Fallstudien zum Technologie und Innovationsmanagement
Book Chapter: Activities and Challenges in Environmental Scanning
3. June 2019
Innovation Posters Teaser
Must-have Innovation Posters
6. June 2019

Journal Article: Big Data in Innovation Management

How machine learning is revolutionizing the search for trends and technologies

In April 2019, Carolin Durst and Christian Mühlroth, ITONICS GmbH, together with Laura Kölbl & Michael Grottke, FAU Erlangen-Nuremberg, and Fabian Wiser, TU Braunschweig, published the article “Big Data in Innovation Management: How Machine Learning revolutionizes the search for trends and technologies” in the magazine “HMD Praxis der Wirtschaftsinformatik”. The article is part of the research project “RADAR: Data-driven environmental scanning for tomorrow’s decisions” funded by the Federal Ministry of Education and Research. In this research project, ITONICS is actively involved in the development of an automated environmental scanning system for SMEs.


Today, innovation management is an important instrument for companies to remain competitive and successful in rapidly changing markets. Large amounts of data are available for this purpose, from which the relevant information must first be filtered out. This paper presents the results of a study on the challenges to successful innovation management in companies and introduces an environmental scanning system that increases the efficiency of innovation management using big data analytics. Through the application of modern methods of machine learning and mathematical algorithms, this can be highly automated. The subsequent case study uses two sets of data to show how the environmental scanning system can be applied to find emerging trends. Finally, the data-based environmental scanning system will be discussed as an opportunity for small and medium-sized enterprises and as a solution to the challenges identified in advance.


This contribution was made as part of the research project RADAR (Data-Driven Environment Scanning for Tomorrow’s Decisions), which is funded by the BMBF under the number 02K16C190.