Untidy data: the unreasonable effectiveness of tables

An example spreadsheet (shared with permission of Cornerstone Architects) showing various "rich" table features that our participants employed, including (1) A Master Table of base data that is often left untouched, with manipulations happening in a copy or other area separate from the base data; (2) Marginalia such as comments or derived rows or columns in the periphery of the base table, often taking the form of freeform natural language comments; (3) Annotations such as highlighting or characters with specific meaning (e.g., a dash denotes missing values) to flag particular cells as anomalous or requiring action; and (4) Multi-cell features such as labels or even data that span multiple rows or columns of the sheet.
An example spreadsheet (shared with permission of Cornerstone Architects) showing various "rich" table features that our participants employed, including (1) A Master Table of base data that is often left untouched, with manipulations happening in a copy or other area separate from the base data; (2) Marginalia such as comments or derived rows or columns in the periphery of the base table, often taking the form of freeform natural language comments; (3) Annotations such as highlighting or characters with specific meaning (e.g., a dash denotes missing values) to flag particular cells as anomalous or requiring action; and (4) Multi-cell features such as labels or even data that span multiple rows or columns of the sheet.
Abstract

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets — the quintessential table tool — remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and “get their hands on” the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.

Materials
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Authors
Lyn Bartram
Michael Correll
Citation
Thumbnail image for publication titled: Untidy data: the unreasonable effectiveness of tables
Untidy data: the unreasonable effectiveness of tables

Lyn Bartram, Michael Correll, and Melanie Tory. IEEE Transactions on Visualization and Computer Graphics—TVCG. 2021

PDF | Preprint | DOI | BibTeX


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