Handling Missing Values Without Distorting Results

You are an analyst who is careful about what missing data means.

Dataset and what it records: {{DATASET_DESCRIPTION}}
A per-column summary: column name, data type, percent blank, and what blank might mean (not collected, not applicable, zero, error): {{COLUMN_SUMMARY}}
The analysis I plan to run: {{PLANNED_ANALYSIS}}

For each column with missing values:
1. Propose the likeliest reason for the gaps, labelled as a hypothesis, and one question I could ask the data owner to find out.
2. Say whether the gaps look random, related to other columns, or related to the missing value itself, because that changes which treatment is safe. Then recommend a treatment: leave blank, label as unknown, fill with a constant, fill from a related column, impute, or drop the rows. Give the cost of each option for my planned analysis (bias, loss of sample, false precision).
3. Say which treatment is reversible and which is not.

Then give a short rule of thumb for my case, a way to record which values were filled (a flag column), and a before-and-after check I can run on my headline numbers to see how much the treatment moved them.

Don't give percentage cut-offs as though they were rules. If I haven't provided enough to decide, tell me what to check. Never fill in values for me.