Innovation Systems Data – Excellence Center - ISDEC

Research on data-driven analysis

The core competence of Fraunhofer ISI lies in the evidence-based strategic consulting of a large number of actors from politics, economy and society using a wide range of data. This includes on the one hand typical innovation indicators such as patents or publications, economic and trade data. In addition, company-specific data on the implementation of strategies and R&D measures, as well as on innovation behavior and production processes, are collected in surveys by the institute itself or together with partners. A further established data and methodological focus is model-based systems analysis, in which transformation pathways can be developed that are used as a basis for policy recommendations and strategy development. For this purpose, Fraunhofer ISI has a comprehensive set of model instruments and an associated database, which is used for varius research projects at Fraunhofer ISI.


ISDEC aims to extend and further integrate the existing data and methodological competence of Fraunhofer ISI with new approaches in order to further strengthen the institute's evidence-based policy advice. The existing methodological competencies and data accesses are to be further developed with the planned project in order to systematically enable the integration and use of additional, especially unstructured data. The goal is to expand the existing competencies in the area of structured data by big-data analytics skills in order to make unstructured data available for analyses in the application fields of the entire institute. To this end, all Competence Centers will work together to strengthen their capabilities for the analysis of innovation systems.


Results are in preparation and are planned in the following areas:

  • Data access (structured data in the area of open access, unstructured data)
  • Data preparation (matching, cluster and classification methods, regionalization / geolocation
  • Data analysis (text mining, semantic and sentiment analysis, machine learning, visualization)




  • planned


2019 to 2022


  • Internal Research Project