PREFACE
For as long as I can remember, I have always been fascinated by the power of a good story (see Figure P.1).
Figure P.1 A young me, reviewing the available data on weekly sales.
As a young girl I was an insatiable reader. I read it all, from fanciful children’s classics, to the weekly Sunday newspaper, to novels way beyond my budding adolescent intellectual bandwidth. In those early days I approached a new book as one might approach a new relationship: if I could get my hands on one and it could hold my attention beyond the first few sentences, then I would read it from cover to cover and let it tell me whatever story it wished. It was a win-win scenario.
I read voraciously, inhaling stories that entertained, inspired, and educated me. I read without discretion, never worrying over parameters of genre, or length, or subject matter—those pesky ingredients that blend together inside the lines of a great piece of writing. I moved through stories with the same speed and determination as might a starving person (or perhaps a late-night cruise line passenger) move through a buffet of delicious foods: greedily and indulgently. I devoured stories. Along the way, I discovered those I liked and those I didn’t. More importantly, I discovered story frameworks and storytelling voices that called to me, and met those special few authors whose powerful prose I would approach for the first time, fall immediately in love, and continue to read their books over and over again until their pages became soft and their spines so bent they required surgical repair with Scotch Tape. It might not surprise you that I began my collegiate career with an eye on a major in literature.
Like taste buds, my taste for stories evolved over time, both in terms of format and content. I moved from flea market paperbacks, to glossy novels, and have now managed to go almost entirely digital in the way I seek out and consume informational storytelling, preferring online articles and satellite news radio over newsprint and cable TV journalism (although, to be honest, if you find yourself tagging along in my company at a rare bookstore, it would be advisable to come equipped with an overnight bag and at least one prepackaged meal, just to be safe—a few people have learned this lesson the hard way). Likewise, my appetite for stories that entertain and inspire have become more complex and multi-faceted; my desire for stories that educate require them to be more dynamic, data-rich, and interactive.
My personal evolution in story preferences parallels a similar journey that we, as a collective, take as our taste for stories mature. Our storytelling appetite changes alongside age, experiences, and interests, yet still the desire for a good story persists.
So it is with data stories. As we learn to communicate the results of our analysis—the hidden secrets carefully plucked from within the rows and columns of our data and curated into insight—we mature from being okay with being told something, to wanting to see it for ourselves, and, eventually, to wanting to interact with it. We need more—more information, more context, more action, and more substance. It’s not “see Jane run” anymore, it’s “let’s talk about how fast Jane ran from Point A to Point B and, more important, why (and if) that’s actually important.”
And sometimes, we learn something entirely new and unexpected. Maybe Jane was being chased by a bear.
We crave stories; it’s part of our design. As a species, humans are intrinsically hungry for a good story. Without getting into the weeds, we can leave it simply at this: We need stories. They entertain us, they educate us, and they provide ways to transmit knowledge, information, and experiences. They don’t even have to be in written form. In fact, we’re rather non-discriminatory about how we receive stories—in fact, we might even prefer stories that move us and touch our senses (there is certainly enough scientific evidence to support this, as discussed in a later chapter). Think about movies, radio, Broadway performances, or screening your favorite film accompanied with a live one hundred-piece orchestra contributing the soundtrack.
Today, the concept of data storytelling is reshaping stories of all forms, from news media and journalism, to boardroom reports and conference presentations, to even how we script documentaries and educational programming. If you have seen any conference presentations over the past couple of years, or watched a TED talk, or read an article out of the New York Times, or listened to National Public Radio, or watched an episode of Cosmos, then you have witnessed data storytelling in action. It’s a trend that we—from the classroom to the boardroom—are jumping on with more velocity than you might imagine. Students from grade school to graduate school are working hands-on with data and changing the way they learn about and communicate about information. Business analysts, managers, and executives are moving away from static, statistic-laden reports and toward interactive, visual data dashboards. Journalists and news editors the world over are using data storyboards and engaging, often interactive, infographics to share information with society-at-large—in print, online, and across form factors.
Visualizing our data helps us to see trends and patterns. Telling stories about our data helps us to connect with it. Combined, visual data storytelling pushes beyond the boundaries of simply analyzing information to providing the capacity to communicate it in ways that leave a meaningful, lasting impact. Together, these converge into what I’ve termed the “visual imperative,” a paradigm shift that is radically reshaping how we work with and seek to understand our data, big and small. This visual imperative is reforming our expectations of information, changing the question from “what can we do with our data” to “what can our data do for us.” And, it’s making its mark on every aspect of a progressively data-driven culture, too. From traditional business intelligence and data discovery, to the personal analytics on our smart devices and wearable technologies, to the manner in which we are using data recipes to cook up our new favorite television shows (I’m looking at you, Netflix), we are becoming more data-dependent and data-driven than ever before. Data has changed from being gold, to being the oil of the century, to what one witty columnist decided to combine and dub the “black gold” (a.k.a., oil) of the century, to being the oxygen of the most innovative and disruptive companies.
