As the global analytics market continues to expand, driven by the increasing prominence of real-time and streaming analytics, businesses are exploring various types of data analysis to gain insights and make informed decisions. Historical data analysis, encompassing descriptive, predictive, and prescriptive analytics, allows companies to understand past trends, forecast future scenarios, and prescribe actionable strategies. Descriptive analytics summarizes past data to convey key themes, predictive analytics forecasts future outcomes using data mining and machine learning, while prescriptive analytics suggests decision-making actions. In contrast, real-time analytics processes data as it arrives, providing immediate insights and enabling swift responses to ongoing business operations. Despite the advantages of real-time data visualization and competitive edge, challenges such as system compatibility and the need for restructured workflows persist. The rise of real-time business intelligence is creating new opportunities for innovation, operational monitoring, and customer engagement, as evidenced by companies like Viacom, the City of Chicago, and BuzzFeed, which utilize real-time analytics to enhance service delivery and user experiences. As technology evolves, open-source tools like the ELK Stack are gaining traction for their ability to centralize and visualize log and machine data in real time, highlighting the growing importance of data-driven decision-making in an increasingly information-rich environment.