А COMPARATIVE ANALYSIS OF DECISION SUPPORT PLATFORMS

Abstract

Unified Information Platforms for supporting Intelligent Decision-Making Systems (IDMSS) play a crucial role in modern organizations worldwide, providing powerful tools for data analysis and business process optimization. This review examines the existing IDMSS, their methodologies, and their impact on decision-making processes. Platforms such as IBM Watson, Microsoft Azure AI, and Google Cloud AI have proven their effectiveness across various industries, including healthcare, finance, and manufacturing. These platforms offer unique capabilities, such as machine learning, natural language processing, and big data analytics, which significantly enhance the accuracy and speed of decision-making. Practical examples illustrate how organizations leverage these platforms to improve decision outcomes. For instance, in healthcare, IDMSS help improve diagnostics and personalize treatments, while in the financial sector, they aid in risk management and fraud detection. The review also introduces a new IDMSS platform developed by a team of experienced professionals. This innovative platform features advanced data integration, flexible machine learning models, and an intuitive user interface. It promises to transform the IDMSS landscape by offering more precise and rapid solutions to complex problems. Its advantages include increased scalability, improved data security, and adaptability to specific industry requirements. Thus, the new IDMSS platform has the potential to significantly influence the future of intelligent decision-making systems, offering innovative approaches and enhanced results. This makes it a valuable tool for organizations striving for excellence in their operations and underscores the importance of IDMSS in today’s data-driven world.