You always start by looking at the data, not by busting out advanced statistical methods. Those methods are obscure and could easily hide how ugly and unclean y

You always start by looking at the data, not by busting out advanced statistical methods. Those methods are obscure and could easily hide how ugly and unclean your dataset is. You really do need to look at types, missingness, the data structure and ensuring the row ID is what you want it to be, eliminating duplicates, joining on other datasets; it's a massive list of steps. Even with a clean dataset, most clients will want basic arithmetic calculations: averages, counts, percentages, standard deviation, etc. Occasionally they'll want some basic logistic models, something slightly more causal. If they go straight to machine learning without these steps, do they actually understand their problem and what they want? Or are they reaching for the shiniest thing they've heard of?