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Part II. Storytelling in the Information Age | 71
simulate free spatial movement and display networked connections (e.g.,
Prezi), while the other group includes dashboards and infographics that
condense complex data contexts into a single static interface (e.g., Tableau,
Canva). These visualizations allow the user to navigate through the data
displays along any path according to his or her own logic. At the same time,
companies have also adapted to the interactive user behavior of target groups,
and since the second half of the 2010s, static presentations of data analyses
have been increasingly replaced by presentations that allow for dynamic
viewing.
A new trend in effective data communication in business and journalism is
Data Storytelling or Data-Driven Storytelling (DDS). In the past, story-based
communication in business was mainly linked to marketing activities as part
of brand building. In the first decades of the 21“ century, however, a trend has
emerged, driven by the opportunities of technological development, to use
visualization and narrative frameworks to interpret data and communicate
data analysis.
Bruner (1986) distinguished between argumentative-logical and narrative
forms of cognition, but the DDS approach combines the paradigmatic
argumentation and narrative modes of thought that also present motivations
and motives. At the same time, the narrative structuring of data challenges
Manovich's (2001) theory of the separation of datasets and narrative. In DDS,
the use of a narrative framework allows for the presentation of problem
justification as well as the relationships between data through varied data
visualizations. Together, visualization and narrative create context and provide
clear information, such as in the case of a multispectral analysis of a large
corpus of data which can be presented in one interface.
Sejal (2019) emphasizes the effectiveness of narrative data presentation
by highlighting its ability to take data sets that are considered dry and
boring and present them in a way through which the audience learns about
the antecedents and consequences of the findings through storytelling. In
explaining complex interrelationships, the narrative framework makes it easier
to evoke the logical connections drawn by the data. At the same time, data
analysis aims to persuade the recipient and to encourage action, which is more
effective when embedded in a story as it facilitates emotional identification.
The linear narrative structure illuminates the research question, highlights
patterns in the data and helps to develop the final conclusions.
The secret to the effective communication of data and its analysis is a clear
visual interface and a narrative structure. Tables, graphs, diagrams, functions,
clusters, maps, and timelines are used to illustrate the relationships between
data series, variables, and comparisons between data sets. These graphical
tools are also used in DDS in both static and dynamic versions that allow
for interactivity.
The products of DDS are specific, compressed narratives of data
visualization. Stolper, Lee, Riche, and Stasko (2018) analyzed visual forms