How Wrangling Tools Shape Wrangling: A Technical Dimensions Analysis

2026-07-28

AI Summary

This study investigates how interface paradigms of data cleaning tools shape users’ actual cleaning strategies. Through a between-subjects observational experiment with 40 participants, it compares usage behaviors across Jupyter, Excel, ChatGPT, and OpenRefine on representative data cleaning tasks, applying for the first time the technical dimensions framework from programming systems to this domain. Findings reveal that interface design significantly steers user strategies without determining outcomes: data-centric interfaces (e.g., Excel) encourage opportunistic, ad-hoc operations, whereas abstraction-centric tools (e.g., Jupyter) facilitate systematic transformations at the cost of higher cognitive load. The results uncover systematic trade-offs among tools, indicating no single optimal choice and underscoring the critical role of interface design in shaping data work practices.

Abstract

Wrangling consumes a disproportionate share of the effort associated with any data project. While a variety of tools support it, relatively little is known about how their differing interface forms shape the way people actually wrangle. We conduct a between-subjects (N=40) observational study of data cleaning tasks performed in tools spanning distinct interface paradigms: Jupyter (notebook), Excel (spreadsheet), ChatGPT (conversational AI), and OpenRefine (visual wranglers). We situate our observations within the Technical Dimensions of Programming Systems framework, which we use as a conceptual scaffold for comparing across interface paradigms. Within the context of our study, the results suggest that tool affordances steer user strategies but do not determine outcomes. There is no consistent advantage of any single tool, nor convergence of results within tools observed across our outcome measures. Instead, we identify trade-offs and connect them with observed practice. For example, a key tension is between data- and abstraction-centered interfaces, where data-centered interfaces encourage opportunistic cleaning rather than systematic, planned transformations found in abstraction-focused tools (but come with a cognitive burden). Tool design, beyond mere functionality, plays a structuring role in how data work unfolds.

Problem

• data wrangling
• interface paradigms
• tool affordances
• user strategies
• data cleaning

Innovation

• data wrangling
• interface paradigms
• tool affordances
• technical dimensions
• observational study