Feigenbaum, Anna and Alamalhodaei, Aria. The data storytelling workbook. London: Routledge, 2020. x, 246 p. ISBN: 978-1-138-05210-9 (hbk). £120.00; ISBN: 978-1-138-05211-6 (pbk). £32.99; ISBN: 978-1-315-16801-2 (ebk). £29.69.
Over the recent decades, the world has witnessed the emergence of many new information and communication technologies and an unprecedented explosion of data. This, the availability of big data, has brought about opportunities for new insights and knowledge relevant for various sectors and different fields of study. However, while infographics, network analysis, mapping and visualizations have gained popularity, the process of identifying, collecting, analysing, making sense of the data, and communicating the results remains complex and difficult.
Today many decisions are made based on big data, and are influenced by the ways in which data is presented and communicated. It is therefore imperative to develop and employ novel techniques that (a) help make sense of the data in meaningful ways and (b) enable effective communications of the findings to meet situated needs and interests. Data Storytelling offers such a method. At the core of data storytelling is the goal to tailor information in an appropriate manner, for a specific audience, in a way that puts a human perspective on the narrative being told. There is a growing need for skilled storytellers and for the related skills to be included in educational programs. That is why a number of colleagues and I are in the process of developing a series of online courses on this topic that will cover the process from data acquisition, all the way to analysis and visualisation. It was as part of that effort that we have looked for and read a selection of different books on the topic, among which was this book.
As mentioned by the authors, the book draws on cross-disciplinary research to introduce key concepts, challenges and problem-solving strategies, in the field of data storytelling. Here data stories are defined as 'intentional communicative artefacts that present data in an interesting, evocative, and informative way' (p. 3). It is, however, emphasised that data stories are not about 'dramatising or embellishing for the sake of a more engaging story' but rather the aim is to 'focus on the human elements of what is in a dataset in order to be able to more clearly pinpoint what is at stake, and to communicate it effectively and empathetically' (p. 4). For this, the book brings together theory and key concepts; practical exercises; insightful and relevant examples; short presentation of inspirational key people and projects; and introduces the reader to additional resources. The book covers a broad range of contents, from elementary topics such as what is meant by nominal, ordinal or interval data to more complex issues such as discussing data bias (arguing that data is not objective or rational, as its collection and interpretation rests in human hands) or data discrimination (where it is presented that predictive algorithms and data models reproduce existing inequalities). The book starts with the initial steps in the process such as getting to know one’s audiences, finding the narrative and data backstory, as well as discussing narrative tools and structures that help engage people. It then discusses topics such as data divide, open data, big data, datafication, and more. When it comes to visual data storytelling, it discusses feminist data visualisation, the challenges for data visualisation, structuring the data, and it also delves into pillars of data storytelling, semiotics, role of colour, and various chart types. It then moves to multisensory data storytelling and story mapping and more.
Generally, the book is subdivided into six chapters: Introduction; A narrative approach to data storytelling; Navigating data’s unequal terrain; Visual data storytelling; Data storytelling with maps; and Future-proof principles. Finally, the book concludes with an index.
I liked the book very much; it flows well, it is well-structured and easy to read, and it does what it preaches, i.e., not only it is a good introduction to the topic, the book itself is a good example of effective storytelling. The presentation of the topics as well as practical exercises in combination with inspiring showcased examples is a great combination to render this book suitable as course material or as a workbook for anyone who works with data and wishes to better communicate the findings.
I particularly enjoyed reading about critical reflections by scholars and key storytellers, and the way in which they communicate with data in novel ways. I was, for example, familiar with the great physician and public speaker Hans Rosling and his amazing ways of telling compelling stories with data. It was not, therefore, a surprise for me to see him mentioned in one of the many spotlights. I had already seen some of his presentations and was impressed by the way he used new technologies and the available data for innovative presentations. The recent technological developments indeed provide us with powerful tools, to which earlier generation did not have access. But reading this book was a good reminder that it is not always the tools that make a difference but rather innovative thinking. Many have access to the tools but not everyone is as good at data storytelling as Hans Rosling was. Another great example that I had not known about before was a very interesting visualisation about the causes of mortality in the army presented already in mid 1800s by Florence Nightingale. With that, without access to sophisticated tools of today, she had managed to create a powerful visualisation that succeeded in influencing public policy and reducing the number of deaths. There are plenty of insights in the book that encourage the reader not to solely rely on the black-boxed automated tools and instead engage with the data and its backstory. For example, the authors state:
'As we become more and more reliant on computers to do the work of structuring and visualising data for us, we become less and less mindful of the importance of digging into the data in front of us. A pretty automated graph or pre-analysed sales figure may look good in a report, but when we cut and paste from dashboards without understanding how the graphs and numbers we are using got there, we are limiting our capacity to critically question data and the ways it shapes decision-making' (p. 123).
This book certainly discusses ways to better engage with and critically examine the data.
The book is full of references and links to other interesting and valuable resources too. While a few of those links already seemed discontinued, the authors rightly claim that the principles covered in the book go beyond the potential changes in the mentioned software, websites and tools as the book aims to 'cultivate the mind-sets needed' for the reader to become a better storyteller (p. 4). In my view, the authors achieve what they set out to do and I have already happily included this book as part of the reading material for my course. I will also eagerly look forward to further work stemming from the Civic Media Hub at the Bournemouth University and future publications or data stories told by the authors.
Dr. Nasrine Olson
Swedish School of Library and Information Science
University of Borås
How to cite this review
Olson, N. (2021). Review of: Feigenbaum, Anna and Alamalhodaei, Aria. The data storytelling workbook. Raoutledge, 2020. Information Research, 25(3), review no. R710 [Retrieved from http://www.informationr.net/ir/reviews/revs710.html]
Information Research is published four times a year by the University of Borås, Allégatan 1, 501 90 Borås, Sweden.