Every few years, someone declares that open semantic standards are obsolete.

The argument usually sounds practical. The market has moved on. Developers prefer simpler formats. Operational platforms need speed and scale. Business users need dashboards, workflows, and applications, not formal models.

There is a grain of truth to this.

Operational platforms should not be judged only by whether they use a semantic standard as their native runtime architecture. Serious systems are layered. They combine SQL, JSON, APIs, graph stores, search indexes, workflow engines, code, and user interfaces.

No one should expect one standard to do every job.

But that does not mean open semantic standards are irrelevant. It means we need to understand what job they are supposed to do.

Open semantic standards give organizations a transparent, inspectable, machine-readable way to represent shared meaning.

Most organizations think they have an interoperability problem.

They have too many systems. Too many dashboards. Too many databases. Too many teams using different words for similar things and the same words for different things. So they buy a platform, build APIs, export data, create pipelines, and declare victory when the data finally moves from one place to another.

But moving data is not the same as preserving meaning.

A spreadsheet can be exported. A JSON object can be passed through an API. A table can be replicated from one system to another. None of that guarantees that the receiving system understands what the data means.

Take something as ordinary as location.

In one system, location might mean where an object was observed. In another, where it is assigned. In another, its last known position. In another, its expected destination. In another, a region associated with responsibility, ownership, service coverage, or responsibility.

The field name is the same. The meaning is not.