Data Storytelling in Data Visualisation: Does it Enhance the Efficiency and Effectiveness of Information Retrieval and Insights Comprehension?

TL;DR

This study evaluates whether data storytelling elements embedded in data visualisations improve the efficiency and effectiveness of information retrieval and insights comprehension, compared to conventional visuals. Using a within-subjects design with 103 participants and six paired visualisations derived from Our World in Data climate data, the authors transform conventional charts into data stories through a defined DS process emphasizing chart choice, decluttering, emphasis, annotations, and explanatory titles. Results show that data storytelling significantly improves correctness across information retrieval and comprehension tasks, especially for single-insight questions, while overall efficiency gains are limited and not universal. Visualisation literacy relates to accuracy but does not consistently amplify DS benefits, suggesting DS can aid diverse users, though designers should balance storytelling elements to avoid cognitive overload.

Abstract

Data storytelling (DS) is rapidly gaining attention as an approach that integrates data, visuals, and narratives to create data stories that can help a particular audience to comprehend the key messages underscored by the data with enhanced efficiency and effectiveness. It has been posited that DS can be especially advantageous for audiences with limited visualisation literacy, by presenting the data clearly and concisely. However, empirical studies confirming whether data stories indeed provide these benefits over conventional data visualisations are scarce. To bridge this gap, we conducted a study with 103 participants to determine whether DS indeed improve both efficiency and effectiveness in tasks related to information retrieval and insights comprehension. Our findings suggest that data stories do improve the efficiency of comprehension tasks, as well as the effectiveness of comprehension tasks that involve a single insight compared with conventional visualisations. Interestingly, these benefits were not associated with participants' visualisation literacy.

Figures (9)

• Figure 1: Illustrative example: Conventional data visualisation (left) converted into a data story (right). The latter features several data storytelling elements: A) annotated data points; B) colour emphasis in text and lines; C) textual annotations; and D) an explanatory title that summarises the key insights.
• Figure 2: The structure of the study. Section 1 included a set of demographic and background questions. Section 2 included a visualisation literacy test adapted to the visualisation techniques utilised in our study Lee2017. Section 3 was the main comparative study in which a set of multiple-choice questions were posed to participants based on 3 conventional visualisations and 3 data stories (counterbalanced order); questions were of two types -- information retrieval and comprehension. Section 4 included questions related to participants' perceptions of the data storytelling visual elements they used while answering the questions in Section 3.
• Figure 3: Examples of two pairs of visualisations shown to participants: conventional visualisations (left) and their data storytelling versions (right)
• Figure 4: Average success time (in seconds) of data storytelling versus conventional visualisations. The difference was not significant.
• Figure 5: Correct rate of data storytelling versus conventional visualisations. The difference was significant (p < 0.0001).