WHATSNEXT: Guidance-enriched Exploratory Data Analysis with Interactive, Low-Code Notebooks

TL;DR

WHATSNEXT is an interactive, low-code notebook framework that guides users through data analysis by providing recommendations and visualizing the analysis workflow to improve organization and accessibility.

Contribution

It introduces a guidance-enriched notebook design with recommendation panels and dependency visualization to enhance exploratory data analysis for users with limited coding skills.

Findings

Improved user engagement in data exploration tasks.

Enhanced understanding of analysis workflows through visualizations.

Facilitated easier navigation and tracing of analysis steps.

Abstract

Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and tables, which can be unorganized and obscure users' data analysis workflow. Furthermore, users with limited coding expertise may struggle to quickly engage in the analysis process. In this work, we design and implement an interactive notebook framework, WHATSNEXT, with the goal of supporting low-code visual data exploration with insight-based user guidance. In particular, we (1) re-design a standard notebook cell to include a recommendation panel that suggests possible next-step exploration questions or analysis actions to take, and (2) create an interactive, dynamic tree visualization that reflects the analytic dependencies between notebook cells to make it easy for users to see the structure of the data exploration threads and trace back to previous steps.