Unknown, unavailable and not collected should not be treated as synonyms

An empty field can describe several different situations, and those situations do not mean the same thing.

A value may be unknown because nobody has established it yet. It may be unavailable because the source does not expose it. It may also be absent because nobody tried to collect it in the first place.

Those distinctions matter when someone later uses the dataset to make a decision.

If every kind of absence is stored as the same blank value, the next person has to guess what happened. They cannot easily tell whether more research might uncover the answer, whether the source was checked and came up empty, or whether that field fell outside the original scope of the work.

A small amount of context can prevent that ambiguity. The exact representation depends on the dataset, but the meaning should be deliberate and documented. A status field, a short data dictionary, or a controlled set of null reasons can preserve information that a plain blank cell would discard.

This is especially useful when datasets are refreshed later. A record marked unavailable can be checked again when a source changes. A record marked not collected can be routed into a new research pass if the project scope expands.

Missing data is still data about what is known, what was checked, and what remains unresolved. Treating those states carefully makes the dataset easier to understand and safer to reuse.