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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Unlock the power of data storytelling to create change
Become a Great Data Storyteller: Learn How You Can Drive Change with Data is a transformational journey guided by author and researcher Angelica Lo Duca. This insightful guide challenges the conventional approach to data visualization by emphasizing the creation of compelling data stories. With a focus on understanding the audience's needs, this book offers a unique value proposition: teaching you how to weave raw data into engaging, narrative-driven presentations that can significantly impact decision-making and generate organizational change.
The author masterfully demonstrates the process of building a data story, from creating relatable characters with clear objectives to tailoring these narratives for specific audiences. You'll discover your role as a narrative guide, learning how to employ the power of context to make your data-driven stories not just informative but captivating. This book sets itself apart by focusing on the human aspect of data storytelling, ensuring your narratives resonate deeply with your audience.
In the book, you'll:
- Learn to craft engaging data narratives that go beyond charts and graphs, making your presentations memorable and impactful
- Tailor your data stories to meet the specific needs and interests of your audience, enhancing understanding and engagement
- Position yourself as a pivotal guide in your data stories, using context to turn data into powerful narratives that drive action
For anyone looking to elevate their data presentation skills from ordinary to extraordinary, Become a Great Data Storyteller offers the tools and insights you need. Whether you're a professional seeking to influence decision-making or simply passionate about the art of data, this book is your roadmap to becoming an impactful storyteller. Take the first step towards transforming your data into compelling stories that inspire change. Order your copy today and start changing the way you communicate.
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Seitenzahl: 459
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Introduction
What Does This Book Cover?
Additional Resources
CHAPTER 1: Why You Need to Become a Data Storyteller
The Need for Data Storytelling
What Is Data Storytelling?
Case Study: Solstices and Equinoxes
Takeaways
References
CHAPTER 2: The Role of the Storyteller
Who the Data Storyteller Is
Three Types of Data Storytellers
The Data Storyteller Skills
The Data Storyteller's Role in the Story
Possible Scenarios in Which to Tell a Story
Examples of Great Storytellers
Takeaways
References
CHAPTER 3: Making a Successful Data-Driven Story
Preproduction: The Data Story Script
Production: Data Story Shot
Postproduction: Data Story Editing
Working in a Team
Takeaways
References
Note
CHAPTER 4: First Act: Defining the Hero
Who the Hero Is
How to Extract the Hero from the Data
Describing the Hero
Takeaways
References
CHAPTER 5: First Act: Defining the Sidekick
Who the Sidekick Is
Adding a Sidekick to Your Data Story
Discovering the Humanity of Your Hero
Presenting the Hero and the Sidekick: The First Act
Takeaways
References
CHAPTER 6: Second Act: Defining the Problem
Introducing the Problem
Problem Types
Describe the Problem
Communicate the Problem
Takeaways
References
CHAPTER 7: Second Act: Defining the Antagonist
Who Is the Antagonist?
Various Types of Antagonists
How to Extract the Antagonist from the Data
How to Add the Antagonist to the Story
Presenting the Problem: The Second Act
Takeaways
References
CHAPTER 8: Third Act: Setting the Climax and Next Steps
The Dark Night
The Climax
Next Steps
Structuring the Third Act
Takeaways
References
CHAPTER 9: From Making to Delivering a Data-Driven Story
Completing the Making Phase
Production: Data Story Shot
Postproduction: Data Story Editing
Starting the Delivery Phase
Takeaways
References
CHAPTER 10: What the Audience Wants and Knows
Defining the Audience
What the Audience Wants
What the Audience Knows
Takeaways
References
CHAPTER 11: What the Audience Thinks
Introducing the Audience's Thoughts
The Stomach-Heart-Brain Theory
Adapting Your Story
Takeaways
References
CHAPTER 12: Retelling the Story
Introduction to Retelling
You as the New Narrator of the Story
The Audience as the New Narrator of the Story
Final Thoughts
Takeaways
References
Index
Copyright
Dedication
About the Author
About the Technical Editor
Acknowledgments
End User License Agreement
List of Tables
Chapter 1
Table 1.1: Diverse Forms of Data Communication and Most Appropriate Usage of...
Chapter 2
Table 2.1: Strategies to Become Humbler
Table 2.2: Strategies to Become More Sincere
Table 2.3: Strategies to Show Your Vulnerabilities
Table 2.4: Strategies to Become Empathetic and Open-Minded
Chapter 3
Table 3.1: Possible Types of Analysis for Different Domains
Table 3.2: Values for Literary Genres
Table 3.3: Values for Major Domains
Chapter 4
Table 4.1: The Data–Hero Humanity Matrix
Table 4.2: Mapping Between Hero Types and Concreteness Levels
Table 4.3: The Data–Hero Concreteness Matrix
Table 4.4: Datasets for the Earthquakes Example
Chapter 5
Table 5.1: Examples of Sidekicks
Table 5.2: Different Examples of Association Between Data, Hero, and Sidekic...
Chapter 6
Table 6.1: Five Examples Using the Different Types of Problems
Chapter 7
Table 7.1: Antagonist Examples
Table 7.2: Four Types of Antagonists in a Story, Their Characteristics, and ...
Table 7.3: Examples of Stories Extracted from the Data
Chapter 8
Table 8.1: Mapping the Five Dark Night Types to the Seven Story Types Define...
Table 8.2: Mapping the Five Types of Dark Nights and Possible Reactions in t...
