This research presents an advanced framework, Optimized Decision-Making using Big Data Analytics (ODM-BDA), to address challenges in extracting actionable insights from large-scale enterprise data. The system integrates structured and unstructured data using scalable distributed architectures for efficient processing. It employs machine learning algorithms such as Random Forest, K-Means Clustering, and Logistic Regression to predict consumer behavior and operational trends. A key feature is the Backtracking-Based Risk Management mechanism, which ensures safe decision-making by reverting risky strategies. Additionally, optimization techniques enhance model training speed and data handling efficiency. Overall, the framework provides a real-time, reliable decision support system to improve business agility and reduce uncertainty.

This paper has presented the ODM-BDA (Optimized Decision-Making using Big Data Analytics) framework, a comprehensive AI-integrated platform for transforming raw data into actionable strategic decisions. The framework addresses critical gaps in existing big data analytics systems by integrating risk-aware decision support, gradient descent optimization, and ethical compliance monitoring within a unified modular architecture. Experimental evaluation on a 100-record sales dataset validated the system across all seven modules: 100% data quality, 8 strong correlations (max r=0.987), Enterprise Risk Score 77 with 9 critical exposures and projected ALE of $4.6M, optimized expected return of 19.24%, average decision confidence of 77.7%, 97% security score, and full COMPLIANT status across all compliance domains. The ODM-BDA framework demonstrates that integrating backtracking-based risk traversal with gradient descent optimization and AI-driven decision ranking produces substantially more comprehensive and actionable analytical outputs than conventional platforms. By treating security, bias detection, and ethical compliance as first-class citizens of the analytical pipeline, ODM-BDA provides a foundation for responsible AI deployment in enterprise environments spanning financial services, retail analytics, healthcare operations, and government intelligence.

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