Further Reading: Storytelling with Data

Tier 1: Essential Reading

Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015. The book the chapter's "Big Idea" terminology comes from and the single most practical treatment of data storytelling for business audiences. Knaflic's framework — context, choose an appropriate visual, eliminate clutter, focus attention, tell a story — is a complete operationalization of the principles in Parts I and II of this textbook. Chapters 6 ("Model lessons in storytelling") and 7 ("Lessons in storytelling") are directly relevant to this chapter. Read this book first if you are serious about data storytelling, especially for business audiences.

Rosling, Hans, Ola Rosling, and Anna Rosling Rönnlund. Factfulness: Ten Reasons We're Wrong About the World — and Why Things Are Better Than You Think. Flatiron Books, 2018. The posthumous book that summarizes Hans Rosling's approach to data communication (see Case Study 1). Factfulness is not a chart design book — it is a book about how to think about the world in ways that match empirical evidence rather than outdated intuitions. But the book's treatment of mental models, audience misconceptions, and the specific examples Rosling used in his presentations makes it essential reading for any data storyteller who wants to understand what made Rosling's work so effective.

Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication. New Riders, 2016. Already cited for Chapters 4, 6, and 7, Cairo's book also contains an excellent treatment of data storytelling in the journalism tradition. His framework for narrative structure in visualizations, combined with his attention to the ethics of framing, makes The Truthful Art a direct complement to Knaflic's more business-oriented approach. The chapters on "The Functional Art" and "The Insightful Art" are most relevant to this chapter.

Dykes, Brent. Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals. Wiley, 2019. Dykes's treatment of data storytelling focuses specifically on the "drive change" dimension — how data stories should lead to action, not just understanding. His framework for narrative arc and call-to-action design complements Knaflic's more presentation-oriented approach. Particularly strong for readers whose data stories are meant to influence organizational decisions.

Segel, Edward, and Jeffrey Heer. "Narrative Visualization: Telling Stories with Data." IEEE Transactions on Visualization and Computer Graphics 16, no. 6 (2010): 1139-1148. An academic survey of data storytelling techniques that proposes a taxonomy of "narrative visualization" structures (martini glass, drill-down story, interactive slideshow). The paper is cited in most subsequent academic work on the topic and provides a useful framework for categorizing the different forms a data story can take. Freely available through the ACM or IEEE digital libraries.

Shneiderman, Ben. "The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations." Proceedings of the 1996 IEEE Symposium on Visual Languages, 1996. The paper containing Shneiderman's original formulation of "Overview first, zoom and filter, then details on demand" — the progressive disclosure mantra that structures Section 9.4 of this chapter. Short, classic, and foundational. Worth reading in its original form even though the ideas are now absorbed into the broader literature.

Bertini, Enrico, and Michael Stefaner. Data Stories (podcast). Available at datastori.es. Not a book, but one of the most important continuing conversations about data visualization and storytelling. The podcast has featured interviews with most of the major practitioners in the field — Cole Knaflic, Alberto Cairo, Jeffrey Heer, Jen Christiansen, John Burn-Murdoch, Amanda Cox, Hannah Fairfield, and many others. A listener who works through the back catalog will develop a sophisticated understanding of the field's debates and practices.

Duarte, Nancy. Resonate: Present Visual Stories that Transform Audiences. Wiley, 2010. Duarte's book is not specifically about data visualization — it is about presentation design more generally — but her framework for narrative arc in presentations, including her "sparkline" diagrams of how a presentation builds tension and resolution, applies directly to data storytelling. Pair with Knaflic for a complete view of data storytelling from two complementary angles (Knaflic for chart-specific, Duarte for presentation-specific).

Wong, Dona M. The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures. W.W. Norton, 2010. Already cited in Chapter 7, Wong's book includes practical guidance on how to structure multi-chart presentations — what to put first, how to sequence related charts, how to build to a conclusion. Complements the narrative focus of Knaflic and Duarte with specific workflow advice from one of the most experienced graphics editors in data journalism.

Lee, Bongshin, Nathalie Henry Riche, Petra Isenberg, and Sheelagh Carpendale. "More Than Telling a Story: Transforming Data into Visually Shared Stories." IEEE Computer Graphics and Applications 35, no. 5 (2015): 84-90. An academic framework for how data stories are constructed and shared, with specific guidance on the roles of annotation, narrative, and interaction. Useful for readers who want a more theoretical treatment of the questions that this chapter treats practically.