Table 8.3: Examples of Stories Extracted from the Data, Enriched with the Tw...
Chapter 10
Table 10.1: The Main Elements You Can Play with Based on the Audience's Obje...
Table 10.2: What Happens If The Audience's Objectives Do Not Coincide with T...
Table 10.3: The Main Elements You Can Play On, Based on the Audience's Knowl...
Chapter 11
Table 11.1: Main Elements You Can Play On, Based on What the Audience Thinks...
Table 11.2: Text Hook Examples Based on What the Audience Thinks
Table 11.3: Which Data to Use in the Reaction Phase Based on the Audience's ...
Chapter 12
Table 12.1: Organization of the Book as a Story
List of Illustrations
Chapter 1
Figure 1.1: The hero's journey from an initial situation (before) to a new e...
Figure 1.2: The difference between data reporting, data presentation, and da...
Figure 1.3: Transforming data into stories involves three phases: 1) explore...
Figure 1.4: The three phases of creating a film include 1) preproduction, 2)...
Figure 1.5: The three phases of creating a data-driven story include 1) prep...
Figure 1.6: Once produced, a story can be adapted to various audiences, base...
Figure 1.7: The three possible purposes of a story: 1) to persuade, 2) to in...
Figure 1.8: Making the story means defining the message. Delivering the stor...
Figure 1.9: Seasonal (yearly) changes in the length of daylight hours in the...
Figure 1.10: A sequence of four scenes explaining what happens to the Earth'...
Figure 1.11: The sequence shows three scenes: 1) the initial moment in the p...
Figure 1.12: A possible implementation of the three scenes of the story desc...
Chapter 2
Figure 2.1: A graphical explanation of four types of social values
Figure 2.2: The three types of data storytellers
Figure 2.3: The apathetic data storyteller fails in building the bridge betw...
Figure 2.4: The authoritarian data storyteller fails in building the bridge ...
Figure 2.5: The authoritative data storyteller builds the bridge between the...
Figure 2.6: The most important skills a great data storyteller should have
Figure 2.7: The presence of the data storyteller in the story
Figure 2.8: The two scenarios to tell a story: synchronously (live and onlin...
Chapter 3
Figure 3.1: The three main elements define the theme of a story
Figure 3.2: A chart showing the average temperature anomalies since 1860
Figure 3.3: The three elements that make up the subject: the hero, the hero'...
Figure 3.4: The three-act structure of the plot
Figure 3.5: An example of dividing a story's plot into sequences and scenes...
Figure 3.6: The first act of the story on global temperature anomalies from ...
Figure 3.7: The story's diagram shows the second act of the story about glob...
Figure 3.8: The third act of the story on global temperature anomalies from ...
Figure 3.9: A possible implementation of the second scene of the first act s...
Figure 3.10: A possible implementation of the third scene of the first act i...
Figure 3.11: The three people involved in the creation of a film
Chapter 4
Figure 4.1: The two types of heroes in data stories: humanlike and nonhumanl...
Figure 4.2: The geographical distribution of earthquakes with magnitude grea...
Figure 4.3: Number of DYFI answers in the different cities normalized to the...
Figure 4.4: A more fine-grained classification of the various hero types
Figure 4.5: An example of how to group data related to earthquakes into homo...
Figure 4.6: The workflow to extract the hero from the data, combining both t...
Figure 4.7: The hero's identikit is based on the answer to the five W questi...
Chapter 5
Figure 5.1: The four roles of a sidekick in narratives
Figure 5.2: Sidekick types in data-driven stories
Figure 5.3: The process of extracting a sidekick from data-driven stories
Figure 5.4: The ecosystem of potential sidekicks
Figure 5.5: The first act timeline in novels
Figure 5.6: The first act timeline in data storytelling
Figure 5.7: The story's first act on global temperature anomalies from 1860 ...
Chapter 6
Figure 6.1: The graph shows the hero's resistance to the problem over time. ...
Figure 6.2: The graph shows a steady increase in product sales. Because ther...
Figure 6.3: The graph shows a decline in product sales during June and July ...
Figure 6.4: Various types of data analysis
Figure 6.5: The three ways of describing a problem
Figure 6.6: A graph showing the average temperature anomalies since 1860
Figure 6.7: Two strategies for communicating a problem
Chapter 7
Figure 7.1: Five tips to build a complex antagonist, according to bestsellin...
Figure 7.2: The four types of an antagonist in a story
Figure 7.3: The seven techniques for identifying a problem
Figure 7.4: Twelve possible causes relating to the decline in sales of a pro...
Figure 7.5: An example of a scene with a direct confrontation between the he...
Figure 7.6: An example of a scene with a direct confrontation between the he...
Figure 7.7: The first act timeline in data storytelling
Figure 7.8: The second act timeline in storytelling
Figure 7.9: The second act timeline in data storytelling
Figure 7.10: The second act of the story about global temperature anomalies ...
Chapter 8
Figure 8.1: The story structure up to the second act
Figure 8.2: The five types of Dark Nights
Figure 8.3: Antonio's story of wanting to learn to ride a bike without train...
Figure 8.4: The sales history of the product
Figure 8.5: Product sales story represented in a single scene, with a single...
Figure 8.6: The two climax types
Figure 8.7: Possible placements of the next steps in the story
Figure 8.8: The decision tree for the next steps choice
Figure 8.9: The structure of the third act in cinema and novels
Figure 8.10: The structure of the third act in data storytelling when the ne...
