Data-Driven Storytelling
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Data-Driven Storytelling

Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale, Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale

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eBook - ePub

Data-Driven Storytelling

Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale, Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale

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About This Book

This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.

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Nathalie Henry Riche
Microsoft Research
Christophe Hurter
French Civil Aviation University (ENAC)
Nicholas Diakopoulos
Northwestern University
Sheelagh Carpendale
University of Calgary


Today, data visualizations are everywhere. They form a significant and often integral part of contemporary media. Stories supported by facts extracted from data analysis proliferate in many different ways in our analog and digital environments including printed infographics in magazines, animated images shared on social media, and interactive online visualizations tightly integrated with news stories on leading media outlets. However, while data visualization may be at the heart of data-driven stories, the concepts are not synonymous. We explore these distinctions in this book.
The appearance of several landmark books such as Bertin’s Semiology of Graphics: Diagrams, Networks, Maps (original French 1967, English translation 1983), Tukey’s Exploratory Data Analysis (1977), Tufte’s The Visual Display of Quantitative Information (1984), and Cleveland and McGill’s Dynamic Graphics for Statistics (1988) set the stage for the emergence of a recognized area of research in visualization by the late 1980s. This emergence was fueled by the increasingly prevalent possibility of using computers to make data visual and interactive and was inspired by the popular idiom that “a picture is worth a thousand words.” While the research community has favored phrases such as “scientific visualization,” “knowledge visualization,” and “information visualization,” in general, media tends to use the more encompassing phrase “data visualization.” Due to the importance of visualization to data-driven stories in journalism, we also use the phrase “data visualization.”
Data has been represented visually from the early history of humans. Perhaps the oldest examples are those documented by Marshack, where he shows examples of keeping records through scratches on surfaces such as Ishango bones (18,000–20,000 BC) and Lebombo bones (35,000 BC) (Marshack 1991). While these considerably predate written language, humans were clearly using visual representations to help themselves understand their world. If the ability to make scratches on bones is considered the first technology boon to data visualization, large advances in what is possible to achieve with data visualization can be associated with the availability of developing technologies. In broad steps, this includes the use of clay tablets in Mesopotamia, the development of paper in Egypt, Johannes Gutenberg’s printing press in 1440, Konrad Zuse’s computer in 1940, and most recently in the late 1980s when Tim Berners-Lee made the Internet widely accessible via the World Wide Web. In terms of our focus on data-driven stories, this last factor of making computational power and prowess widespread has been crucial. It is this factor that has made it possible for all types of media to consider incorporating evidence, portrayed by the visualization of the data that supports a given story, directly into the presentation of that story.
We think this movement towards data-driven stories, which is apparent in both the data visualization research community and the professional journalism community, has the potential to form a crucial part of keeping the public informed, a movement sometimes referred to as the democratization of data – the making of data understandable to the general public. This exciting new development in the use of data visualization in media has revealed an emerging professional community in the already complex group of disciplines involved in data visualization. Data visualization has roots in many research fields including perception, computer graphics, design, and human-computer interaction, though only recently has this expanded to include journalism.


Early research in data visualization focused on producing static images and quantifying the perception of different visual encodings to understand data visually (Card, Mackinlay, and Shneiderman 1999). The vast majority of research since then has focused on designing and implementing novel interfaces and interactive techniques to enable data exploration. Major advances in visual analytics and big-data initiatives have concentrated on integrating machine learning and analysis methods with visual representations to enable powerful exploratory analysis and data mining (Thomas and Cook 2005). As interactive visualizations play an increasing role in data analysis scenarios, they also started to appear as a powerful vector for communicating information. The popularity of JavaScript Web technology and the availability of the D3 toolkit (Bostock, Ogievetsky, and Heer 2011) also made it possible for a wider range of people to create data visualizations. Being able to easily share interactive data visualizations on the Web also increased the democratization of interactive visualizations. Since the field is mature enough, it is now time to understand how these powerful interactive and dynamic data visualizations can play a role in communicating information in novel ways.


