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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