Figure 8.11: The structure of the third act in data storytelling when the ne...
Figure 8.12: The third act of the story on global temperature anomalies from...
Chapter 9
Figure 9.1: The three phases of making a data-driven story include preproduc...
Figure 9.2: The eight types of scenes for the representation of a piece of t...
Figure 9.3: A possible implementation of the buildup phase in the first act ...
Figure 9.4: The possible special effects to include in a scene
Figure 9.5: A possible implementation of the first scene describing the hero...
Figure 9.6: A possible implementation of the second scene describing the her...
Figure 9.7: The editing techniques described in this book
Figure 9.8: An example of a flashback that uses the Dark Night as a hook and...
Figure 9.9: A possible editing of parallel stories, showing the story of the...
Figure 9.10: Some of the most popular types of editing, which you can also u...
Figure 9.11: Some techniques to study the audience
Chapter 10
Figure 10.1: Classification of an audience based on the objective, topic kno...
Figure 10.2: The three objectives that the audience wants to achieve when th...
Figure 10.3: The parts of the story that you can work on, based on the audie...
Figure 10.4: How to adapt the first plot point, based on the audience object...
Figure 10.5: How to adapt the second plot point, based on the audience objec...
Figure 10.6: How to adapt the next steps, based on the audience objective
Figure 10.7: The audience tree with respect to what it knows
Figure 10.8: The parts of the story you can work on, based on the audience's...
Figure 10.9: How to adapt the hero's presentation, based on the audience's k...
Figure 10.10: How to adapt the context, based on the audience's knowledge
Figure 10.11: How to adapt the antagonist based on the audience's knowledge...
Figure 10.12: How to adapt the action, based on the audience's knowledge
Figure 10.13: How to adapt the Dark Night, based on audience knowledge
Chapter 11
Figure 11.1: What the audience thinks when they encounter a story
Figure 11.2: An example of a spaghetti graph used to provoke a disinterested...
Figure 11.3: Two examples of difficult-to-read graphics used to provoke a di...
Figure 11.4: The Stomach-Heart-Brain theory
Figure 11.5: Engagement at the Stomach level of the audience during the thre...
Figure 11.6: Engagement at the audience's heart level during the story's thr...
Figure 11.7: Engagement at the brain level of the audience during the three ...
Figure 11.8: Engagement at the audience's stomach, heart, and brain level du...
Figure 11.9: The parts of the story (in dark gray) that you can work on, bas...
Figure 11.10: How to adapt the hook based on the audience's thoughts
Figure 11.11: How to adapt the inciting event based on the audience's though...
Figure 11.12: How to adapt the buildup based on what the audience thinks
Figure 11.13: How to adapt the data presented during the reaction phase base...
Figure 11.14: How to adapt the Climax based on what the audience thinks
Chapter 12
Figure 12.1: Level of internalization of a story compared to the number of t...
Figure 12.2: Level of understanding of the story based on the number of reli...
Figure 12.3: The main characteristics of an internalized story
Figure 12.4: The three main elements defining the theme of a story
Figure 12.5: A possible scheme of channels used at each retelling of the sto...
Figure 12.6: Memory retention using spaced repetition
Figure 12.7: Who can potentially retell the story to the audience
Figure 12.8: Two ways of retelling a story
Guide
Cover
Title Page
Introduction
Table of Contents
Begin Reading
Index
Copyright
Dedication
About the Author
About the Technical Editor
Acknowledgments
End User License Agreement
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Become a Great Data Storyteller
Learn How You Can Drive Change with Data
Angelica Lo Duca
Introduction
What you're holding isn't just another book about the general concepts of data storytelling or a set of strategies for best visualizing your data. This book takes you far beyond the basics, equipping you with a proven framework borrowed from the world of cinema and literature—where stories captivate hearts and minds. Step by step, you'll learn how to craft data-driven stories that mirror the structure of great films and novels. You'll understand how to build characters (yes, even in data!), create a gripping plot, and arrange your narrative into a sequence that resonates with your audience. The inspiration for this book is simple: As data storytelling becomes increasingly essential in today's world, there's a pressing need for a guide that not only explains why stories matter but shows you exactly how to tell them. This is that guide.
I have always had a passion for stories, but until I met my Italian teacher in high school, I never knew how to organize and tell them following a logical structure. I remember feeling incredibly frustrated when I wrote stories that started and never finished or went on too long in the introduction and ended suddenly. I crafted very unbalanced stories. Even at school, the essays I did in class weren't that great. They had no structure; they talked about everything and nothing. Then, a flash of genius: My Italian teacher one day explained how to structure a story. From that day, I learned to write balanced, well-organized stories that follow a logical thread. I was only 15 years old. Many years have passed since then, but the desire to tell stories is still alive.
This book derives from the desire to share what I have learned with you, dear reader, to ensure that your data-driven stories are also structured, balanced, and follow a logical thread from beginning to end. Surely, you already know how to analyze data, and perhaps you also know how to present it appropriately. But have you ever thought about organizing your data precisely like a story with characters and a plot? Have you ever thought each story phase must last a certain time to be balanced? Have you ever thought that in order to make the audience listen to you while presenting your story, you must create a certain suspense? In one sentence, have you ever thought about designing your own data-driven story using the techniques that screenwriters, directors, or novelists use?
