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When data storytelling meets research, featuring Data-Driven Storytelling (multiple authors)



written by human, not by AI




Last Data Viz Bookshelf talked about Storytelling with Data by Cole Naussbaumer Knaflic - a book targeted primarily to data professionals looking for a practical guide to help them present data in more compelling ways. Cole’s books tend to go more towards actionable advice: a few pages into the book, and you’ll straightaway have some good ideas to implement positive changes in your own work.


But what if you want to dive deeper into why certain choices matter? That’s when research comes in. But data visualisation research is difficult to find and often hard to access for a range of reasons: paywalls, available only in some obscure publication, or even just because of the structure and language. Academic lingo doesn’t exactly make for accessible literature.


The good news is that today’s book recommendation comes to fill this exact need - it is a compilation of research articles published by authors in the fields of data visualisation, information design, data journalism and data storytelling. Edited by Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopolous and Sheelagh Carpendale, the 2015 book titled Data-Driven Storytelling brings the evidence you need to dip your toes into more advanced data storytelling.



Storytelling as a communication tool


Each chapter of the book dives into an overarching theme within the data storytelling field. The book starts off defining storytelling techniques that can be used to communicate information in multiple forms: narratives, stories, exploratory displays of information and even the visual language of comics. I particularly like the section about visual devices used in graphic novels - as someone who has loved them since I was a kid, I can see how this particular type of storytelling may have shaped my visual communication vocabulary.


The book then goes into differentiating exploratory and explanatory displays of data and how storytelling techniques can be intertwined with both approaches in different ways, resulting in different experiences. From there, each chapter goes on building on the previous ones into more complex facets of the subject. There’s an entire section dedicated to classifying techniques and patterns that can be used to tell stories with data, filled with examples of each kind, as well as critiques of what works best and when. If you’re looking to advance your storytelling techniques, this is an awesome resource to go back to whenever you need some inspiration.


The book also goes into rich technical details on how the medium can impact and shape which data storytelling techniques can be used to different effects. Chapter 6 explores the influence of different devices in displaying information with storytelling techniques across different technologies - from smartwatches to VR sets. If it’s technical detail that you’re looking for, this is the book for you.



Theory meets practice


One of the most interesting pieces within the book is in Chapter 7, where the authors interview people who work with data storytelling from multiple backgrounds - journalists, designers, computer science professionals, and academics. They all share their methods, struggles, and how they go from idea or data to compelling data narratives or exploratory pieces. It is always nice to see what other people in the field are up to and how different yet similar we are in many ways. The idea of a somewhat organised chaos as a way of working comes through the chapter, which is entirely accurate to my experience as well - even though I’ve only dabbled in the storytelling aspects of data communication.


From this point on, Data-Driven Storytelling goes into deep exploration of methodologies and how a data storytelling process can be organised. It guides readers through the entire lifecycle of a data-driven story - from conception, data collection and preparation, data analysis, story development to visual presentation. It is incredibly insightful to learn how the workflow can differ between three types of organisations taken by the chapter’s authors as reference: design studios, media houses, and NGOs. It is a chapter full of practical knowledge of how each of these places implemented their workflows to enable effective data storytelling.



Evaluating a data story


The book's last chapter covers a system to evaluate the effectiveness of data-driven stories. It is one of those super interesting chapters in a book you’ll always go back to for reference. It covers and describes a series of points that can be used to check if your data story will meet its goals within the context it is designed for. It covers dozens of themes, such as comprehension, memorability, engagement, dissemination, increased knowledge, impact, credibility and trust. It also goes into the techniques and methods that can be used to evaluate such points, like how to collect performance statistics, run questionnaires and interviews to evaluate the story’s effectiveness, as well as usability tests and case studies. It is a very complete resource to help you analyse in detail how a data story has performed and which parts could be subject to improvement.



Should you read it?


It is a technical book and full of evidence-based research. If this is your cup of tea, you’ll enjoy it a lot. It is not something I’d recommend for a complete beginner, though, as it does imply you already have previous knowledge of some terms and are familiar with reading more academic language.


Having said that, it is a very interesting read, as it covers every aspect of data-driven storytelling, and it does so from multiple perspectives. It is also commendable that it doesn’t focus on any technology in particular, covering storytelling from a broader scope that can be used in any context with any tools you may have available. It covers theory and practice in detail and depth, and it is not a heavy read - despite the scientific appeal and language.


I recommend it to anyone interested in deepening their data storytelling knowledge with more detail and evidence into the techniques and methodologies that can make a good data story. It is a good resource to consult from time to time, rather than a book you’ll read from to cover in one sitting - but it is a very good resource at that.


Always check your local library first to see if any of the books I recommend are available. If they’re not, consider donating a copy!

 

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written by human, not by AI

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