The purpose of this research study was to identify hurdles and challenges pegged in the way of inclusive big data analytics and big data health policymaking. The study aimed to propose a data-driven model for big data integration into the health policymaking process. For this purpose, the author adopted the usage of both primary and secondary data through in-depth interviews and content analysis (both quantitative and qualitative analysis). The empirical results showed that there are several hurdles and challenges when it comes to big data integration in the healthcare and health policy-making process such as resistance towards change, data privacy and security, interoperability, expertise and infrastructure, management and governance, data quality and reliability, financial constraints, lack of capacity, government priorities and political will. Based on the empirical findings, the Health Data Cooperative (HDC) model was proposed, which is a key element and main component for public health policymaking and for leveraging the potential of rapidly emerging and all the existing data sources. In order to exploit all the data needs to overcome the technological algorithmic, computational challenges that define the present highly heterogeneous data environment, along with heaps of complex normative, regulatory, governance and policy constraints. Consequently, HDC offers a roadmap to address technological, political and social hurdles and empower healthcare and health policymaking by incorporating and implementing big data. Key recommendations and solutions concerning big data integration in the healthcare and health policymaking process was presented at the end of this study. Supervisor: Dr. Attiya Yasmin Javid Co- Supervisor: Dr. Fazli Hakim Khattak