Data storytelling helps simple data tasks but not complex thinking
"Piecing Data Connections Together Like a Puzzle": Effects of Increasing Task Complexity on the Effectiveness of Data Storytelling Enhanced Visualisations
Human-Computer Interaction
Summary
Sometimes adding explanations and stories to charts helps people understand data better. This study found that these stories work well when the task is simple, like spotting data points or big ideas. But when tasks need more thinking, like analyzing or creating new ideas, stories don’t necessarily help people get correct answers. Still, stories make it faster to finish harder tasks. The researchers tested this by giving people different charts with and without stories to solve questions of varying difficulty.
What this means in practice
- •For business intelligence teams: Design dashboards with storytelling elements to speed up simple insights identification but rely on other methods for complex data analysis tasks.
- •For public health analysts: Use data storytelling features in maps and charts to improve task efficiency when extracting basic data but avoid depending on them for detailed evaluations or creative applications.
Authors
Mikaela Elizabeth Milesi, Paola Mejia-Domenzain, Laura Brandl, Vanessa Echeverría, Yueqiao Jin, Dragan Gašević, Yi-Shan Tsai, Tanja Käser, Roberto Martínez-Maldonado
Abstract
The emerging concept of data storytelling (DS) suggests that enhancing visualisations with annotations and narratives can make complex data more insightful than conventional visualisations. Previous works found that DS-enhanced visualisations are more effective than conventional visualisations for simple tasks like identifying key data points or the main message. However, no previous work has explored the extent to which DS enhancements influence task completion across different levels of cognitive complexity. We address this gap by presenting the results of a study where 128 participants completed tasks based on four visualisations (two line charts and two choropleth maps, either with or without DS elements) spanning a range of complexity based on Bloom's taxonomy, which has been applied in data visualisation to categorise tasks hierarchically from lower to higher-order thinking. Results suggest that while DS-enhanced visualisations effectively support lower-order tasks (finding data points and understanding insights), they don't necessarily aid the correct completion of higher-order tasks (application, analysis, evaluation and creation). However, DS enhancements improve how efficiently participants complete complex tasks.