This approach of data maximalism seems to work well primarily for NLP and CV. However, there are lot of domains where throwing every feature without some consid

This approach of data maximalism seems to work well primarily for NLP and CV. However, there are lot of domains where throwing every feature without some consideration of information will not get you good results. To take a simple case, you are asked to model (forecast) the sales of an item at a retail store. Jamming in features like - color of people shirts, average length of their nails, the duration of traffic signals in front of the mall, the length of the store name etc.. will not lead to good results. In contrast, seasonality, prices, promotions, weather etc. will most likely lead to good results. Why does the second set of features work while the first doesn't? I like to think of the second set having a model of causality, to figure that out you need some domain expertise and experience. Without that expertise, the feature set of non-causal factors is almost infinite.