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Data storytelling has two main approaches for creating visual narratives: author-driven (prescribed path) and reader-driven (discovery journey). Let’s compare their differences in messaging, ordering, and level of interactivity through a simple comparison table and real-world examples.
| Aspect | Author-Driven (Prescribed Path) | Reader-Driven (Discovery Journey) |
| Messaging | Clear, focused, and directed | Flexible, exploratory, and open-ended |
| Ordering | Linear and structured | Non-linear and unstructured |
| Level of Interactivity | Limited interactivity | High interactivity |
Messaging
Author-driven storytelling involves a clear, focused, and directed message. The author carefully curates the narrative and guides the audience through the story, ensuring they understand the key points. They can distill complex information or data into a simplified and easily understandable form, breaking down intricate concepts and presenting the message clearly and concisely. An example of this approach is a line chart showing the growth of a company’s revenue over time, with annotations that explain significant events or trends.
In contrast, reader-driven storytelling allows for more flexible, exploratory, and open-ended messaging. Reader-driven storytelling messaging will enable readers to interact with the data and discover insights independently, interpreting and engaging with the narrative based on their unique perspectives and interests. This type of storytelling empowers readers to create meaning and connect with the message individually, accommodating a range of perspectives and needs. For instance, an interactive dashboard with multiple filters and visualizations lets users explore various aspects of a company’s performance, such as sales, profits, and customer demographics.
Ordering
Author-driven narratives are linear and structured, with a well-defined beginning, middle, and end. They follow a predetermined logical sequence that supports a hierarchy and the author’s perceived importance of information. The order is crafted to create a cohesive and engaging narrative flow where concepts or insights build upon each other and lead to a desired conclusion or message. Think of it like a presentation where the author walks the audience through each slide, explaining the story step-by-step.
On the other hand, reader-driven narratives are non-linear and unstructured. The audience can navigate through the data in any order they like, exploring different aspects of the story at their own pace, resulting in orders that vary from reader to reader. Reader-driven data narratives often present information in modular units or chunks to support unknown flow. The content is organized into smaller, self-contained modules that readers can access and engage with independently. This allows for more flexibility in ordering information based on the reader’s preferences. An example would be a website with various interactive visualizations where users can click and explore the data.
Level of Interactivity
Author-driven stories typically have limited interactivity, as the author controls the narrative and the audience’s experience. Interactivity is often limited to predetermined interactions, such as clicking on predefined elements or scrolling through predetermined visualizations. The level of interactivity is typically designed to support the author’s intended message and takeaways as the author selects and presents specific insights or findings from the data. For example, a static infographic that shows key findings from a research report is a form of author-driven storytelling.
Reader-driven stories, however, are highly interactive, allowing the audience to engage with the data and accommodate multiple perspectives and interpretations. Readers can dive deeper into specific aspects, change parameters, or select different visualizations to gain insights, discover patterns, and form connections that the author might not have preconceived. An example is a map visualization with selectable layers and tooltips, where users can explore spatial data and discover patterns that are most relevant to them.
Author-driven and reader-driven data storytelling approaches differ in messaging, ordering, and interactivity. The former is more structured and directed, while the latter offers more flexibility and exploration for the audience. As a data storyteller, it’s essential to understand these differences and choose the best approach for your audience’s needs and expectations.