Heuristic Evaluation of MetaViz: Insights and Strategic Recommendations to Elevate User Experience in an Automated Data Visualization Platform

In the era of big data, effective data visualization tools are essential for deciphering complex information across numerous domains. This paper evaluates MetaViz, a novel visualization tool designed to address the gap between theoretical principles and practical application in data visualization. MetaViz employs a Model-Driven Architecture (MDA) framework to facilitate intuitive and efficient dashboard creation, aiming to improve decision-making processes by enabling users to generate insightful and dynamic visualizations. Through a heuristic evaluation, this study identifies critical usability issues within MetaViz, including challenges in user interaction feedback, interface design, and navigation. The findings highlight the necessity of enhancing the tool’s design to meet user expectations better and to provide a more seamless, engaging experience. Proposed improvements focus on refining the tool’s feedback mechanisms and interface design to foster a deeper understanding of data visualization principles among users. The paper concludes with insights into the potential impact of these enhancements on user experience and the tool’s effectiveness in navigating complex data landscapes, setting a direction for future research and development.