Good post, but I couldn't disagree more. Regardless of your business size, it will always be valuable to know information such as:

* How does every additional coupon-dollar affect the total amount a customer buys?

* What is the relationship between customer age and retention for my store?

* Does giving a customer more purchase options help or hurt their chances of making a purchase?

My experience is that each of these questions can be solved, in part, using 3 lines of Python code:

    from sklearn.linear_model import LinearRegression
    lr = LinearRegression()
    lr.fit(X,y)

Then look at the beta coefficients of the model, and you have a rough idea of how different features are correlated. Doing something like this in SQL sounds difficult. If you have data to interpret, it makes sense to use similar methods. I can't think of an example where you have data but refuse to look at it until your company is "bigger".