Harnessing Emulation Models and Big Data Analytics for Enhanced Business Intelligence and Competitive Edge

This research paper investigates the application of advanced emulation models and AI-driven big data analytics to enhance business intelligence and competitive advantage. The study integrates sophisticated analytical methodologies to provide actionable insights for strategic decision-making. The research encompasses three primary emulations: Prognostic Modelling, Sentiment analysis, and Customer Segmentation, each designed to explore different aspects of business operations and decision-making processes. The study examines the impact of prognostic modelling on inventory management and sales forecasting. Model can optimize supply chain operations and improve performance. The study also includes customer segmentation analysis, utilizing clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN to identify distinct customer segments and their characteristics. This segmentation informs targeted marketing strategies and personalized experiences. Additionally, sentiment analysis models, including VADER and BERT, assess customer perceptions by analyzing reviews and feedback, providing insights into product quality, customer service, and overall satisfaction. The findings reveal that integrating these advanced methodologies significantly enhances business intelligence, enabling organizations to anticipate trends, manage risks, and tailor strategies to meet customer needs. The research underscores the importance of adopting sophisticated data analytics techniques to drive strategic decisions and achieve sustainable competitive advantage.