It does feel like it's a very early 20th century technique. Nowadays we have so many tools which would be less feasible for calculators (the people) and more feasible for software.
Recently, we started using the arcsinh instead of the log as well because the function has nice properties[1]
1. https://worthwhile.typepad.com/worthwhile_canadian_initi/201...
It makes nonlinear relationships linear. Makes the model less sensitive, too. For instance if the data spans several OoM, adding or removing one datapoint in one of those orders can generate a lot of skew before the log-linearization.
It's easy to cast the log back to the original distribution by taking the exponent afterwards.
[1] https://en.m.wikipedia.org/wiki/Generalized_linear_model
Read the Wikipedia article for a more formal explanation.
[1] https://en.m.wikipedia.org/wiki/Variance-stabilizing_transfo...