Well, in this book, you'll do just that: apply cinematic techniques to data storytelling! You will extract a hero from the data, a sidekick, and even an antagonist. You will build a plot based on the goal you want to achieve and structure it in three acts, just like screenwriters and novelists do. Each act will play a role in the story.
Throughout the book, I will use the story of rising temperatures in recent years as an example. I am not an environmental expert, so the example is only for demonstration. If you find any inaccuracies, please be patient and possibly report them to me through the contacts you see in this book.
So, are you ready to face this journey together?
What Does This Book Cover?
This book covers the following topics:
Chapter
1
: Why You Need to Become a Data Storyteller
This chapter introduces the concept of data storytelling and explains why it is essential for communicating insights effectively. Data storytelling differs from data reporting and presentation in that it focuses on creating a narrative involving a “hero” and a “plot.” Unlike simple data presentations, data storytelling constructs a coherent story around data points, highlighting the people, events, and transformations the data represents. The chapter emphasizes that stories are powerful because they resonate emotionally, creating a bridge between the storyteller and the audience.
Chapter
2
: The Role of the Storyteller
This chapter explores the qualities of a great data storyteller, likening their role to a director who must make data the focus rather than themselves. It identifies three types of storytellers—apathetic, authoritarian, and authoritative—and emphasizes the importance of being authoritative by engaging, collaborating, and building trust with the audience. Essential skills for great data storytelling include humility, sincerity, vulnerability, empathy, flexibility, openness, and patience. The chapter also outlines three roles a storyteller can play—external, internal, or absent—and discusses synchronous and asynchronous storytelling methods.
Chapter
3
: Making a Successful Data-Driven Story
This chapter delves into the “making” phase of creating a data-driven story, focusing on planning and structuring the narrative through three essential stages: preproduction, production, and postproduction. Preproduction requires selecting a theme, defining the subject, and planning scenes. Production is the story implementation and can be done using any tool. The final step, postproduction, refines and organizes the story by selecting and polishing scenes, ensuring smooth transitions, and enhancing visual and narrative clarity.
Chapter
4
: First Act: Defining the Hero
This chapter focuses on the foundational aspect of data storytelling: defining the hero. A hero in data storytelling differs from literary heroes, as they emerge directly from the data rather than from the imagination. Heroes in data stories are characterized by their quest for an “object of desire” but face obstacles that hinder their journey. Unlike traditional storytelling, the hero in data storytelling must be grounded in real-world elements, either human-like (connected directly to people) or non-human-like (such as abstract concepts or phenomena).
Chapter
5
: First Act: Defining the Sidekick
This chapter explores the role of the supporting character (i.e., the sidekick) in data storytelling, emphasizing that while the hero remains central, a well-crafted sidekick adds depth and resonance to the story. In data storytelling, a sidekick can serve several functions, from being a confidant who reveals the hero's inner thoughts to acting as a proxy for the audience, simplifying complex data.
Chapter
6
: Second Act: Defining the Problem
This chapter centers on defining the problem in data storytelling, which is essential for crafting a compelling narrative. A hero's journey in storytelling only gains meaning with a clear problem that needs solving. Without a problem, there's no story—just a straightforward data presentation. This chapter explains the importance of the problem and describes how to identify and describe it.
Chapter
7
: Second Act: Defining the Antagonist
This chapter defines the antagonist in data storytelling and structuring the story's second act. An antagonist in storytelling opposes the hero and heightens the story's conflict, making the hero's journey to achieving their goal more compelling. In data-driven stories, antagonists aren't just villains but can be forces, circumstances, or even internal conflicts that create obstacles for the hero.
Chapter
8
: Third Act: Setting the Climax and Next Steps
This chapter delves into the structure and elements of the third act in data storytelling, which is critical for resolving the story's tension and providing a satisfying conclusion. Known as the climax phase, this act includes a series of steps to bring the story to its peak: the Dark Night, the Climax, and the Next Steps. Together, these elements provide closure, impact, and motivation for the audience to take action based on the story's message.
Chapter
9
: From Making to Delivering a Data-Driven Story
This chapter discusses the transition from creating a data-driven story to effectively delivering it, focusing on crafting and adapting the story for the audience. With the story completed, the delivery phase involves tailoring it to the specific audience. The chapter emphasizes that while the story's core remains unchanged, its presentation and emphasis should adapt to the audience's expectations and knowledge level.
Chapter
10
: What the Audience Wants and Knows
This chapter begins by underscoring that every audience is unique. Even a small group may consist of individuals with varied responses shaped by their personal beliefs and backgrounds. Therefore, effective data storytelling involves recognizing and respecting this diversity while striving to engage each person individually. This understanding is foundational to audience analysis, a critical skill in storytelling that involves identifying the characteristics, interests, and knowledge levels of different audience segments.
Chapter
11
: What the Audience Thinks
This chapter continues the story adaptation to the audience. While the previous chapter focused on the audience's needs and knowledge, this chapter focuses on the audience's perceptions, which specify how the audience behaves after encountering the story.
Chapter
12
: Retelling the Story
This chapter highlights the importance of repeating data-driven stories to overcome the forgetting curve, which causes most people to forget 90 percent of what they hear within a week. Retelling through varied channels and formats keeps the message alive, helps audiences internalize it, and broadens its reach. Techniques like spaced repetition ensure effective timing while adapting the context, and presentation maintains interest. The chapter emphasizes that data storytellers and audiences benefit from this process, potentially becoming new narrators who spread and enrich the story.
