In my experience the real meat of data manipulation/wrangling is in extracting statistical value from the data. If the data is noisy, what aggregations and what

In my experience the real meat of data manipulation/wrangling is in extracting statistical value from the data. If the data is noisy, what aggregations and what filters produce the best signal without smudging the underlying statistical properties? While this is a good skill to have and is a good sign of efficiency, for me the deal breaker is how well the purpose of data transformations is understood and the care to which statistical value is extracted and tested.