How do you explain time spent to non-data science supervisors?

It seems like a lot of time spent in data science is reviewing papers and methodology, finding usable data, exploratory analysis, cleaning data, etc. and very little time is actually running models and creating reports.

How much time do you devote to each project step, and if you spend a lot of time reviewing papers and data, how do you explain to a non-data science supervisor that this is a valuable use of time?

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