The persistent dominance of Facebook has led many scholars and policymakers to generate proposals to invigorate competition in social networking. In this piece we address a remedy that has received renewed attention: interoperability. Prior proposals of interoperability have focused on eroding entry barriers that exist due to user-based network effects. We focus here on data-generated network effects: the more data Facebook acquires from its users, the more its AI algorithms can learn and improve the content Facebook provides its users. Without access to a rich stream of user data, a social network is merely a static interface, with limited capacity to serve engaging or personalized content. As such, we propose a version of interoperability that addresses both user and data-driven network effects. In doing so, we also explicitly tackle the privacy issues that invariably arise whenever data is shared across firms.