure out what the best practices are for whatever your “it” is and apply those. And you’ll have to accept at the start of the project that you will make mistakes; nothing will be perfect. In fact, the best step to getting over your fear is to simply admit that things will not be perfect. Once you’ve acknowledged that, you can start moving!

    For example, let’s say you’re setting out to start measuring member engagement. There are probably dozens if not hundreds of data points you could choose to measure. So which are the “right” points to choose? Well, there aren’t really any “right” data points. So choose five or so (not 50!) and start measuring. And then go back and check after six months, and modify as needed. You’re only doing it “wrong” if you’re not doing it at all.

  2. Overwhelm/not knowing where to start. This one is quite common, especially when it comes to “improving data management.” After all, there is SO much data and SO many places we could improve, it’s overwhelming to decide where to start.

    The answer is simple: Start somewhere, but start. My favorite example is email addresses. Can you easily identify what your deliverability rate is on your email addresses? And if so, is that a number that can be realistically improved? (Keeping in mind that nothing can be perfect, if your email deliverability is above 97%, it’s probably not worth much effort to improve it. But if email deliverability is below 80%, there’s probably a lot that can be done to improve!)

    So look at your data, determine which data points are most important, and pick one that has the greatest opportunity for improvement.

    For example, one of my clients (a trade association) determined that, while they had good data for their primary reps, their data on secondary reps at each member company was very thin (under 50% of member-companies with secondary rep data). So they focused on that piece of data, and in a very short period of time were able to increase that to over 80%.

  3. Unrealistic expectations. I’ve found that when I first start working with my clients, their expectations fall into two broad categories: a) we can fix everything that is “broken” and b) once we’ve fixed it, we’re done. Both assumptions are wrong.

    The first rule of data management is that our data sets are never perfect, ever. No matter how much effort we make, no matter how many resources we throw at it, data changes. People move, people die, companies merge, and so on. Life changes. So does the data.

    The second rule of data management is that we’re never done managing data. As I’ve written before, with data management, your data is either getting better or getting worse. It’s never static.

    So understanding that success is our goal, rather than perfection, is the first giant step toward making the right moves on data management.

There are certainly many other reasons that we don’t manage data in a manner that we know we should. We might have poor technology. We might have mismatched employees. We might have processes that actually work against us.

Any of these challenges can be overcome, but we have to take the steps to overcome them. We can’t just plan to do them, we actually have to take action and DO them.