Vast amounts of data are being generated daily with the adoption of Internet-of-Things (IoT) solutions in an ever-increasing number of application domains. There are problems associated with all stages of the lifecycle of these data (e.g., capture, curation and preservation). Moreover, the volume, variety, dynamicity and ubiquity of IoT data present additional challenges to their usability, prompting the need for constructing scalable data-intensive IoT data management and processing platforms. In this talk, I will present our recent efforts in modelling and building IoT data platforms based on an Actor-Oriented Database (AODB). We take advantage of two complementary case studies – in structural health monitoring and beef cattle tracking and tracing – to describe novel software requirements introduced by IoT data processing. Our investigation illustrates the challenges and benefits provided by AODB to meet these requirements in terms of modeling and IoT-based systems implementation. Obtained results reveal the advantages of using AODB in IoT scenarios and lead to principles on how to effectively use an actor model to design and implement IoT data platforms.