Goal-Driven Data Story, Narrations and Explanations (2025)

Abstract: In this paper, we propose a system designed to process and interpret vague, open-ended, and multi-line complex natural language queries, transforming them into coherent, actionable data stories. Our system’s modular architecture comprises five components—Question Generation, Answer Generation, NLG/Chart Generation, Chart2Text, and Story Representation—each utilizing LLMs to transform data into human-readable narratives and visualizations. Unlike existing tools, our system uniquely addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles. Our system is cost-effective, which uses open-source models without extra training and emphasizes transparency by showcasing end-to-end processing and intermediate outputs. This enhances explainability, builds user trust, and clarifies the data story generation process.

## introduction business intelligence (bi) is critical for enterprise decision-making across functions like sales, hr, and it. traditionally, bi relied on static dashboards, manually crafted sql queries, and complex laborintensive workflows that were effective but rigid and required technical expertise, limiting in-depth or exploratory analysis. the advent of large language models (llms) has transformed bi, raising user expectations for systems that process natural language, handle numerical data, and address complex, multi-faceted queries with intuitive, narrative insights aligned with business goals. while ai and llms have been integrated into bi systems, they have primarily handled simpler queries. modern bi users now demand more sophisticated systems capable of interpreting intricate natural language requirements and providing comprehensive, engaging, and easily understandable answers supported * all authors contributed equally to this work. by visual analytics. this growing demand highlights the need for solutions that bridge the gap between complex data analysis and human interpretability, enabling seamless communication of insights without technical expertise @xcite . we term these insights or narratives data stories. data storytelling merges data analysis, visualization, and qualitative insights into a unified narrative that highlights the broader significance of data @xcite . unlike conventional business intelligence, it focuses on aligning insights with business objectives and user context. by leveraging visual aids and addressing various learning styles, this approach enhances comprehension, making complex data more accessible and engaging. large language models (llms) have demonstrated near-human performance in text-based applications, but their capabilities in handling numerical data, complex reasoning, and domain-specific queries remain limited. existing approaches, such as text-to-sql @xcite and table qa @xcite @xcite provide only partial solutions to the needs of bi users. these methods often struggle with vague, multi-line queries that require nuanced understanding, advanced numerical reasoning, and the seamless integration of interconnected insights (figure 1 ). while table qa methods can handle more complex queries, they are not scalable to enterprise dataset because of their dependency on input data in prompt. innovations like the agentic framework @xcite show promise in addressing some of these challenges, but they heavily depend on the design and functionality of underlying tools. as a result, these frameworks often fall short in generating detailed, comprehensive narratives enriched with visual analytics, without requiring significant effort in constructing and optimizing the tool pipeline. to address these challenges, we propose a goaldriven data story narration system that transforms figure 1 : an example of data storytelling -a complex bi ask and its associated response generated by our system vague, open-ended, multi-line queries into structured, coherent, and actionable data stories. it goes beyond existing approaches by offering a holistic framework to resolve ambiguous queries through systematic sub-query generation, extraction of relevant data insights, and seamless narrative presentation tailored to user intent. the system's modular architecture includes: question generation, which plans the narrative framework by formulating pivotal questions; answer generation, which provides reliable responses; nlg/chart generation, which translates insights into text or charts; and summarization, which compiles the output into a coherent narrative. each module operates synergistically to create human-readable, verifiable outputs. our system is distinct in its ability to handle vague, multi-line queries systematically, ensuring transparent, data-driven results. through intermediate transparency and evidence-based storytelling, it fosters trust and usability. its use of open-source models makes it cost-effective, scalable, and accessible to enterprises of all sizes while its modular architecture makes easy to integrate into existing bi solutions. we conduct a human evaluation focusing on relevance, readability and presentability metrics, and our system excels on all these metrics ( ## data story generation the system is initiated when a user queries tabular data using a natural language utterance. this query is processed through a series of modules, as detailed in figure 2 , culminating in a compre-hensive data story presented through text and infographics. these modules leverage llms and prompt engineering in a zero-shot setting, ensuring the pipeline's versatility across various domains without requiring fine-tuning. for reproducibility, the prompts used in our pipeline are provided in appendix a. ## evaluation in order to measure the effectiveness of our approach, we use human evaluation, which is a critical component of our research for several reasons. firstly, there is a lack of specialized labeled datasets for vague, multi-line queries that can effectively measure the nuanced capabilities of data storytelling systems. secondly, the subjectivity