<![CDATA[In today’s interconnected world, organizations across industries face a wide range of risks, from cyber threats to economic crises, which demand agile, data-driven crisis management strategies. Knowledge Management (KM) systems have become essential in managing these challenges by enabling real-time decision-making through data-driven insights. This study examines the role of KM frameworks integrated with advanced data science techniques, such as sentiment analysis and big data analytics, in improving crisis management across various sectors. Additionally, emerging technologies, including Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and cloud computing, have been incorporated into KM frameworks to enhance risk mitigation, communication, and organizational resilience during crises. A cross-industry comparison reveals that while the finance sector has successfully integrated these technologies into its KM systems, other sectors, such as manufacturing, struggle with knowledge retention and data security. The study highlights the value of sentiment analysis in understanding stakeholder perceptions, which refines decision-making in crisis scenarios. The results indicate that KM practices contribute to a 60% reduction in risk, a 65% improvement in crisis resolution speed, and a 62% increase in organizational resilience. Furthermore, the integration of advanced technologies within KM frameworks reduces crisis response times by 82%. Despite these benefits, sectors like healthcare and manufacturing continue to face challenges in knowledge sharing and data security. The study emphasizes the importance of addressing these barriers and incorporating advanced technologies into KM frameworks to optimize crisis management effectiveness. These findings underscore the critical role of KM systems in strengthening organizational resilience, supporting proactive risk management, and enabling quick responses to future crises.]]>
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