Data To Insights: Bridging the Gap between Data and Natural Language

Modern organizations generate vast amounts of data, which are used to generate valuable data insights. Such insights have been shown to enhance organizational performance, driving the growing adoption of Data‑Driven Decision‑Making. However, transforming datasets into actionable insights remains manual and dependent on dashboards, queries, and expert interpretation. Existing business intelligence systems and natural language interfaces have improved access to data but still require significant human effort to detect patterns, relationships, and anomalies. We introduce Data-to-Insights, a Large Language Model-based framework that autonomously converts structured datasets into grounded, interpretable natural language narratives. Data-to-Insights processes data through adaptive segmentation, cross-relationship analysis, and narrative synthesis, preserving semantic and relational context while enabling scalable reasoning. To ensure reliability, the framework incorporates multi-dimensional evaluation metrics assessing semantic alignment, factual accuracy, and inferential validity. By complementing traditional analytics pipelines with system-driven insight generation, Data-to-Insights supports faster, explainable, and outcome-focused decision-making, lowering the barrier for organizations to act on data at scale.