Taking the reader on a journey through this emergent area of research and practice, the Data Storytelling Workbook explores how visualisation and storytelling can be best utilised to communicate the significance of data to policy-makers and the public. Utilising this workbook, readers will be able to develop professional skills in data collection, analysis, visualisation and the promotion of data stories.
This workbook responds to this emergent market for data storytelling, exploring the rise of ‘data storytelling,’ or the different ways in which we tell stories using data. Drawing from practitioners’ first-hand experience, as well as the latest social science research, this
workbook introduces readers to the emerging, interdisciplinary field of data storytelling and the challenges and opportunities that come with it through practical applications and activities. It is informed, in particular, by our experiences over the past three years founding the Bournemouth University-based, Civic media Hub and BU Datalabs with our cross-disciplinary team of journalists, media scholars, computer scientists and geographers.
By approaching both ‘data’ and ‘storytelling’ in a broad sense we are able to combine theory and practice around real-world data storytelling scenarios. This workbook will offer critical reflection alongside practical and creative solutions to challenges in the data storytelling process, from tracking down hard to find information, to the ethics of visualising difficult subjects like death and human rights. Because visual representations construct and communicate meaning, questions of how best to symbolise places, quantities, experiences, or anything else that is part of the data story will always be fraught with debate.
Linked to these challenges, there are also important concerns arising around data distortion in data-driven storytelling and data visualisation. In every stage of the data storytelling process, from gathering information to circulating a visualisation on social media, distortion can come into play. This might arise from missing data, mis-recorded data, or information displayed out of context. It can happen when designs exaggerate the sizes of bubbles or bars. Or distortion might occur in the analysis, where biases become codified in the data story. For these reason it is increasingly important that people working with data—whether computer scientists or journalists, humanitarian organisations or academic researchers—think reflexively and critically about data storytelling practices. Transparency and reflection, not only about the sources data comes from, but also around the entire process of gathering, analysing, editorialising, visualising, and circulating data stories, needs to be at the heart of our practice.