Machine Learning for Strategic Business Intelligence: A Short Review

Integrating machine learning (ML) with business intelligence (BI) systems marks a significant advancement in data analysis and decision-making, allowing for more dynamic, data-driven insights and strategies. This paper explores the applications, limitations, and future directions of applying ML in business contexts, aiming to enhance efficiency, accuracy, and competitiveness. By leveraging cutting-edge ML techniques such as predictive analytics, natural language processing (NLP), and automated decision-making, businesses can gain deeper insights and make more informed decisions. This discussion encompasses various ML applications in BI, including sales forecasting, customer feedback analysis, and financial anomaly detection, highlighting their potential to optimize different aspects of business operations. The paper also addresses the challenges of integrating ML into BI, such as data quality, computational resource requirements, and model interpretability, along with ethical and privacy concerns. By examining the latest research and practical implementations, this paper aims to provide a comprehensive roadmap for leveraging ML in business intelligence, ultimately driving innovation and growth in the modern business landscape.