A field name is often an abbreviation for a longer idea. Different readers may interpret “active,” “complete,” or “value” differently unless the rule behind the field is written down.
Create a small data dictionary with the meaning, unit, allowed format, and important exclusions for each column. Include an example where it helps. The description should reflect the actual collection process, rather than an ideal definition no one follows.
Update the dictionary when a field’s meaning changes. A historical table may need the earlier definition preserved too. Clear column definitions make analysis, sharing, and future corrections easier to discuss.
- Define the rule behind each field.
- Include units and important exclusions.
- Keep earlier definitions when the meaning changes.
A small working example.
Consider handing over a table with a special code for unavailable values. Explain that code before someone mistakes it for an ordinary measurement.
A note to keep beside it.
A column name can hide an assumption. Check units, missing values, and the counting rule before combining observations that only appear to be alike.
Follow a related question
Ask which situations the examples cover.
Examples shape the model’s taskDistinguish an amount from a rate.
Energy and power answer different questionsKeep learning
Related background to continue exploring this subject.
W3C: a primer for tabular data RFC Editor: the CSV format
