Tasks and Visualizations Used for Data Profiling - A Survey and Interview Study

Aug 30, 2026

High-quality data underpins decision making and depends on robust, organization-dependent processes that ensure data is fit for purpose. This paper presents a survey of 53 data analysts across industries, followed by in-depth interviews with 24 participants, focusing on computational and visual methods for characterizing data and evaluating data quality. The work contributes comprehensive task and visualization lists and addresses what effective profiling looks like, revealing diverse practices, visualization exemplars, and recommendations for formalizing processes through rulebooks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages42

Reading Time1 h 46 min

AccessFree to download

Introduction

Related Work

[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk) Universities of Leeds, Sheffield and York Deposited via The University of Leeds. White Rose Research Online URL for this paper: [https://eprints.whiterose.ac.uk/id/eprint/197083/](https://eprints.whiterose.ac.uk/id/eprint/197083/) [Version: Accepted Version](Version: Accepted Version) Article: Ruddle, RA, Cheshire, J and Fernstad, SJ (2024) Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study. IEEE Transactions on Visualization and Computer Graphics, 30 (7) . pp. 3400-3412. ISSN: 1077-2626 [https://doi.org/10.1109/TVCG.2023.3234337](https://doi.org/10.1109/TVCG.2023.3234337) © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, forresale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request. Tasks and Visualizations used for Data Proﬁling: A Survey and Interview Study Roy A. Ruddle, James Cheshire, and Sara Johansson Fernstad 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 wereport 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. Index Terms—Data proﬁling, data quality, survey, interview. ~~ ~~ ✦ ~~ ~~ 1 INTRODUCTION GOOD-QUALITY data has become an essential part of decision making, and is entirely dependent on robust processes to ensure it is ﬁt for purpose. Data analysts therefore spend huge amounts of time performing the exploratory analysis required to characterize data (e.g., distributions) and assess its quality (e.g., missing values), which are known collectively as “data proﬁling” [1], [2], before it is used in detailed analysis and decision making. The motivating hypotheses for our research are twofold. First, most analysts perform proﬁling in an ad-hoc manner, following an undocumented process that makes data proﬁling more an art than a science that adopts a rigorous and reproducible method. Second, visualization techniques are underused in proﬁling, perhaps due to a lack of formal education/training in visualization and knowledge of how to apply visualization for complex/large-scale data.

FAQs

What is the focus of the survey in the paper?

The survey examines the computational and visual methods data analysts use to characterize data and investigate data quality.

How many participants took part in the survey and interviews?

The study surveys 53 data analysts, and 24 of them also participate in in-depth interviews.

What contributions does the paper make?

It provides more comprehensive lists of data profiling tasks and visualization techniques than previously published work, and it explains what “good profiling” looks like to experienced practitioners through observed diversity and recommendations.

Aug 30, 2026

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