Interactive Data Visualization to Optimize Decision-Making Process

The increasing volume and complexity of data in today's business environment present significant challenges for traditional data analysis methods. Static reports and spreadsheets often fall short in providing comprehensive insights necessary for strategic decision-making. This paper addresses the critical issue of enhancing data comprehension and utilization through interactive data visualization. The primary objectives of this paper are to explore various visualization techniques, design and implement an interactive dashboard with a content-based recommender system, and evaluate its effectiveness in optimizing business processes. The content-based recommender system utilizes TF-IDF vectorization and cosine similarity to offer personalized product recommendations. The process includes data cleaning, preprocessing, and integrating the recommender system into the dashboard. Comprehensive evaluation through user testing and performance analysis ensures the vizualisations and recommender system's accuracy, responsiveness, and effectiveness. In addition, by leveraging descriptive analytics, the paper aims to create a user-friendly tool to explore data, uncover hidden patterns, and facilitate decision-making. The expected outcome of this paper is an interactive dashboard that significantly enhances data comprehension and strategic decision-making for businesses.