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

TL;DR

This paper explores how data workers navigate, interpret, and communicate complex analytical conversations in natural language interfaces, proposing structured tools to improve re-visitation and understanding.

Contribution

It introduces a design probe with structured elements to enhance navigation and summarization of analytical conversations, addressing limitations of linear transcript interfaces.

Findings

Participants used visual recall and sequential strategies for navigation.

Summarization included process details and contextual information.

Structured tools improved re-visitation of analytical conversations.

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.