storytelling

Existing tools neglect the central role of narrative in data analysis, resulting in non-traceable reasoning, fragmented reflection, and incoherent explanations. This paper introduces the “narrative-as-interface” paradigm, proposing an exploratory framework where narrative generation serves as the primary interaction mechanism—integrating questioning, visualization, reflection, and explanation into an iterative narrative flow. Key contributions include: (1) semantic-aligned automatic view generation; (2) a narrative-first input interface; (3) insight provenance modeling; and (4) inquiry lifecycle tracking. A user study (N=20) demonstrates a 37% increase in exploration breadth and a 2.1× improvement in reflection depth. Expert evaluation (N=6) confirms 92% alignment in narrative intent, substantiating significant advances toward traceable, defensible, and intention-aware data analysis practices.

Narrative-driven data exploration faces core challenges—including contextual discontinuity across views, difficulty in tracing analytical reasoning paths, and insufficient externalization of intermediate interpretations. Method: We conducted a qualitative empirical study with 48 participants, combining in-depth interviews and task-based observations, to code and thematically analyze multi-stage dynamic analytical behaviors. Contribution/Results: The study systematically identifies three critical impediments and derives three design principles for supporting narrative evolution in visual analytics: (1) enforcing cross-view contextual consistency, (2) explicitly tracking reasoning trajectories, and (3) structurally externalizing intermediate interpretations. These principles are operationalized into concrete interaction mechanisms and practical guidelines. The work advances visual analytics systems from static chart presentation toward next-generation tools that actively support dynamic, iterative narrative construction.

To address the challenge of organizing discrete insights from tabular data into coherent visual narratives, this paper proposes a dual-module framework. First, a structured insight graph enables efficient retrieval based on relational and semantic criteria. Second, a large language model (LLM)-driven semantic reasoning module integrates structural filtering with retrieval-augmented generation (RAG) to deliver dynamic, user-intent-aligned insight recommendations. The framework supports interactive, goal-oriented data storytelling, allowing users to iteratively refine narrative logic. Evaluated through case studies and user experiments, the system significantly improves storytelling efficiency—reducing construction time by 42% on average—and enhances narrative quality, increasing coherence and insight coverage by 37% and 31%, respectively. Moreover, it strengthens users’ ability to comprehend and manipulate complex insight interrelationships.

Data-driven narrative creation faces challenges in visual-textual co-design and weak semantic alignment. To address these, we propose DataWeaver, an interactive data narrative authoring framework that introduces the first bidirectional vis-to-text and text-to-vis co-generation paradigm. It anchors narrative semantics via user-initiated “call-out” interactions—highlighting key data points—to jointly govern narrative logic and chart generation. The framework integrates interactive visualization, controllable natural language generation (NLG), context-aware visualization recommendation, and cross-modal semantic alignment modeling. A 13-participant user study demonstrates its effectiveness: DataWeaver improves narrative authoring efficiency by 42% over baseline tools, and significantly enhances logical-chart consistency (p < 0.01). This work provides a scalable, methodology-driven system for accessible, high-fidelity data storytelling.

Existing data-analytic models, grounded in positivism, neglect critical dimensions of power, tacit knowledge, and cognitive schemata. Method: This paper proposes an interpretivist “iceberg model of meaning construction” (Add-Check-Refine), treating data as schematized artifacts and distinguishing explicit from implicit cognitive schemata; it emphasizes schema primacy, multiplicity, and epistemic humility. Validation employs historical conceptual analysis and four empirically grounded scenarios—e.g., sensor measurement bias and data neglect—to demonstrate interpretivist coherence and explanatory power. Contribution/Results: The model precisely identifies canonical analytical dilemmas while offering actionable remediation pathways. Crucially, it constitutes the first systematic integration of humanistic critique with data practice, thereby establishing both theoretical foundations and methodological scaffolding for institutionalizing interpretivism within data science.

Automated end-to-end data science suffers from “chart-rich but insight-scarce, rigid reporting” bottlenecks. Method: This paper proposes a two-stage multi-agent pipeline: the Analyzer agent performs data profiling, generates diverse visualizations, conducts readability validation (via hybrid rule-based and LLM-driven checks), and scores insights across dimensions—depth, correctness, and actionability; the Presenter agent organizes themes, plans narrative structure, and refines documentation into coherent, professional reports. Contribution/Results: We introduce the first analyzer–presenter collaborative paradigm that unifies automated insight quality assessment with narrative-level report generation. The system integrates code-generation-and-execution agents, hybrid readability validation, a multidimensional insight scoring model, and a narrative planning agent. Evaluated on multiple real-world datasets, it significantly outperforms single-stage baselines and enables out-of-the-box generation of publication-ready visualization reports.

This study addresses the challenge of translating user research (UXR) data into strategically impactful insights within complex developer tooling contexts—such as AI agents, command-line interfaces, and error messaging—where traditional approaches often fall short. To bridge this gap, the work proposes a mixed-methods research framework that triangulates qualitative and quantitative data to produce high-confidence findings. Central to this approach are three structured “playbook cards”—Paradigm Shift, Explainability as Trust, and Friction Cost—that transform technical observations into compelling, irrefutable business narratives. By operationalizing a reusable pipeline from raw insight generation to strategic viewpoint formulation, this framework significantly enhances the influence and persuasive power of UXR in technology product decision-making.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

This study addresses the limited real-world impact of current explainability research, which often fails to support actionable decision-making and interventions in practical settings. To bridge this gap, the work proposes a novel, systematic redefinition of explainability centered on "actionability," articulated through two key dimensions: concreteness and verifiability. Building upon this reconceptualization, the authors develop an application-oriented evaluation framework grounded in both conceptual analysis and cross-domain use cases. Through this integrative approach, they identify five distinct domains with high potential for impactful deployment. By prioritizing tangible outcomes over abstract interpretability, the proposed framework offers both theoretical grounding and practical pathways to enhance the real-world relevance and effectiveness of explainable AI research.

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.