For many associations, making the change to a new database is not only expensive and time-consuming, but it also decreases productivity for an extended period of time. But shouldn’t a new database improve productivity?
Yes, it should. But frequently, when an association makes this move, productivity actually falls, rather than increasing. The productivity curve looks like this:
Why is that? In a nutshell, moving to a new system requires knowing how to use the system. Theres a learning curve. And the way to provide your users with the best approach to the learning curve is to provide them with the following:
With any new system, even if its simply an upgrade to an existing system, training is absolutely critical. For example, I worked recently with an association that was moving from version 1.x of their current database to version 2.x. While this was simply an upgrade, the user interface had changed fairly radically, and a lot of new functionality had been added to the system. As a result, my client was smart enough to provide training for all users prior to go-live. Because most of the users already knew how to use the current system, the training session were relatively brief and only covered areas where changes had occurred.
In addition to the training, my client also spent considerable resources on documenting all of their primary processes in the new database. As a result, when training occurred, documentation was already available to the users.
And finally, once training and documentation had been completed, my client made sure that every department tested their existing processes in the new database (in a test environment) to ensure that the documentation was correct and that the processes they would need to use atgo-live were, in fact, working properly in the new database. Once testing was completed, users provided feedback to the project manager, who then updated documentation based on that feedback. An additional result is that future training will have the updated information.
This feedback loop of training, documentation, and testing looks like this:
Presumably, one of the reasons a conversion is being made is to improve staff efficiency and effectiveness. So what does an ideal conversion look like? An ideal conversion would see an immediate improvement inefficiency. An example of one of my clients illustrates this nicely: Prior to converting to a new database, the average time for my client to respond to a membership application was six weeks! Six weeks between the time the member applied and the time the member received his or her membership card. Following their successful implementation of a new database, that response time went from six weeks to 48 hours (or less). By properly implementing training,documentation, and testing, their productivity curve looked like this:
While many system conversions wind up initially decreasing productivity, it doesn’t have to be that way. With the proper training, documentation, and testing prior to go-live, productivity can actually increase as soon as the system is implemented.
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