How do I get my users to trust the database?

I spoke with an association executive recently who told me, of the 22 people on his staff, only two are actually using the database. When I asked him why, he said “No one trusts the database or the data it contains. And I don’t blame them; the database is an unmitigated disaster.” One of their major issues is that much of the data is incorrect or out-of-date and thus no one trusts any of the data to be accurate.

The question then becomes, how can I fix things so that my users will start using the database again?

As I’ve written previously, when it comes to data accuracy, you have to have instilled in the organization a virtuous cycle of data management. Good data begets better data.

But beyond that, you have to rebuild the trust in staff that the data is accurate and being updated. And how do you do that? By changing what your users believe.

As consultant Alan Weiss points out, beliefs inform attitude, which in turn informs behavior. Or to invert the equation, in order to change behavior, you have to change attitudes, and in order to change attitudes, you have to change beliefs.

In this case, we have to change the belief that the data in the system is “bad.” So how do we do that? Here are a couple of suggestions:

  1. Identify the data that is most critical to your organization’s success, the data that most needs to be accurate. For many of my clients that would include membership data (e.g., join and renew dates) and contact information for key members (e.g., board members, key volunteers, emeritus members). Start your clean-up on these records. Data integrity reports will help.
  2. Set benchmarks of the current accuracy of the database so you know when you’re making progress. For example, one of my clients tracked the bounce-back rate of their snail-mail over the course of months of mailing, and was able to demonstrate that over time, the data accuracy had improved because the bounce-back rate had declined from 7% to less than 1%.
  3. Practice database PR. Tell your users what’s working and the successes you’re having. If you don’t tell them, who will? And be honest about the improvements you are making and the time it will take to clean the data.

In the scenario above, the users believe that the data is bad, thus their attitude is that the data and database are useless. Their behavior is reflected in that no one is actually using the database. By moving their belief to “the data is good,” through sweeping data clean-up, their attitude toward the data and the database will change, and in turn, their behavior will change.

A scenario like the one described above is not an overnight fix. The steps outlined above are a multi-month process. But the return on that investment will pay very high dividends for years to come.

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