Widely spread breast cancer takes patients to an early grave. Early detection and ability to
predict the effectiveness of treatments are among the means to fight this malignant
disease. Data federation from dozens of data sources is needed for data
analytics. The granularity, internality, structure and all other characteristics
differ in federated data. We discuss alternative approaches to data federation
and their theoretical basis, especially the ontology and governance of data. We
developed an artifact in our on-going research. The artifact is used to support
the federation of cancer data at a university hospital. We detected that our federative
approach and the artifact improved the interoperability of data in the case. We
suggest that our approach is capable to that also in other contexts.