Enterprise Integration and Interoperability Improving Business Analytics

Abstract

In applied research and industrial business analytics (BA) projects data preparation requires around 80% of the total effort. Preparation tasks include establishing technical, semantic interoperability of data and processes to generate value. Enterprise Integration and Interoperability (EI2) approaches address these challenges, but these approaches are hardly taken into account in business analytics. In this position paper, we analyse approaches for their contribution to improving business analytics by supporting the interoperability of data, services, processes and business in general. For more details, we focus on the application domain of smart grids. Existing and missing tool and methodological support as a basis for data-access required for efficient and effective descriptive, predictive and prescriptive business analytics.