DataMagic: Transforming Tabular Data into Data Insight Video

DataMagic is an AI system that automatically converts raw tabular data and natural language queries into narrative data-insight videos with dynamic charts, voice narration, and animations. The system introduces DVSpec, a declarative specification ensuring data fidelity, and uses a multi-agent architecture to generate and orchestrate video scenes while supporting interactive exploration modes.

DataMagic addresses a significant gap in the data visualization pipeline by automating the labor-intensive process of creating narrative data videos. Traditional approaches force users into a false choice: static BI dashboards lack storytelling capability, while video authoring tools demand pre-prepared visualizations rather than working directly with raw data. Pixel-level generative video models, meanwhile, cannot guarantee data accuracy or traceability—a critical limitation for enterprise and research applications where data provenance matters.

The system's core innovation lies in DVSpec, a declarative specification that binds visual elements to underlying data fields through semantic references rather than pixel values. This architectural choice decouples logic from rendering, enabling both data fidelity guarantees and flexible interaction modes. The Generate-then-Orchestrate multi-agent approach tackles computational complexity by producing candidate scenes in parallel before optimizing narrative coherence globally, a pragmatic solution to the combinatorial explosion inherent in design space exploration.

For data teams and enterprises, DataMagic streamlines workflows by eliminating manual video production steps while maintaining data integrity. The addition of interactive exploration and provenance-based Q&A transforms passive videos into dynamic interfaces, increasing analytical utility. The evaluation on 109 real-world samples provides empirical validation, though enterprise adoption will depend on integration capabilities with existing data infrastructure and ease of natural language query interpretation.

The broader implications extend to knowledge worker productivity, where narrative communication of data insights remains a bottleneck. As AI systems mature in automating analytical storytelling, expect downstream interest from business intelligence platforms and educational institutions seeking scalable data communication tools.

• →DataMagic automates end-to-end conversion of raw data into narrative videos with guaranteed data fidelity through DVSpec specification.
• →The system's multi-agent Generate-then-Orchestrate architecture enables parallel scene generation with global narrative optimization.
• →Interactive exploration and provenance-based Q&A functionality transform one-way videos into explorable data interfaces.
• →Evaluation on 109 real-world samples validates effectiveness, though enterprise adoption requires seamless integration with existing data stacks.
• →The technology addresses a significant productivity bottleneck in data communication within enterprise and research workflows.