For years it has been a well-known fact that data analysis projects spend only a small fraction of time on actual analysis. Much more time is spent gathering, integrating and preparing the data for analysis. Still, many data analysis tools focus on the analytical parts only. In this talk we will present the core technology behind KNIME, an open source integration and analysis platform. In addition to offering comprehensive built-in ETL, analysis and visualization methods, KNIME's open API facilitates the integration of other tools. The underlying modular architecture enables a coherent and transparent fusion of the diverse data sources spread out over the corporate IT environment, while at the same time integrating existing legacy tools and other data processing and analysis methods. We will show real-world examples of KNIME being successfully deployed as an integration and analysis backbone and how it can be used to quickly deploy new science, e.g. new methods for the analysis and exploration of data at the same time. We will also take the time to provide a brief overview of how the graphical, modular representation of a data workflow enables complex data processing and analysis procedures to be documented, archived and communicated.