Additional Resources
Sprinkled throughout the book are featured sidebars that offer those extra bits of information that will enable you to further your storytelling.
Challenge: This resource is an excellent way to get you started in developing your story, as it provides challenging opportunities for you to complete your craft.
This sidebar will include general bits of information that will explain and aid you in understanding data storytelling.
NOTE This sidebar includes notes on items of interest in data storytelling. Pay attention as you might just find more information to learn here.
How to Contact the Author
You can contact the author in the following ways:
LinkedIn:
https://www.linkedin.com/in/angelicaloduca
Medium:
https://alod83.medium.com
CHAPTER 1Why You Need to Become a Data Storyteller
So, you have decided to become a data storyteller. You have probably already had several opportunities to present your data to an audience—either your boss, your teams, or even a large number of people—and you've likely had the impression of not being understood by those who listened to you. You've probably even had one big disappointment when few have listened to your words after working hard on your data. Not having had time to organize your speech at the time of presentation, you might have gotten lost in the details of this or that data, without giving a broader scope to your speech. Or, you've had plenty of time to organize the results of your data analyses, but in the end you simply didn't get the desired results, that is, having your data used for something.
I understand you. I also found myself in the same situation more than a decade ago. I was at a conference in Germany, in Bonn to be precise, presenting the preliminary results of exciting research on underwater networks. I had a minute available (yes, just one minute) to convince the audience to come and see the poster I presented. I had studied a lot to prepare that poster. For me, it was a really important moment. In the only slide I could use for the presentation, I had included many data visualizations describing the results of my analysis and had rehearsed the words to say on stage many times. But when my turn came, I slowly climbed the steps toward the stage and my mind went dark. I remembered all the tests I had done, the graphs of the results, and the software written. I wanted to collapse all the work done in that single minute. I began to speak quickly, describing all the data visualizations without any order, almost as if I wanted to concentrate all my work into one minute. I talked about numbers, values, tests performed, and so on and so forth. The result: my presentation was an absolute disaster. I became lost in the details, and the minute flew by quickly. In the end, almost no one understood what my poster was about or came to see it. It was a real disaster. I returned home very disappointed, believing that I wasn't understood and thinking that the work I'd done wasn't as important as I had assumed. The real problem was that I presented the results of my analysis without a specific order, a central point, and an end. Probably, organizing my presentation as a short story would have persuaded the audience to understand the importance of my poster.
That terrible experience taught me a lot. Over the years, I have understood the power of stories and how telling stories makes a difference to the audience. In this book, you will learn to become a data storyteller so you will no longer be frustrated, disappointed, or messy in the presentation of your data.
To tell the truth, you are already a storyteller. You might not know it, however. Each of us is a storyteller, because telling stories is an innate characteristic of every human being. Think about all the times that you've told your friends or family about an event or fact from your life. Maybe you spoke about that time you went to the mountains for the first time or the time you flew with your children. Maybe you've never noticed it, but you've told many stories in your life. Likely, they were primarily stories about your life. When you told them, you felt emotions, not only yours but also those who listened to you. Throughout your story, you established a bridge between what you told and the people who listened to you. In some cases, sometime later, you still hear your friends or family talking about that story you told them sometime before.
Yes, a story can establish a strong bond between you and those who listen to you. A story can overcome the barriers of time and space. It can make you see with the eyes of your imagination what is heard with words. It can bring to mind the past and project toward the future. It can also make you navigate in space and take you thousands of miles across distances. Indeed, stories are powerful things.
The Need for Data Storytelling
If you want to become a data storyteller and even a great data storyteller, you must learn to tell other people's stories. You must understand the need for great data storytelling. A great data storyteller tells the stories of others—of people, of events, of situations, of concepts, of places—that hide behind the data. Data does not arise by chance and behind it there are people who have produced it with their behavior, with their situations. And these people are waiting for someone to tell their stories. Sometimes, these stories are of joy or of suffering; other times they're business stories, stories of failed products, stories of sales, stories of success, stories of anything. But behind these stories are people. And you are the right person to tell their stories—the stories behind the data.
In this book, you will learn to become a data storyteller—someone who not only analyzes data but also knows how to look beyond and communicate it effectively to those who'll listen. A data storyteller is just that: someone who acts as an intermediary between the people behind the data and those in the audience. A data storyteller acts as the bridge between them. There's a gap between the data and the audience, and you have the task of filling this void. Kindra Hall, the author of many bestselling books in the field of storytelling, states in her book Stories That Stick that if you want to win the game, you must build the bridge that fills the void (Hall, 2019). This is precisely your job: building the bridge between data and audience. So, what are you waiting for? Off you go.
What Is Data Storytelling?
Let's start from the beginning. We are neither the first nor the last to tell stories. Since our origins, humans have told stories: stories to explain the origin of the universe or the causes of phenomena such as myths and legends, stories to entertain children such as fairy tales, stories to explain more profound concepts, and stories based on actual events. Stories of all kinds. What is a story, and why is it so important to tell stories? In his book Building a StoryBrand: Clarify Your Message So Customers Will Listen, Donald Miller states that a story is the most potent weapon we have to combat noise because it organizes information so that people are forced to listen (Miller, 2017). A story breaks barriers and places the storyteller and the listener on the same wavelength. A story makes us feel deeply human, with our strengths and weaknesses, our hopes and disappointments, and our feelings. Stories are an important part of the human experience.
