DATA-DRIVEN STORYTELLING FOR CASUAL USERS

Today’s overwhelming volume of data has made effective analysis virtually inaccessible for the general public. The emerging practice of data-driven storytelling is addressing this by framing data using familiar mechanisms such as slideshows, videos, and comics to make even highly complex phenomena understandable. However, current data stories still do not utilize the full potential of the storytelling domain. One reason for this is that current data-driven storytelling practice does not leverage the full repertoire of media that can be used for storytelling, such as speech, e-learning, and video games. In this dissertation, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling by putting more tools in the hands of designers. We expand the idea of data-driven storytelling into the group of casual users, who are the consumers of information and non-professionals with limited time, skills, and motivation , to bridge the data gap between the advanced data analytics tools and everyday internet users. To prove the effectiveness and the wide acceptance of our taxonomy and data-driven storytelling among the casual users, we have collected examples for data-driven storytelling by finding, reviewing, and classifying ninety-one examples. Using our taxonomy as a generative tool, we also explored two novel storytelling mechanisms, including live-streaming analytics videos—DataTV—and sequential art (comics) that dynamically incorporates visual representations—Data Comics. Meanwhile, we widened the genres we explored to fill the gaps in the literature. We also evaluated Data Comics and DataTV with user studies and expert reviews. The results show that Data Comics facilitates data-driven storytelling in terms of inviting reading, aiding memory, and viewing as a story. The results also show that an integrated system as DataTV encourages authors to create and present data stories

Visual Analytics to Support Evidence-Based Decision Making

The aim of this thesis is the design of visual analytics solutions to support evidence-based decision making. Due to the ever-growing complexity of the world, strategical decision making has become an increasingly challenging task. At the business level, decisions are not solely driven by economic factors anymore. Environmental and social aspects are also taken into account in modern business decisions. At the political level, sustainable decision making is additionally influenced by the public opinion, since politicians target the conservation of their power. Decision makers face the challenge of taking all these factors into consideration and, at the same time, of increasing their efficiency to immediately react on abrupt changes in their environment. Due to the digitization era, large amounts of data are digitally stored. The knowledge hidden in these datasets can be used to address the mentioned challenges in decision making. However, handling large datasets, extracting knowledge from them, and incorporating this knowledge into the decision making process poses significant challenges. Additional complexity is added by the varying expertises of stakeholders involved in the decision making process. Strategical decisions today are not solely made by individuals. In contrast, a consortium of advisers, domain experts, analysts, etc. support decision makers in their final choice. The amount of involved stakeholders bears the risk of hampering communication efficiency and effectiveness due to knowledge gaps coming from different expertise levels. Information systems research has reacted to these challenges by promoting research in computational decision support systems. However, recent research shows that most of the challenges remain unsolved. During the last decades, visual analytics has evolved as a research field for extracting knowledge from large datasets. Therefore, combining human perception capabilities and computers’ processing power offers great analysis potential, also for decision making. However, despite obvious overlaps between decision making and visual analytics, theoretical foundations for applying visual analytics to decision making have been missing. In this thesis, we promote the augmentation of decision support systems with visual analytics. Our concept comprises a methodology for the design of visual analytics systems that target decision making support. Therefore, we first introduce a general decision making domain characterization, comprising the analysis of potential users, relevant data categories, and decision making tasks to be supported with visual analytics technologies. Second, we introduce a specialized design process for the development of visual analytics decision support systems. Third, we present two models on how visual analytics facilitates the bridging of knowledge gaps between stakeholders involved in the decision making process: one for decision making at the business level and one for political decision making. To prove the applicability of our concepts, we apply our design methodology in several design studies targeting concrete decision making support scenarios. The presented design studies cover the full range of data, user, and task categories characterized as relevant for decision making. Within these design studies, we first tailor our general decision making domain characterization to the specific domain problem at hand. We show that our concept supports a consistent characterization of user types, data categories and decision making tasks for specific scenarios. Second, each design study follows the design process presented in our concept. And third, the design studies demonstrate how to bridge knowledge gaps between stakeholders. The resulting visual analytics systems allow the incorporation of knowledge extracted from data into the decision making process and support the collaboration of stakeholders with varying levels of expertises