How do we determine if the data has to be transformed to reduce skewness? Visually or metrics? I am wondering what would be the normal way for a data scientist to validate if the data is skewed or not. Is it by plotting the histogram or by finding skewness/kurtosis value (ex:- using pandas methods etc)?
What is the correct way? and what is the normal way it's done in real data analysis/machine learning work?

Read up on and consider lognormal distributions. They are very common in many fields of science. A log transform converts a lognormal distribution to a gaussian. But this is not just a mathematical trick, to be done when a normality test flags the distribution as nonnormal. There are reasons why data are lognormal, and it is appropriate to account for this with a log transform of the data before some analyses, and a reverse log transform of some results (to convert differences between logs to the ratio of actual values).
For ratio data (only positive values are possible; changes are multiplicative rather than additive), some argue that assuming a lognormal distribution should be the default.
