Inter-Organizational Data Sharing Processes - An Exploratory Analysis of Incentives and Challenges

Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational data sharing is proposed, e.g. in the form of data ecosystems. The aim of this study was to perform an exploratory investigation into the data sharing practices that exist in business-to-business (B2B) and business-to-customers (B2C) relations, in order to shape a knowledge foundation for future research. We launched a qualitative survey, using interviews as data collection method. We conducted and analyzed eleven interviews with representatives from seven different companies across several industries with the aim of finding key practices, differences and similarities between approaches, so we could formulate the future research goals and questions. We grouped the core findings of this study into three categories: organizational aspects of data sharing, where we noticed the importance of data sharing and data ownership as business driver; technical aspects of data sharing, related to data types, formats, maintenance and infrastructures; and challenges, with privacy being the highest concern along with the data volumes and cost of data.