Vol. 1, Issue 1, Part A (2024)

As data-driven decision-making becomes the foundation of modern business strategy, the need for effective analysis of complex, high-dimensional datasets intensifies. Traditional statistical methods, while foundational, are often insufficient to unravel the intricate structures embedded within multivariate business data. Visual Data Mining (VDM), an intersection of data mining, information visualization, and human-computer interaction, has emerged as a vital approach in Exploratory Data Analysis (EDA). This paper provides an extensive overview of VDM techniques and their application to multivariate business datasets. We discuss the theoretical foundations, common techniques, tools, applications, and challenges. We further evaluate emerging research directions, including AI integration, user-personalized visualizations, and scalable, interactive interfaces. The paper draws on recent academic literature and case examples to illustrate how visual approaches can amplify analytical capabilities and enhance strategic insight in business contexts.