The utilization of data presents significant opportunities for enhancing business models and generating value. Consequently, the integration of data into value creation processes has given rise to the emergence of data-driven business models. This study aims to delineate and introduce diverse classes of data-driven business models. Leveraging the conceptual frameworks of data value networks and ecosystem players, we propose a theoretical foundation for understanding data-driven business models. The synthesis of these frameworks elucidates a spectrum of activities within the value network and underscores the diverse roles of stakeholders in extracting value from data. Employing thematic analysis, we synthesized insights from case studies and empirical articles to develop profiles for nine distinct data-driven business models. Furthermore, thematic analysis was employed to analyze empirical studies, facilitating detailed descriptions of each model. Utilizing the i* ontology, we modelled the nine identified business models, providing a structured taxonomy for classification. Additionally, we conducted a comparative analysis of our proposed taxonomy with existing research, thereby validating its robustness and relevance. Real-world evidence was also leveraged to further validate the applicability and efficacy of various data-driven business models.