While the healthcare landscape continues to evolve, rural-based hospitals face unique challenges in providing quality patient care amidst resource constraints and geographical isolation. This study evaluates the impact of big data analytics in rural-based hospitals in relation to service delivery and shaping future policies. Evaluating the impact of big data analytics in rural-based hospitals will assist in discovering the benefits and challenges pertinent to this hospital. The study employs a positivist paradigm to quantitatively analyze collected data from rural-based hospital professionals from the Information Technology (IT) departments. Through a comprehensive evaluation of big data analytics, this study seeks to provide valuable insights into the feasibility, infrastructure, policies, development, benefits and challenges associated with incorporating big data analytics into rural-based hospitals for day-to-day operations. The findings are expected to contribute to the ongoing discourse on healthcare innovation, particularly in rural-based hospitals and inform strategies for optimizing the implementation and use of big data analytics to improve patient care, decision-making, operations and healthcare sustainability in rural-based hospitals.

Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.