"I Need to Find That One Chart": How Data Workers Navigate, Make Sense of, and Communicate Analytical Conversations

2026-02-28

AI Summary

This work addresses the limitations of linear conversation logs generated by existing conversational data analysis systems, which hinder data workers’ ability to retrospect and communicate about nonlinear, iterative analytical processes. To overcome this, the paper proposes a structured dialogue presentation method that introduces probes enabling multi-level navigation, on-demand detail expansion, and context-enhanced summarization—going beyond conventional scrolling and keyword search. By integrating visual recall with sequential and abstraction-based navigation strategies, the approach effectively supports users in recalling, reorienting within, and prioritizing past analytical exchanges. A user study with ten participants demonstrates that the method significantly enhances traceability of analytical reasoning and improves collaborative efficiency, validating its effectiveness in real-world data analysis workflows.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Conversational interfaces are increasingly used for data analysis, enabling data workers to express complex analytical intents in natural language. Yet, these interactions unfold as long, linear transcripts that are misaligned with the iterative, nonlinear nature of real-world analyses. Revisiting and summarizing conversations for different contexts is therefore challenging. This paper investigates how data workers navigate, make sense of, and communicate prior analytical conversations. To study behaviors beyond those supported by standard interfaces (i.e., scrolling and keyword search), we develop a design probe that supplements analytical conversations with structured elements and affordances (e.g., filtering, multi-level navigation and detail-on-demand). In a user study (n = 10), participants used the probe to navigate and communicate past analyses, fulfilling information needs (recall, reorient, prioritize) through navigation strategies (visual recall, sequential and abstractive) and summarization practices (adding process details and context). Based on these findings, we discuss design implications to support re-visitation and communication of analytical conversations.

Problem

• conversational interfaces
• data analysis
• analytical conversations
• navigation
• summarization

Innovation

• conversational analytics
• design probe
• analytical conversation navigation
• structured interaction
• data communication