You should look at the lognormal distribution.
People may use logs because they think it compresses the scale or something, but the principled use of logs is that you are working with data that has a lognormal distribution. This will tend to be things like salaries, housing prices, etc, where all values are positive and most are relatively modest, but some are very large.
If you can take the log of the data and it becomes normalish, then you can take advantage of many features of a normal distribution, like well-defined mean, standard deviation (and hence z-scores), symmetry, etc.
Similarly, addition of logs is the same as multiplication of the un-log'd values. Which means that you've turned a distribution where errors are additive into one where they're multiplicative (i.e. percentage-based). Since techniques like OLS regression require a normal error distribution, working with logs extends their applicability from additive to multiplicative processes.