One of the great things about software developers is that they tend to be "solutions-oriented." By that, I mean that if you give them a particular data management challenge, they want to figure out some way the software can address that challenge. This is a good thing!

But too often, we jump to solving a problem ("solutioning") before we really understand the scope and breadth of the problem itself. An example:

One challenge a client has is address changes made by members via the member portal. When these address changes are made, sometimes they can affect which chapter the member belongs to. And so the client wants a solution that allows for the staff to check address changes before they are "posted" to the member's account. An interim step to allow for quality control.

On the surface, this seems like a challenge worth addressing, and anything that could automate the process would certainly be helpful. Upon further digging, though, we discovered that, for most of the year, there are only about 10-12 of these changes per month. So rather than trying to build something to automatically deal with this, the correct approach is to check these manually as address changes are made. No automation required.

My recommendations before solutioning an issue:

1. Understand the depth of the problem. How often does "this thing" happen? How many transactions occur in a given time period?
2. Understand the breadth of the problem. For example, what's the financial or reputational impact if we don't automate this? If it's done manually and takes a little more time than automation would, who will notice, and is that a big problem, or a little problem?
3. What if we did nothing at all? Or put another way, do we really need to do what we think we need to do? If we did nothing, would anyone notice?

Solving problems is great. But sometimes the solution is more than the problem demands. Be careful about falling into the trap of "solutioning" without understanding the breadth and depth of the problem.