"Business Intelligence and Data Mining: A Review of Tools and Techniques"

Abstract

In the rapidly evolving landscape of data-driven decision-making, Business Intelligence (BI) and Data Mining (DM) have emerged as pivotal fields in harnessing the power of data to gain actionable insights. This paper provides a comprehensive review of contemporary tools and techniques in BI and DM, examining their methodologies, applications, and impact on modern businesses. The review begins with an overview of key BI concepts and the role of data warehousing, OLAP, and visualization tools in transforming raw data into meaningful information. It then delves into data mining techniques, including classification, clustering, regression, and association rule mining, highlighting their practical applications and limitations. By evaluating popular software tools and platforms in both domains, the paper outlines the strengths and weaknesses of each, offering insights into their suitability for various business needs. The review also addresses emerging trends and future directions in BI and DM, emphasizing the integration of artificial intelligence and machine learning to enhance predictive analytics and decision support systems. This paper aims to provide a valuable resource for researchers, practitioners, and organizations seeking to leverage BI and DM tools and techniques to gain a competitive edge and drive informed decision-making.