One thing that most scientists in these soft scientists already have a good grasp on, but a lot of laypeople do not, is the idea of appropriately normalizing parameters. For instance dividing something by the mass of the body, or the population of a nation, to do comparisons between individuals/nations of different sizes.

People will often make bad comparisons where they don't normalize properly. But hopefully most people reading this article are not at risk for that.

As I said, dimensional analysis does not help with categorical variables. And when the number of dimensions is low and/or the number of variables is large, dimensional analysis can be useless. I think it's a necessary component of any model builder's toolbox, but not a tool you will use for every problem. Still, I would argue that it's underutilized. When dimensional analysis is useful, it definitely should be used. (For example, despite its obvious applications in physics, I don't think most physics undergrads learn the Buckingham pi theorem. It's usually only taught to engineers learning fluid dynamics and heat transfer.)

Two very common dimensionless parameters are the ratio and fraction. Both certainly appear in biology. Also, the subject of allometry in biology is basically simple dimensional analysis.

I've seen dimensional analysis applied in other soft sciences as well, e.g., political science, psychology, and sociology are a few examples I am aware of. I can't comment much on the utility of its application in these cases, but it's such a simple technique that I think it's worth trying whenever you have data with units.

Speaking more generally, the idea of simplification coming from applying transformations to data has broad applicability. Dimensional analysis is just one example of this.