Home | english  | Impressum | Datenschutz | Sitemap | KIT

Co-opetitive Data Alliances - Barriers and Solutions to inter-organizational data pools and exchange.

Co-opetitive Data Alliances - Barriers and Solutions to inter-organizational data pools and exchange.

Wolfgang Badewitz (FZI),
Dr. Henrik Fälsch (BASF)

Enterprises are increasingly engaging in their digital transformation affecting all levels of their supply chain from supplier to end-customer. Sharing, aggregating and analyzing data across the value chain can have positive business impact for all parties. Being part of a partner ecosystem like this can become a differentiator and strategic imperative.

Unfortunately, not every party has access to all available and relevant data since a lot of potentially useful data is controlled by their environment: companies or customers that are before or after them in the supply chain. As it becomes more important to gain access to and make use of the data, the question becomes prominent, how to design the economical side of inter-organizational data exchange and cooperation in regards to new joint business approaches. Market Engineers have to incorporate technical aspects from data governance as well as the strive for revenue and the proper treatment of risks out of a business view.

Research Task:

In the course of this master thesis, an economic analysis of relevant industries with respect to the data value chain should be conducted and potential barriers to the successful implementation of cooperative data pools and their data monetization concepts should be identified. Business models will have a non-monetary ”Data for Data” element but also direct monetization component as new additional value-adding services can be developed.

Use cases of BASF should be elaborated in this context. Further, the thesis should investigate how these barriers could be overcome by the use of incentive mechanisms, game-theoretic modeling and the design of institutions. Relevant contacts into BASF will be provided. In the context of this thesis, it is also possible to conduct an internship at BASF (not mandatory).

Required skills/interests:

Data Science, Economical Analysis, Autonomy, Conceptualization and Organization Skills

Other requirements:


For questions, do not hesitate to contact Wolfgang Badewitz. To apply for the thesis, please send a current transcript of records, a short CV, and a brief motivation (2-3 sentences) to badewitz@fzi.de.