In modern enterprise systems, risk management has become increasingly complex due to the growing volume of data, interconnected infrastructures, and evolving cyber threats. Data analytics provides a powerful approach to identifying, assessing, and mitigating risks by transforming large datasets into actionable insights. This study explores the role of data analytics in enterprise risk management, focusing on techniques such as descriptive, predictive, and prescriptive analytics. It examines how organizations leverage data from multiple sources, including operational systems, financial records, and external data feeds, to detect anomalies, forecast potential risks, and support strategic decision-making. The paper also highlights the integration of advanced technologies such as machine learning and artificial intelligence to enhance risk detection, fraud prevention, and compliance monitoring. Key challenges, including data quality, integration complexity, privacy concerns, and model interpretability, are discussed along with effective mitigation strategies. The findings demonstrate that data-driven risk management significantly improves organizational resilience, decision accuracy, and operational efficiency in dynamic enterprise environments.\n\n