Analyzing a Story
Let's analyze what a story is with a more formal eye. Note, not all stories are created equal; gifted raconteurs have been passing along their technique of storytelling over time. In this book I hope to do something similar for modern-day storytellers—to help you develop your storytelling technique.
A story is a narrative of events organized in a temporal sequence (Forster, 1927). These are not unrelated events but are rather organized in one intertwining narrative. The plot is the order in which events are presented. It is the first element that transforms a simple set of events into a story. Think about the last movie you watched or the last novel you read. Analyze it according to the previous definition: related events organized in a temporal sequence. Definitely, in the story, something happened. Several events occurred that followed one another over time and were related to each other. So, there was a before and an after. To have a story, therefore, there must be a change—a before and an after. Something must happen in the middle that messes everything up. Go back to your latest novel or film. What was it that changed everything? Probably an unexpected event—a complication that happened to someone, perhaps the hero. So, the second element, the hero, appears fundamental to the story. The hero is the one with whom the audience identifies. The hero, however, is only one of the characters in the story. There are others, such as the antagonist and the sidekick. But we will see this over the course of the book shortly. Now, all you need to know is that a story is a narrative telling about the change that the hero experiences. A story is described through a series of related events. Figure 1.1 summarizes the simplified structure of a story, with the plot and the hero.
Figure 1.1: The hero's journey from an initial situation (before) to a new equilibrium (after) through a change (the plot), where something happens
So far, we have talked about the generic structure of a story. Thinking back to your last movie seen or novel read, it would just seem like it's the structure of a creative story. In reality, specific studies explain that any story, whether fiction or nonfiction, must have a plot and characters, including the hero, to be recognized as such (Rayfield, 1972). So, like it or not, if we're talking about data storytelling, we must have a plot and characters. Otherwise, those who listen to us won't think that we are telling a story but simply that we are presenting data. In fact, there's often confusion between data presentation and data storytelling, as the two terms are used interchangeably. In reality, when executing a data presentation, it is sufficient to present the data; when performing data storytelling, instead we have to tell a story.
Data storytelling is, therefore, telling a story based on data. Compared to novels or films, in data storytelling the subject of the story changes, which must be connected to reality—to events and facts contained in the data. Throughout the book, you will see how to extract characters and a plot from data. But for now, however, just know that data storytelling is not a simple presentation of data, and it is not even a data report. Figure 1.2 summarizes the difference between data reporting, data presentation, and data storytelling.
Figure 1.2: The difference between data reporting, data presentation, and data storytelling. All receive data as an input, but the produced output is different
All three are data driven. Data reporting describes the data; data presentation organizes it in a clear and visually comprehensible way; and data storytelling extracts a plot and characters from the data and tells them to an audience. Use data reporting to provide a comprehensive overview of the data without much interpretation. If you want to convey complex data through dashboards, instead use data presentation. Finally, to engage and persuade your audience, use data storytelling. The audience is the third central element of data storytelling. We will review the role of the audience in more detail later. For now, it's enough to know that when you tell a story, you always tell it to someone, and it's never about yourself. Table 1.1 shows when you should use each data communication form and its main pros and cons.
Challenge: Now, think about the last speech you listened to or a time you chatted with a friend that particularly impressed you. Was it a story, a report, or a presentation?
If we reflect on the previous challenge, we note that the impact of a speech often hinges not merely on its content but on its delivery and form. Whether it is a captivating story, an informative report, or a polished presentation, the way in which information is conveyed can significantly influence its reception by the audience. A speech that leaves a lasting impression typically possesses a combination of factors, including compelling storytelling, articulate delivery, and relevance to the audience. A well-told story has the potential to transport the audience into the narrative, making the message more memorable.
Now that you know that a data-driven story involves characters and plot, let's see how to build a story starting from data.
Table 1.1: Diverse Forms of Data Communication and Most Appropriate Usage of Each
WHEN TO USE
PROS
CONS
Data Reporting
Reports without interpretation
Focuses on factual representation
May lack audience engagement
Data Presentation
Dashboards
Adds a layer of clarity and accessibility to the data
Can be difficult to extract a call to action
Data Storytelling
Presentations
Humanizes the data by contextualizing it within a narrative framework, making it more memorable for the audience
May sacrifice objectivity and accuracy in favor of narrative coherence
Understanding the Data Storytelling Process
Data storytelling is communicating the results of your data exploration/analysis process to an audience through a story. Transforming data into stories requires three fundamental steps, as illustrated in Figure 1.3.
Figure 1.3: Transforming data into stories involves three phases: 1) explore and analyze data, 2) create a story, and 3) deliver the story. Data storytelling involves only making and delivering the story, although you might need to return to data exploration and analysis many times before finalizing the story
Let's break down the data storytelling process. First, you must explore and analyze data. I assume you already know how to do this and are an expert. I also assume you already know how to extract insights from your data, calculate statistics, etc. In addition, I suppose you know how to do it so well that it doesn't need to be the subject of this book. Second, you must create a story, starting from the data. Creating a story means precisely what it sounds like: creating a story with a plot and characters. But here, the situation is a little different. It's not about inventing a story, like a tale, but creating one based on facts, evidence, and data. There is a parallel thread to this, which is creative nonfiction, that aims to tell the stories of the people behind the facts (Gutkind, 2007). The term creative nonfiction might seem strange or even contradictory. In reality, according to its inventor, Lee Gutkind, creative nonfiction aims to capture and describe a subject so that even the most resistant reader is interested in learning more about it. It's not about making up stories based on facts but about presenting the facts in a more dramatic way, just like you do in creative writing. What is required of the creative nonfiction writer is a passion for the written word, a passion for research and observation to understand how things work in this world, and a passion for understanding their audience in order to tell the best story possible. Here, your job as a data storyteller is precisely this: to tell the stories of the people behind the data.
