Data warehouses (DW) form the backbone of data integration that is necessary for analytical applications, and play important roles in the information technology landscape of many industries. We introduce an approach for addressing the fundamental problem of semantic heterogeneity in the design of data integration requirements during DW development. In contrast to ontology-driven or schema-matching approaches, which propose the automatic resolution of differences ex-post, our approach addresses the core problem of data integration requirements: understanding and resolving different contextual meanings of data fields. We ground the approach firmly in communication theory and build on practices from agile software development. Besides providing relevant insights for the design of data integration requirements, our findings point to communication theory as a sound underlying foundation for a design theory of information systems development.
['Christoph Rosenkranz', 'Roland Holten', 'Marc Räkers', 'Wolf Behrmann']
Supporting the design of data integration requirements during the development of data warehouses: a communication theory-based approach