Designing Data Stories to increase Data Understanding and Trust
- Type:Bachelor Thesis
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Stories are one of the most important human communication structures and storytelling has always been a means of passing wisdom and information to other generations. They collectively help us make sense of our past and reason about the future. It is thus not surprising that Analysts who analyze data to gain business insights are dependent on data storytelling techniques to communicate their findings to decision makers. Nowadays, organizations rely on BI&A applications such as Tableau or PowerBI to transform and visualize their data. But despite the growing use of data visualization tools, it is still difficult for business users to interpret the content of those dashboards. Data Analysis must go beyond presenting the data results and instead, it must tell a story that can create a connection between the audience and the topic. Data itself does not generate value, it must be properly communicated with the audience. But yet it is still unclear how data stories need to be designed to increase data understanding and trust. In addition to that, there is little empirical evidence on the effect of data storytelling related to the user.
Goal of the Thesis
The overall goal of this thesis is to establish a design science research project to design, build and evaluate multiple data stories based on current dashboards. For designing the data stories, various business reports of the Robert Bosch GmbH can be provided.
Conduct a systematic literature review on data storytelling success factors to identify best practices in data story design
Conduct interviews with business users to identify further features to increase data understanding
Map identified success factors to selected existing dashboards and create a data story for them
Evaluate the data stories using an experiment or focus group
High intrinsic motivation and proper time management
Analytical skills in PowerBI or Tableau
Fluent in English (as the thesis has to be written in English)
Reifer et al. (2020) - Data Storytelling als kritischer Erfolgsfaktor von Data Science
Behere & Swain (2019) - Big Data Real-Time Storytelling with Self-Service Visualization
Watson (2017) - Data Visualization, Data Interpreters, and Storytelling
Ojo & Heravi (2018) - Patterns in Award Winning Data Storytelling
Shi et al. (2021) - Calliope: Automatic Visual Data Story Generation from a Spreadsheet
Boldosova & Luoto (2019) - Storytelling, business analytics and big data interpretation
If you have questions before, feel free to contact me anytime. If you are interested, leave me an email with a short motivation statement, your CV and your current transcript of records.