IEEE VIS 2024 Content: Data Guards: Challenges and Solutions for Fostering Trust in Data

Data Guards: Challenges and Solutions for Fostering Trust in Data



 Nicole Sultanum - Tableau Research, Seattle, United States

 Dennis Bromley - Tableau Research, Seattle, United States

 Michael Correll - Northeastern University, Portland, United States

 Screen-reader Accessible PDF

 Download Supplemental Material

 Room: Bayshore VI

2024-10-17T16:09:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T16:09:00Z

Exemplar figure, described by caption below — Data-driven decision making is ostensibly more common now than ever, but without specific points of trust in the data handling process, people often fall back on ad hoc decision justification mechanisms. Driven by user interviews of both data producers and data consumers, Data Guards is a set of seven proposed strategies for improving users' trust in data to help them make more confident data-driven decisions.

Fast forward

Full Video

Keywords

Data visualization, data cleaning, data quality, trust

Abstract

From dirty data to intentional deception, there are many threats to the validity of data-driven decisions. Making use of data, especially new or unfamiliar data, therefore requires a degree of trust or verification. How is this trust established? In this paper, we present the results of a series of interviews with both producers and consumers of data artifacts (outputs of data ecosystems like spreadsheets, charts, and dashboards) aimed at understanding strategies and obstacles to building trust in data. We find a recurring need, but lack of existing standards, for data validation and verification, especially among data consumers. We therefore propose a set of data guards: methods and tools for fostering trust in data artifacts.