Returning to Figure 1.3, the third step is to deliver the story. Keep the making and delivery phases separated if you plan to tell the story several times to different audience types. Otherwise, transform the two into a single phase in which you think about the audience directly from the beginning. In this book, we consider the two phases separately to allow the same story to be told to multiple audiences. This is where contact with the audience takes place. Delivering the story doesn't necessarily mean going on stage and giving a brilliant presentation. There are also other forms of delivery, such as prerecorded videos, written documents, presentations, discussions, etc. In any case, the delivery can take place synchronously or asynchronously. Synchronous delivery requires that the storyteller and the audience are present simultaneously during the delivery. This simultaneity does not occur in the asynchronous delivery. You might think that synchronous delivery has greater audience engagement, but this is actually not true. Returning once again to Figure 1.3, you can see how data storytelling doesn't include the data exploration phase but only the creation of the story, beginning from the data and its delivery. However, this doesn't mean that the story creation process is linear, that once you have explored your data, it is set in stone. The process is iterative and dynamic in the sense that if, as you build the story, you realize that you need more data or more details, you can always go back to the exploration and analysis phase and gather what you need.
So remember: Explore, Make, Deliver. That is the secret to turning your data into stories. Now, let's explore in greater detail what making and delivering a story means, by beginning with making a story.
Making a Data-Driven Story
To better understand what making a data-driven story means, let's borrow the terminology of the field of cinematography. Not that you are creating films here; you won't invent stories, for goodness’ sake, but you can think that the process of creating a story is similar in the two cases, in data storytelling and cinema. Why can this simile work? Because they both rest on visual elements. In the case of cinema, the visual elements are the moving scenes imprinted on the film. In contrast, in the case of data storytelling, the visual elements are the graphics and animations that accompany your story and that represent the analyzed data. Filmmaking consists of three fundamental parts (see Figure 1.4): preproduction, production, and postproduction.
Figure 1.4: The three phases of creating a film include 1) preproduction, 2) production, and 3) postproduction. Consider the process of creating a data-driven story like that of creating a film
Preproduction includes the definition of the film script, that is, the choice of theme, subject, and scenes, which involve the plot definition. The choice of theme also includes the message that the story wants to convey. Examples of messages can be general values, morals, and so on. Production concerns the moment in which the film is produced (that is, the cinematographic shooting), and postproduction includes the final editing, where the choice of the order of the scenes, the starts and cuts of the scenes, the soundtrack, etc., are decided upon. Don't worry if everything isn't clear to you now. We will return to this structure throughout the book. For now, it's enough just to know the three phases: preproduction (i.e., definition of the script), production (i.e., film shooting), and postproduction (i.e., editing) (Aimeri, 1998).
Similar to what happens in filmmaking, making a data-driven story includes these three phases (see Figure 1.5).
Figure 1.5: The three phases of creating a data-driven story include 1) preproduction, 2) production, and 3) postproduction
In the preproduction phase, the data storyteller defines the story's script. Starting with the data, the data storyteller extracts the story's theme, subject, and scenes from the data supplied. In the production phase, the data storyteller then writes the text and the accompanying visual elements. In general, the visual elements are graphs that, for example, show the trend of the data over time, the spatial distribution, etc. The text and visual elements represent scenes from the story. In the final postproduction phase, the data storyteller chooses the order in which to narrate the scenes and possibly eliminates some that aren't necessary for the narrative. I'll be diving deeper into how we do this in later chapters. The data storyteller can also decide to manipulate the scenes already produced for a better result. At the end of the postproduction phase, the story is finally ready. Now, it's time to tell someone about it.
Delivering a Data-Driven Story
When you create a data-driven story, you don't typically make it just for the sake of it but with a specific goal: you want to communicate the results of your analysis to your audience. For the audience to understand your story, you need to use their language; otherwise, they won't understand what you mean. Imagine having to speak about how the inclination of the earth's axis determines the duration of the hours of light and darkness during the various periods of the year. You will undoubtedly use a different language if you have to explain it to an audience of 10-year-olds versus an audience of scientists. This means the same story can be told in a thousand different ways, depending on the audience you have in front of you. Then, after you've created the story, you'll need to tailor it to your audience. In my poster example, this question should've come to mind: why not think about the audience right from the start and create a story directly adapted to my audience? Unfortunately, I didn't take into account who I was presenting to before I took the stage; thus, I didn't provide them with intriguing reasons in a language they could understand as to why they should specifically come see my poster. I rambled. Before I presented to them, I needed to understand who my audience was exactly, before I could relate why underwater networks should be specifically important to them. If you consider your audience early on, you create a story tailored only to that audience. If you create a generic story and then adapt it to your audience, you can tell it to different audiences each time, as shown in Figure 1.6.
