Do you think it's normal for data science projects to have some amount of "what should we do" -time? Or does it mitigate by experience?
By "what should we do" -time I refer to time being spent on reading about and experimenting on "possible ways to do things, when many alternatives exist". This kind of "time" has bothered me, because someone could think it's some kind on inefficiency or failure to concentrate.
On the other hand I've rationalized that it could be a normal part of a data science project, because projects may be different, they may have special considerations or boundaries that are different from other projects. This would lead to time having to be allocated to "particularizing from general and other solution methods". Thus also, to expect that one would instantly understand the "best way" to solve a problem, would be too optimistic and forget that "one may not know, without experimenting, what the best solution is". Since a data science project is not necessarily an "exact mathematical problem".