The optimization problem is the problem of finding the best solution from all feasible solutions.
What does the optimization problem look like?
- Person A raises that they have this problem and they're going to try X.
- Person B sees similarities with what another team is going. They are excited by the opportunity to optimize. Person B sets up a meeting to consolidate approaches and decide on a consistent approach across the org.
- Person A and B both meet, perhaps several times. They map out all the possibilities, differences in context, edge cases, etc… in the end, they spend hours seeking an approach that suits both their contexts and future needs.
- After several meetings and countless hours, they finally came up with a consolidated approach that is for everyone but doesn't 100% work for any one team.
As the adage goes: "If you build something for everyone, it works for no one."
This is a common scenario many organizations regularly find themselves in. They spend weeks, even months, trying to find a 'one-size-fits-all' solution.
This is when the optimization problem becomes a trap.
You may not see this happening directly. It doesn't always manifest itself in countless meetings stress testing against every conceivable possibility.
Instead, I often find it's hidden in the form of:
- Trying to avoid inconsistency
- Avoiding duplication
- Early convergence on a solution/idea
Now don't get me wrong, there are advantages to consistency and not having to do things twice.
But there is also a cost to striving TOO much for consistency and having ZERO duplication.
The cost is experimentation, divergence, innovation and speed.
Rather than experimenting and trying new things to see if something works first, we sit in a room and discuss for hours, trying to find the optimal and perfect approach.