How should versions, schema changes, backward compatibility, deprecation and migration be managed?

Direct answer

Every published human text, data schema and file package carries a version. Classify separately a non-material change such as a spelling correction, a backward-compatible new field or explanation, and a breaking change that disrupts an existing field, norm or consumer behaviour. Do not overwrite an older version silently with a breaking change. Publish a new schema with a change log, list of affected records, old-to-new migration mapping, compatibility test, deprecation date and archival relationship.

In plain language

Think of a city road map. Correcting a spelling error in a street name is not the same as closing a bridge and opening a new road. People using the new route need a date, an alternative and the point until which the old map remains usable.

Why this matters

Silently deleting a field or changing a norm may cause old applications to read data incorrectly and create different rulings across languages. A version number alone is not enough; the effect and migration route must be known.

Do not confuse

A spelling correction is an editorial change that does not alter material meaning.
A backward-compatible addition provides a new field or option without breaking older consumers.
A breaking schema change may cause an older consumer to operate incorrectly or incompletely.
A normative change alters what is required or prohibited; it is not merely a technical schema change.
Deprecation is a lifecycle with a notified end and migration path, not immediate deletion of a field.

What should you do?

Define public fields, normative records and file contracts in versioned form.
Classify every change as editorial, backward-compatible, breaking, normative or security-related.
In the change log, state the changed field, reason, affected records, date and decision owner.
Connect human, JSON, schema, API, PDF and language versions to the same release statement.
For a breaking change, provide an old-to-new field mapping, transformation example and migration tool or instruction.
Announce the deprecation warning, support period, removal date and safe fallback route.
Test backward compatibility with older examples, validate the schema and parity with new examples, and archive the release immutably.

How do you audit it?

Are the change class and material effect explicit?
Does the version number denote the same release on every surface?
Was a breaking change hidden as a minor correction?
Are the change log, affected records and migration route complete?
Was compatibility with older consumers and files actually tested?
Does a deprecated field have a date, warning and alternative?
Is the old version available immutably and related to the new version?

Limit

Numbering patterns such as MAJOR.MINOR.PATCH provide a useful signal about change; they do not prove compatibility. The same change may affect a book, normative rule, dataset and API differently. Security or legal necessity may shorten a migration period, but the reason and effect should still be recorded.