Journalism has always been about communication, finding relevant stories and disseminating them publically, observing events, gathering information, and telling this information to the general public in a manner that can be understood and is both interesting and relevant (Kovach and Rosenstiel 2007). There has been an increasing onus on the quality of this information, from the right to protect an information source, to increasing interest in documented evidence, as in photographs, audio recordings, and video recordings, all of which can be thought of as types of data. There has also been a growing consciousness that some of today’s most relevant stories are buried in data. This data can be quite hard to understand in its raw formats but can become much more generally accessible when visualized. Journalists have not only begun to use standard data visualizations such as charts and maps in their stories, but are also creating new ones that are tailored to the particular data type and to the message of the story they are writing. Since journalists are now able to easily share interactive data visualizations on the Web, the democratization of data visualization is accelerating with new compelling data visualizations emerging in the media daily. This has led to extensive and practical progress on the challenges of data-driven storytelling. News sites like The New York Times,* FiveThirtyEight, Bloomberg, and The Washington Post§ were early movers in capturing the surge of attention and interest in consuming data-driven news by the public. By carefully structuring the information and integrating explanation to guide the consumer, journalists help lead readers towards a valid interpretation of the underlying data. In parallel to the last section on the visualization researcher perspective, we can also say that it is now time to learn how these powerful interactive and dynamic data visualizations play a role in communicating information in novel ways.


At the time of formulating the possibility of this book, there was little overlap and collaboration between the two major communities involved: professional journalists who are at the forefront of making data-driven stories and academic researchers who are exploring research questions in regard to the role visualization can play in storytelling with data. This gap between research and practice has been widening as novel and innovative examples and genres of storytelling with data flourish in the media quite separately from the knowledge being built by the research community. The goal of this book is to try to close this gap by bringing together the voices of leading researchers and practitioners on data-driven storytelling. The chapter topics and their content were defined by authors with representation and participation from both communities.
Because of the rapid and practical advances in data-driven storytelling and its increasingly widespread use, we gathered several of the top practitioners from journalism and design together with visualization researchers to discuss the challenges and opportunities of data-driven communication during a Dagstuhl Seminar. Schloss Dagstuhl,* a unique venue sponsored by the German government, is a place where computer science researchers can meet to discuss currently important research questions. Dagstuhl encourages interdisciplinary discussion that includes leading thinkers from academia and industry. Founded in 1990, it has earned an international reputation as an incubator for new ideas. The four editors of this book organized the 16061 seminar February 7th–12th, 2016. During these 5 days, a carefully selected group of 42 thinkers with diverse backgrounds: journalists, designers, perception, human-computer interaction, and visualization researchers, reflected, exchanged, and synthesized knowledge on data-driven storytelling, which led to this book.
In brief, the aims of the seminar were as follows:
  1. To bring together academic and industrial researchers from the human-computer interaction, cognitive psychology, information visualization, and visual analytics research communities, as well as storytelling experts from data journalism, design, art, and education.
  2. To prepare a data-driven storytelling research agenda that includes a definition of data-driven storytelling, to compile examples, and to provide a detailed description of research directions in this space, and to offer a motivating list of research opportunities and challenges.
  3. To investigate how the evaluation of data-driven stories can be done, including via expert critique, as well as through studies of audience comprehension, engagement, biases, and visualization literacy.
  4. To discuss the ethics of data-driven storytelling authoring, identifying possible sources of bias and investigating how the lie factor of static visualizations applies to different media.
  5. To compile examples of good and bad practices in application domains (data journalism, design, art, and education) and report on current processes and practices to create data-driven stories.
  6. To formalize and explore the design space for novel consumption experiences in each domain. In particular, to reflect on the various advantages of different devices and input technologies (e.g., mobile phones, touch, or pen-enabled interfaces).
  7. To formalize and explore the design space for novel authoring interfaces to democratize data-driven storytelling, focusing on audiences that are not able to program their own custom experiences.
  8. To build individual collaborations between the seminar attendees and hence, build the community around data-driven storytelling research.
The chapter topics in this book were chosen through full group discussions. Once the topics were selected, smaller subgroups with particular interest and expertise in a given topic discussed the topic in depth. Thoughts presented in this book are the results of these conversations that were initiated at the seminar and were pursued over a year and finalized as chapters in this book.


Since this book has emerged from cross-discipline discussions, it seems appropriate to define, for use in this book, common terms in use in all communities with often slightly variant definitions. We include the definitions of key concepts for data-driven stories below.
InfoVis: the definition from Card, Mackinlay, and Shneiderman (1998) as “the use of computer-supported interactive visual representations of abstract data to amplify cognition” still serves well and provides us with our workin...

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