Figure 1.6: Once produced, a story can be adapted to various audiences, based on the objective to be achieved
Figure 1.6 illustrates three types of audiences: a general audience, an audience of executives, and a technical audience. The general audience doesn't know your data, so to make them understand the story, you need to simplify the text and visual elements as much as possible. Executives are those who know the data in broad terms. They're interested in making decisions based on your data, so you'll need to highlight the critical elements in your story. Finally, the technical audience knows the data in detail. They aim to understand all technical aspects, such as how the data was collected and processed. We’ll look at the various types of audiences in more detail in Chapter 8, “Third Act: Setting the Climax and Next Steps.”
To adapt a story to your audience, you need to use their language. Not only that, but you also need to understand what the audience wants: their desires, their problems, and what they care about most. One way or another, your story has to be of interest to your audience. You can't present a story about how many books a person reads in a year to an audience that isn't interested in reading. The audience must be interested in the change that the hero of the story experiences. If this doesn't occur, you must change the story.
The goal of your story is to communicate the results of your analyses to the audience so that they do something after engaging with your story. This “something” can be essentially anything, like making a decision, supporting one of your initiatives, etc. Therefore, behind every story there is a message defined during the making phase and a purpose being defined during this delivery phase. The end specifies the objective, or the purpose, of the story. Broadly speaking, we can define three purposes: to persuade, inform, and entertain the audience (Kelliher & Slaney, 2012), as summarized in Figure 1.7. Another type will be discussed soon.
Figure 1.7: The three possible purposes of a story: 1) to persuade, 2) to inform, and 3) to entertain
Persuading the audience means you're convincing them of a particular point of view. Informing the audience means you're teaching or giving information. Entertaining means you're maintaining the audience's attention through enjoyment. In reality, there is also a fourth type of purpose, which is to explain, but you can aggregate it in the informing category. Consider again the example of presenting a story about how many books a person reads in a year to an audience that isn't interested in reading. Persuading that audience could involve using the book data to make them see why they should care. Then, if you've made them care, you can use real data to inform them. Finally, through entertainment, you can make the audience enjoy, capturing and holding their attention as you present the information and a relevant call to action.
Summing up what has been discussed thus far, we can say that making a story means defining its message and structuring it with characters and a plot. Delivering a story means defining its purpose and adapting it to a specific audience (see Figure 1.8).
Figure 1.8: Making the story means defining the message. Delivering the story means defining its purpose
At this point, we are ready to analyze a simple case study.
Case Study: Solstices and Equinoxes
I recently read a beautiful example on how a graph can be created, starting from four simple points. In Visual Analytics Fundamentals by Lindy Ryan, the author shares that the four points are the dates of the summer and winter solstices and the dates of the equinoxes of the spring and fall (Ryan, 2023). Figure 1.9 is a simple graphical representation of these points, reworked from one of Dale Ward's lectures (Ward, 2017a), held as part of the Weather, Climate, and Society (ATMO 366) course in 2017 (Ward, 2017b). Ward is a lecturer on hydrology and atmospheric sciences at the University of Arizona.
Figure 1.9: Seasonal (yearly) changes in the length of daylight hours in the Northern Hemisphere. At the equator, there are no seasonal changes. There are always 12 daylight hours throughout the year. At 35° latitude North, the longest day is 14.5 hours (June 21), and the shortest is 9.5 hours (December 21). At 50° latitude, the longest day is 16.5 hours (June 21), and the shortest day is 7.5 hours (December 21). At latitudes of 66.5° and higher, the longest day of the year is 24 hours, and the shortest day has 0 hours of sunlight
The book, however, stops at the simple visualization of data. Intrigued by this simple and attractive example, I considered how such a simple set of points could be transformed into a story. A first approach could be to explain what happens at each point, as described in Figure 1.10.
Figure 1.10: A sequence of four scenes explaining what happens to the Earth's axis at the equinoxes and solstices
I have defined four scenes, each of which describes what happens at each data point, with details relating to the position of the earth's axis with respect to the sun. Are you satisfied with this result? I'm honestly not. They seem like four figures to be included in a high school astronomical geography textbook. What's the deal with this sequence of scenes? The problem is that it's not a story: it doesn't have a hero, it doesn't have a plot, and it doesn't even reach a conclusion. It is simply a way of presenting data, of describing it, but it doesn't tell a story.
So, how can we tell a story with this data? Let's apply what we learned in this chapter. A story must have at least one hero and a plot. The whole story depends on the existence of these two elements. Who could be the hero in this particular case? Let's try to think. The four dates in Figure 1.10 represent the transition from one season to another, so our hero could be the transition from one season to another. Now, let's move on to the plot. To have a story, the hero must experience a change, which is usually caused by a problem. (We will learn more about this in the next chapter.) Let's reflect for a moment. What change does our hero experience? It's here that the search begins. We can look for the answer in the data (if necessary, returning to the exploration phase) or in reliable sources. In our case, I found a very interesting scientific article by Wang et al., which describes how over the years the length of the seasons is varying considerably due to climate change. By 2100 in the Northern Hemisphere of the globe, we will have very short winters and long and very hot summers (Wang et al., 2021). In our story, therefore, we can talk about this change, as shown in Figure 1.11.
Figure 1.11: The sequence shows three scenes: 1) the initial moment in the past, 2) the change in 2011, and 3) the situation after the change in 2100
The story consists of three scenes. The first scene shows the hero before the change, otherwise known as the initial moment in the past; the second scene shows the change, or the reduction of the length of winter in 2011 due to climate change; and the final scene shows the hero after the change.
