Ruddle, Roy A;
Cheshire, James;
Fernstad, Sara Johansson;
(2023)
Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study.
IEEE Transactions on Visualization and Computer Graphics
10.1109/tvcg.2023.3234337.
(In press).
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Abstract
The use of good-quality data to inform decision making is entirely dependent on robust processes to ensure it is fit for purpose. Such processes vary between organisations, and between those tasked with designing and following them. In this paper we report on a survey of 53 data analysts from many industry sectors, 24 of whom also participated in in-depth interviews, about computational and visual methods for characterizing data and investigating data quality. The paper makes contributions in two key areas. The first is to data science fundamentals, because our lists of data profiling tasks and visualization techniques are more comprehensive than those published elsewhere. The second concerns the application question “what does good profiling look like to those who routinely perform it?,” which we answer by highlighting the diversity of profiling tasks, unusual practice and exemplars of visualization, and recommendations about formalizing processes and creating rulebooks.
Type: | Article |
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Title: | Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/tvcg.2023.3234337 |
Publisher version: | https://doi.org/10.1109/TVCG.2023.3234337 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Data visualization, Task analysis, Data integrity, Interviews, Visualization, Bars, Industries |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10163713 |
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