Researching how to leverage visual storytelling for medical imaging data analysis, I was looking for a road map to obtain a more structured and solid theoretical background of the field and to expand my knowledge about it. Then, I came across Data-Driven Storytelling and it was love at first sight.
This bible of data-driven storytelling covers everything you need to know about the topic, from storytelling techniques (including scrollytelling) and narrative design patterns to evaluation and ethics in storytelling, providing readers with explanations and examples of the concepts described as well as extensive reference material. All of this is expressed in an accurate, concise, and clear way.
The reader is gently introduced to this journey of discovering their storytelling potential and understanding of its techniques. You learn through illustration what a data-driven story is, how to build a narrative, andwhy we need data-driven storytelling. As soon as you dive into this book, you will be unavoidably intrigued and hooked into it.
The technical aspects of the subject are progressively presented. The concepts of story event, story structure, story segmentation, the levels of communication structure, the kinds of discourse, and the role of animation are explained together with some design principles. The communication via storytelling never appeared to be so sophisticated as well as fascinating.
In the second part of the book, the authors illustrate how the domain situation affects a data story, “a set of data-driven events from a narrative adjusted based on data, task, user.” In particular, how the domain situation determines the trade-off between the exploration and explanation, and, consequently, the level of flexibility and interpretation of a story.
Therefore, the reader is informed about a more sensitive approach to visual storytelling. A detailed exposition of the main narrative design patterns and storytelling techniques categorised per scope facilitates the definition and navigation of the visual data story design space. In addition, some research opportunities are also nicely discussed, which reveal the still adolescent state of the field and in which directions it can still be broadened.
One of the most interesting parts of the book is the description of the visual storytelling design process, supported by interviews with actual storytellers. Together with providing an extensive list of the different tools used in storytelling projects, the authors enable the reader to immediately practice and create their own data-driven stories. This is a real call to action!
Another strength of this book is the emphasis on the importance of the audience in data communication via storytelling. To obtain effective and engaging data-driven stories, we need a Copernican revolution to place our target audience in the center of our attention (and of our stories). The authors hit the bull’s-eye, including a rich argument for how to address it.
Yet, “with great power comes great responsibility.” A visual data story can describe, show, reveal, inform, and explain, but it can also persuade, convince, delude, deceive, and hide. As Edward Tufte pointed out about graphic integrity in data visualisation, some ethical considerations must also be made in visual storytelling, and a chapter is reserved to also discuss these issues.
Moreover, special attention has been drawn on the different devices to create and deliver stories. Nowadays, a large variety of smartwatches, smartphones, tablets, laptops, desktop PCs, AR, and VR headsets, tabletops, wall displays, and tangible media are accessible to storytellers and learning more about their characteristics can be useful to opt for the right device in the right situation.
Finally, I really enjoyed the last chapter of the book about evaluation in storytelling. Evaluating the adequateness of a visual data story is often nontrivial. The authors present a list of evaluation criteria and methods based on goals and perspective of each stakeholder involved in the storytelling process, which can be checked to review and refine a story before its delivery.
Personally, this scientific book helped me to build up my theoretical background about visual storytelling and inspired my ongoing research work. The 360 degree analysis based on collaboration between the best researchers in the field and an extensive study of storytelling projects, contributed in filling my knowledge gaps up. It is definitely a great overview of the state of the art.
In addition, (maybe unfortunately) I had a lot of work to do once I finished to read the book too! Even if it was satisfying to find many already-familiar scientific articles mentioned in this book to which I relate, the rest of the presented material was unknown and required further reading. No storytelling aspects are neglected and you can find much inspiration for future research.
Therefore, the authors stimulated my curiosity in investigating some of the suggested research opportunities and to put things in a different perspective. They refined my approach to research in visual storytelling, and they showed that visual data stories follow procedural and methodological rules even if they are not (yet) represented in mathematical formulas.
Thus, I strongly recommend this book to both experts and newbies in data visualisation. It can be read from the beginning to the end if one would like to undertake and enjoy the journey, but it can also be consulted as documentation thanks to its clear and logical layout, and lists of definitions and references. It is just unfortunate that ctrl + F cannot be used on paper.
PhD student in Computer Science at the University of Groningen. #Visualisation #VisualStorytelling #HCI