Towards a policy tuning method for data ecosystems

Data ecosystems are often used in many social, industrial, and research areas to boost the learning process or enhance the value of the endless amount of data generated and collected daily. Their design principles are consolidated, but today, data ecosystems must address new needs, as they are faced with multiple interdependent challenges, such as users' engagement, intellectual property, data confidentiality, and data sharing. The achievement of these business objectives highly depends on the deployed policies that govern many, if not all, aspects of the data ecosystems. Identifying the role of these policies in achieving data ecosystems' objectives is key to their success. This paper analyzes policies and their relations to define how they impact the business objectives of the systems and, possibly, to improve them. The proposed method highlights the interdependencies between different policies. Such results will be leveraged to tune policies to achieve data ecosystems' business objectives.