This master thesis investigates the applications, opportunities, and challenges of big data analytics in healthcare systems. The study combines a systematic literature review with an empirical case study to examine how data driven analytics can support decision making in the healthcare and pharmaceutical sectors. The research explores the role of big data in healthcare applications such as medical imaging, disease monitoring, telemedicine, and pharmaceutical services. Particular attention is given to the potential of data analytics to improve healthcare outcomes, reduce costs, and enhance operational efficiency in healthcare systems. The empirical component of the study analyzes the relationship between pharmacy location and prescription drug sales. Using a dataset covering 1475 pharmacies and 212 hospitals in the Moscow pharmaceutical market, the study tests whether the distance between pharmacies and hospitals is associated with pharmacy sales performance. Statistical correlation analysis and regression techniques were implemented using Python and R. The results indicate a statistically significant but weak relationship between the distance to the nearest hospital and the volume of prescription drug sales. The findings suggest that location characteristics and competitive dynamics among nearby pharmacies play an important role in determining sales outcomes. The research highlights both the potential and the limitations of applying big data analytics in healthcare decision making. Overall, the thesis contributes to the understanding of how large healthcare datasets can support evidence based strategies in pharmaceutical retail, healthcare management, and digital health innovation.