Data-driven innovation has great potential for the development of innovative services that not only have economic value, but that help to address societal challenges. Many of these challenges can only be addressed by data sharing of public and privately owned data. These public-private data sharing collaborations require data governance rules. Data governance can address many barriers, for example by deploying a decision model to guide choices regarding data sharing resulting in interventions supported by a data sharing platform. Based on a literature review of data governance and three use cases for data sharing in the logistics sector, we have developed a data sharing decision model from the perspective of a data provider. The decision model addresses technical as well as ownership, privacy, and economical barriers to sharing publicly and privately owned data and subsequently proposes interventions to address these barriers. We found that the decision model is useful for identifying and addressing data sharing barriers as it is applicable to amongst others privacy and commercial sensitive data.