How Wranglers Shape Wrangling: A Technical Dimensions Analysis

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 representative data cleaning tasks performed with four 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. Our results reveal no consistent advantage of any single tool, nor convergence of results within tools, suggesting that tool affordances steer user strategies but do not determine outcomes. 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.

Shiyi He, El Kindi Rezig, Paul Rosen, and Andrew M. McNutt. How Wranglers Shape Wrangling: A Technical Dimensions Analysis. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (to appear), 2026.

@inproceedings{he2026wranglers,
  title = {How Wranglers Shape Wrangling: A Technical Dimensions Analysis},
  author = {He, Shiyi and Rezig, El Kindi and Rosen, Paul and McNutt, Andrew M.},
  booktitle = {IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
    (to appear)},
  year = {2026},
  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 representative data cleaning tasks
    performed with four 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. Our results reveal no consistent advantage of any single tool, nor
    convergence of results within tools, suggesting that tool affordances steer user
    strategies but do not determine outcomes. 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.}
}