Tier 3: Examples and Inspiration

These are not books but sources of example data stories that can train your eye. Study them with the analytical framework from this chapter.

Resource URL / Source Description
The New York Times Upshot and Graphics nytimes.com/section/upshot The most consistently high-quality body of data storytelling in the English-speaking world. The Upshot section and the broader Graphics output provide countless examples to study. See Case Study 2 for more on the NYT scrollytelling tradition.
The Pudding pudding.cool A data-driven visual essays publication that specializes in scrollytelling and interactive data stories. Less polished than the NYT in some ways, more experimental in others. Essential reading for anyone interested in the edge of what data storytelling can do.
Our World in Data ourworldindata.org Max Roser's project, which produces charts and stories about long-term global trends. Less narrative than the NYT and more reference-oriented, but the charts are excellent and the written analyses are thorough. The site is also a good example of how to structure a large body of data content.
FiveThirtyEight fivethirtyeight.com Less active now than in its peak years, but the archive of data stories from FiveThirtyEight's prime (2014-2020) is a rich source of examples of data-driven narrative journalism. Nate Silver's election forecasting work is particularly relevant.
Financial Times Visual Journalism ft.com/visual-and-data-journalism The FT's data journalism team produces consistently excellent work, often with a different sensibility than the NYT (more restrained, more business-focused). John Burn-Murdoch's pandemic work (see Case Study 2 in Chapter 5) is a canonical example.
Reuters Graphics graphics.reuters.com Reuters's data journalism team produces elaborate interactive pieces on elections, climate, and other major stories. Worth studying for their use of maps and multi-panel layouts.
The Washington Post Graphics washingtonpost.com/graphics Another major American data journalism outlet with a distinctive visual style. Particularly strong on political and election coverage.
Gapminder gapminder.org Hans Rosling's foundation. The site includes the original Trendalyzer tool (now Gapminder World), the "Ignorance Project" survey, Dollar Street (a visual journey through global income levels), and a large collection of educational materials. Essential for understanding the Rosling tradition discussed in Case Study 1.
Information is Beautiful informationisbeautiful.net David McCandless's project. A mixed quality — some pieces are brilliant, others are overdesigned — but worth browsing for the range of what information design can look like. Annual "Information is Beautiful Awards" are a useful way to see the current state of the field.
Edward Tufte's website edwardtufte.com Tufte's personal site. Contains essays, gallery examples, and details about his workshops. Tufte is less focused on storytelling per se than on graphical excellence, but his examples are foundational and his treatment of small multiples (relevant to Chapter 8) connects directly to data storytelling.

Notes on Studying Examples

The best way to develop data storytelling judgment is to study examples with discipline. Passive viewing of good work is not enough; active analysis is what builds the skill. Some specific practices:

Annotate the structure. For a piece you are studying, identify the Big Idea in one sentence. Identify the three-act structure (which charts are context, which are evidence, which are implications). Identify the audience the piece is written for. Identify the visual emphasis techniques used at each step.

Ask what you would change. Every piece of published work has trade-offs. Identify one thing the piece does well that you would not have thought to do. Identify one thing the piece does that you think could be improved. The exercise is not to judge the piece but to train your judgment by making specific, defensible claims about its choices.

Look for the cuts. Professional data stories have gone through extensive editing. A piece with five charts probably started as a draft with ten. Try to imagine what the cut charts might have been and why they were cut. The decisions about what to leave out are often more interesting than the decisions about what to include.

Read the same story across multiple outlets. When a major story breaks (a pandemic, an election, a scientific finding), multiple outlets produce data stories about it. Reading several versions from different publications reveals how different editorial decisions produce different stories from the same underlying data. The comparison is educational because it makes the editorial choices visible.

Return to the same pieces multiple times. A great data story rewards repeated reading. The first reading gives you the main point; the second reading reveals the structural choices; the third reading shows the smaller decisions about typography, color, and annotation. Pieces like Rosling's TED talk, the NYT's 2018 racial mobility piece, and the FT's pandemic trajectory charts are worth returning to every few years as your own judgment develops.

A note on reading order: If you want one additional book beyond this textbook, read Knaflic's Storytelling with Data. It is the most practical treatment of the material in this chapter and can be read in a long weekend. For a historical and broader view, add Rosling's Factfulness and Cairo's The Truthful Art. For the academic framework, add Segel and Heer's "Narrative Visualization" paper. For examples, spend an hour a week studying one piece from the NYT, Pudding, or FT archives. The craft builds with practice, and practice is easier when you have examples of excellent work to study and principles from books to guide you.