With the explosion of deep learning, I don't want to say that feature engineering is a lost art - no at all - but many of the benefits of DL, such as automatic

With the explosion of deep learning, I don't want to say that feature engineering is a lost art - no at all - but many of the benefits of DL, such as automatic feature extraction, is certainly something to be considered before just jumping into a project. I've seen examples of where good and careful data collection in advance would have solved the problem, but instead, someone decided to transform the (easiest) collected data into a format suitable for DL - and spent months trying to optimize and tweak the DL model. Because their rationale was that it's much easier to just fine tune an out-of-box DL / transfer learning model, than spending ages on extra planning, data collection, feature engineering, and what not.