This is missing an important component of the process. If you don't want to have to reinvent the wheel every time you are asked to do a certain type of analysis, you also have to set up some infrastructure to support your analytic pipeline. That involves understanding databases, writing scripts to automatically harvest data, possibly creating APIs for your data to support flexible analytic views, etc. The more time I spend in data science, the more time I find myself spending on these types of infrastructural tasks. It's great to work for a company that provides engineers that will do all of this for you, but those companies aren't super common.