DIVE - A Neuro-Symbolic and User-Centered Approach for Data Insight Visualization

Web-based dashboards are fundamental to digitalization, serving as central components in various organizational processes. However, their development is typically restricted to software engineering teams who must translate domain-specific requirements from stakeholders into functional interfaces. Despite this process often being repetitive and structured in nature, it remains complex and largely inaccessible to non-experts. While template-based tools such as Power BI increase accessibility, they frequently lack the flexibility needed for highly customized and adaptive solutions. To overcome these limitations, we present DIVE (Data Insights and Visualization Engine), a novel stakeholder-driven, hybrid neuro-symbolic framework for generating highly adaptable dashboards. By leveraging natural language interaction and flexible Knowledge Graph (KG) structures, DIVE empowers users to define, query, and visualize data without programming knowledge. At its core, DIVE integrates an interaction of symbolic and neural techniques together with a Retrieval-Augmented Generation (RAG) architecture that integrates coding-relevant context to generate precise queries and synthesize dynamic User Interface (UI) components in real time. To assess DIVE’s effectiveness, we conducted a mixed-method evaluation using the NASA Task Load Index (NASA-TLX) and the System Usability Scale (SUS), supplemented by participant and expert interviews for qualitative insights. The results demonstrate that DIVE supports completing complex analytical tasks with high usability and low cognitive load. Study participants and experts emphasized the system’s innovative approach, practical applicability, and potential for substantial time and cost savings, thus confirming its capacity to produce customizable, future-proof dashboards.