The advent of real-time data analytics has revolutionized decision-making processes across various industries, offering businesses the potential to respond swiftly to market changes, consumer behaviours, and operational demands. However, while the benefits of real-time data are evident, practical challenges persist that hinder its effective interpretation and integration into decision-making. It explores the core obstacles faced by organizations in interpreting real-time data and transforming it into actionable decisions. The Key issues include data overload, the accuracy and reliability of data, the need for real-time decision-making frameworks, and the integration of AI tools in the analysis process. By research addresses organizational challenges such as skill gaps in data analytics, resistance to technology adoption, the balancing act between automation and human judgment in decision-making. As employs a mixed-methods approach, combining qualitative interviews with industry professionals and quantitative data analysis to assess the impact of these challenges on organizational outcomes. Findings suggest that overcoming these barriers requires not only technological upgrades but also a restructuring of decision-making protocols, increased investment in talent, and fostering a data-driven culture. The researcher in his research aims to provide strategic recommendations for businesses to enhance the efficacy of real-time data interpretation, streamline decision-making processes.