Data governance for platform ecosystems

Platform ecosystem has become an information system research subject after many years of industry success. The concept facilitates fast and self-growing of a platform by encouraging data contribution and consumption of multiple networks. It is essential to understand how data should be managed in platform ecosystems where there are complicated relationships. However, this topic has been rarely addressed in both industry and academia. Prior research is still in early-stage. To respond to this, we proposed a novel data governance framework based on a design science research approach which delivers a set of guidelines and practical disciplines to support right decision making. A contingency model is proposed as an operating model of the framework to support designing and implementing data governance. It describes the influence of the context and characteristics of a platform ecosystem on data governance design. In this study, we used qualitative research within a design science approach which is mainly based on a literature review and case studies. The case studies are carried out to understand how and if industry platforms are addressing the theoretically important factors of the proposed framework in reality. Through the case studies, we confirmed that industry platforms consider data governance as a set of important decision areas. We have also found there are some limitations of the implementation of practices such as a lack of consideration of various types of data or proactive monitoring mechanisms. For the next steps, we will consider further case studies as there are limited cases are examined in this study