Data interoperability is the ability of different systems to exchange data and use it correctly without losing its meaning. It goes beyond moving bytes between platforms, which data integration already handles, and beyond agreeing on a file format. True interoperability preserves the context that makes a value mean the same thing on both sides, such as units, definitions, and the relationships between fields. For example, two hospitals can both store a glucose result as a number, yet the value is only safe to compare if each one carries the same unit and reference range. Healthcare is where this shows up most sharply, since a lab result that loses its reference range or timing becomes unsafe to act on. For AI, interoperability is a readiness prerequisite: data that arrives technically connected but stripped of its context will quietly degrade a model, because the model inherits whatever meaning survived the handoff.
Frequently asked questions
What is data interoperability?
The ability of different systems to exchange data and use it correctly without losing its meaning, not just its format.
How is interoperability different from data integration?
Integration moves data between systems. Interoperability ensures the data still means the same thing once it arrives, including units, definitions, and relationships.
Why does interoperability matter for AI?
Data that arrives connected but stripped of context degrades a model, so preserving meaning across systems is a prerequisite for AI-ready data.