inherent in evaluating the quality of data stories means that automated metrics alone cannot fully capture the relevance, readability, and presentation of the generated narratives. traditional evaluation metrics often fail to address the qualitative aspects of human-centric tasks, such as the clarity and engagement of the produced content. moreover, evaluating complex data storytelling systems requires metrics that go beyond mere technical accuracy, encompassing dimensions like user satisfaction and the practical utility of the generated stories. existing metrics are frequently insufficient to gauge these subjective criteria effectively. ## path to deployment our system is designed for seamless integration, either within an existing bi system or as a standalone service for bi tasks. its modular architecture ensures easy deployment and cost-effectiveness, leveraging open-source models. the deployment can be carried out in the following two ways. integration of entire pipeline with existing bi systems: the data story generation pipeline can be deployed as a streaming api that processes user queries and tabular data, producing narratives in incremental chunks. this approach mitigates long processing times by delivering partial data stories as they are generated, with continuous updates until completion. this strategy has been validated with an internal bi system that features a natural language query interface. in this system, our pipeline is integrated as an additional feature, accessible through an insights tab that triggers the streaming api. the data story, including both text and charts, is displayed in chunks, providing an interactive and dynamic interface to enhance user comprehension. module-wise deployment: components like the question generator, answer and chart generator, summarizer, and chart2text can be deployed independently as api endpoints. this flexibility enables integration into existing pipelines to address specific sub-problems, with each module functioning as a black box with defined inputs and outputs. in summary, the use of open-source models and a modular design offers the following advantages: cost-effectiveness and adaptability: opensource llms in zero-shot settings significantly cut costs compared to proprietary solutions, offering scalability and accessibility. emphasis on prompt design over fine-tuning enhances adaptability. ## future work and research challenges our system effectively addresses descriptive and, to some extent, diagnostic questions but has scope for growth in predictive and prescriptive analytics. expanding into these areas will enable forecasting and actionable recommendations, enhancing its utility. key challenges include integrating advanced forecasting techniques, designing recommendation algorithms, and addressing ethical concerns. additionally, building a comprehensive benchmark dataset will be crucial for evaluating system performance. such a dataset would provide a standardized framework for future research, enabling validation of data storytelling approaches and facilitating comparisons with other methods. furthermore, developing an automatic evaluation system to replace the time-consuming human evaluation process will ensure a more scalable, consistent, and efficient assessment of system performance. ## related work addressing complex, open-ended queries over tabular data has spurred research in nlp, database management, and data visualization. this section reviews progress in text-to-sql, data interpreta-tion, and narrative generation systems. text-to-sql systems enable non-technical users to query data by translating natural language into sql. early systems like seq2sql @xcite and spider @xcite focused on query translation. recent transformer-based models handle more complex queries but often lack the ability to generate actionable insights, particularly for enterprise-specific open-ended queries. tabular question answering systems answer queries directly from tables @xcite @xcite . while these systems perform complex reasoning, they suffer from limited accuracy due to reliance on llms and context length constraints, reducing their effectiveness for large datasets. insights extraction systems, such as insightpilot @xcite and jarvix @xcite , focus on extracting insights from data. insight-pilot aligns insights with specific goals, while jarvix combines automl tools for summaries and visualizations. systems like llm4vis @xcite and quis @xcite create visualizations and exploratory insights. however, these focus on isolated insights rather than cohesive data narratives. data story systems combine insights with narrative generation but often rely on llms, limiting scalability. for instance, datanarrative @xcite uses multi-agent systems to generate stories but struggles with large datasets. in contrast, our system employs deterministic sql execution for precise computations and meaningful narratives. related works also include datadriven storytelling from notebooks @xcite , articles @xcite , and autonomous agents in data-copilot @xcite . most existing systems, including datanarrative, are not open-source, hindering direct comparisons. furthermore, their benchmark datasets, often using small tables, fail to evaluate the scalability of our system effectively. ## conclusion in this paper, we propose a first-of-its-kind system to address vague, multi-line queries by integrating natural language processing with data analysis to generate comprehensive and interpretable data stories. our system prioritizes adaptability and transparency, offering a dynamic interface that adjusts content presentation and provides insights into the processing pipeline. this design enhances storytelling effectiveness while building user trust through explainability and access to intermediate outputs. by combining state-of-the-art llms with practical design considerations, our system marks a significant advancement in data storytelling, delivering a robust tool for generating actionable and understandable insights.