Best tool vs optimal tool

I was looking for a cloud data storage solution recently and researched it on the internet. I read a number of product reviews with pros and cons. And then I read some users comments that provided some real world rebuttal of the reviewers assessments. These users used the product within their use cases longer than the product reviewers who used the product only a short time for the sole purpose of writing a review article for the internet. The perspectives from other users put the product reviews into context whether they are relevant or not for my use cases.

Context matters a lot, but context is often overlooked

When we want something, we research it to find the best product / service that we can buy for the specific use case. We buy it and then sometimes we discover afterward that the best product / service has other costs aside from the money we pay for it.

It is not necessarily wrong, if best performance or best value or best whatever is the one and only goal. But it is important to understand well: are we absolutely sure there is no other goal that we ought to consider within the context of the big picture?

We ought to avoid “local optimisation” that can degrade the overall expected benefit.

For example, let us say that we have two inter-dependent jobs that need to be done by two different tools and we bought the best tools we could find for each of the jobs: tool A and tool B. Great, so now we will get the maximum benefit when we put these two together, right? Not so fast. It depends on how well these two work together in managing the inter-dependent aspect of their jobs. We need to understand the short term and long term implication whether problem or additional effort or cost may ensue out of the integration between A and B.

Considering the full context, we ought to assess if A and B are still the tools of choice for the jobs, or whether alternative options will integrate more optimally to yield a better overall benefit.

Assumptions can mislead

This one is obvious, but also often gets overlooked. We want to be explicit about all our assumptions and understand how each assumption affects our decision making process. If our assumptions change, the optimal solution may change also, because what we need may turn out to be different.

As for my cloud data storage search, I challenged my assumptions and in the end I reframed my “jobs to be done” differently from when I started my initial research. By questioning my assumptions of what I need vs want, I reconsidered a solution that I excluded previously. This solution is not the technical best because it does not meet a few of my criteria, but it fits perfectly one criterion: simplicity.

Optimise for total benefit

The optimal tool balances trade-offs to maximise the total benefit.

“The whole is greater than the sum of its parts.” Aristotle

In my cloud example, the best tool is not the simplest and, for now, simplest is what I need. Therefore the optimal tool that I bought, in this case, is not the technical best, but I am happy with it, because it gives me the maximum total benefit.

#design #learningorg

P.S. May I interest you to read my older post: Best practice can be wrong?