Metadata-Driven Data Interoperability

Digitalization and smart systems increasingly rely on data from multiple heterogeneous sources. These data are often structured very heterogeneously, even if they contain related information. This lack of data interoperability-the ability to combine data-hinders systems from effectively interpreting data from multiple sources, resulting in missed opportunities where data integration could make systems smarter. For example, measurements with weather data can lead to better irrigation plans. Integrating diverse sources raises many challenges in ownership, responsibility, and privacy. This study investigates a metadata-driven data interoperability approach (MDDDI) to solving technical, organizational, ethical, and regulatory obstacles to data interoperability, revealing that existing solutions do not fully address these issues. By defining key metadata types and outlining interactions among actors, the domain-agnostic MDDDI reference schema, demonstrated with a synthetic smart greenhouse example, helps to bridge isolated systems by embedding all necessary integration details in the metadata. The approach is discussed based on a synthetic smart greenhouse example.