Bridging the analytics gap: A platform-based framework for SME data analysis

Abstract

This research addresses the critical analytical gap facing Small and Medium Enterprises (SMEs). Despite generating substantial operational data, most SMEs lack the technical expertise and resources to extract actionable insights. This study presents a web-based platform designed to bridge this divide by democratizing access to advanced machine learning without requiring programming skills. The proposed framework integrates Exploratory Data Analysis (EDA), clustering algorithms, and classification models within an intuitive interface. The system automates complex technical operations, such as data preprocessing and algorithm selection, enabling non-technical users to perform sophisticated tasks like customer segmentation and predictive analytics. By removing technical barriers, this platform empowers entrepreneurs to transition from intuition-based to data-driven decision-making, optimizing operations and enhancing competitiveness in the modern digital economy.