In a nutshell, this was the premise of my first book, The Visual Imperative: Creating a Visual Culture of Data Discovery, which arrived in bookstores in 2016. Written primarily from an analyst’s perspective, this book focuses on how the data industry-at-large has been undergoing a beautiful period of disruption, and how this innovation and transformation has affected society across the blurred boundaries of customer to consumer. It discusses how the emergent visual revolution of the last several years has changed the language of data discovery across industries; how this has affected modern data architectures, governance expectations, and practices; and explores several of the advancements in data visualization that I expect to see over the upcoming years.
In my years as an analyst, the use of visuals to convey incredible insights into data captured my interest immediately. This led me to focus my research on data visualization and visual analytics, and, eventually, on visual data storytelling. I learned how critical data visualization truly is to communicating the results of data analysis and discovery to enrich the intelligence that is provided to decision makers and leaders within any organization and how a compelling visual narrative can transform insight into action. Then, as I transitioned from industry into academia, I recognized firsthand the importance of careful planning and foresight in communicating data, as narratives begin to take form and we build visualizations to support story arcs. More specifically, as I watched education translate into application, I recognized this isn’t an intuitive and obvious process. It takes work, it takes understanding, and it takes a lot of practice.
If my previous book was the use case to support the visual imperative, then this book is the how-to guide to put it into action with visual data storytelling. For lack of a better phrase, I’m done pontificating: it’s time to get our hands dirty.
Three Core Takeaways
This book focuses on giving you the foundational knowledge, contextualized learning, and hands-on skills you need to be successful in leveraging the power of data visualization to tell compelling data stories. The goal is not to inundate you with academic lessons in the science of data visualization or story composition, nor to provide a full-scale training experience on any particular software or technical application, but instead to provide guidance as you build the necessary skills to produce visual data narratives within the context of business communication. Therefore, this book concentrates on helping you learn how to organize your data and structure analysis with stories in mind; to embrace exploration and the visual discovery process; and to articulate your findings with rich data, purposefully curated visualizations, and skillfully crafted narrative frameworks. Ultimately, these presentations can help you to deliver your business message while satisfying the needs of your audience. By the time you’ve reached the end of the text the expectation is that you will have earned the three core takeaways represented in Figure P.2.
Figure P.2 The core takeaways.
What You’ll Learn in This Book
In data visualization courses, students learn how to present data in visual form. This involves working with data, learning how to apply visual design principles, and—often—learn new software. This book is intended to tackle all three. What you will learn from this book includes the following:
Why we tell stories and what visual data storytelling is
The difference between data visualization for analysis versus presentation
How to tell visual stories that communicate insights and make an impact
How to leverage questions to design logical and fruitful data collection and analysis
How to create important graphs in Tableau and know which chart to use
How to utilize concepts of design in data visualization and storytelling
How to best storyboard your story for your message based on your audience
How to direct your audience's attention to the most important parts of your data story
How to design effective business presentations to showcase your data story with Tableau
Who This Book Is For
This book is for anyone who has data and wants to use it to communicate something to someone else in an engaging and memorable way. This includes, but is not limited to:
Analysts sharing the results of their data discovery or analysis
Students communicating data for reports or presentations
Teachers helping learners (of any age) to cultivate visual data literacy
Executives and business managers reporting data-driven results or metrics
Journalists giving data the starring role in their editorials
Essentially, if you have some data and want to tell a story about it to someone else, then this book is for you. So, from the most savvy of data users to students just beginning to learn about the power of data visualization in business communications, as long as you are interested in becoming a better visual data storyteller, then you’re in the right place!
There are no prerequisites for this book. In fact, it might actually be preferable if you’re coming into this text with fresh eyes and a fresh perspective. We start with the basics and build incrementally on concepts and to move through the data storytelling process from beginning to end.
Don’t let the topic overwhelm you: Whether or not you have been practicing data visualization and visual analysis for some time or are just beginning, you don’t have to be a statistician or a computer scientist, a graphic designer, or even a well-trained writer to learn how to navigate the art and science that is data visualization or to become a master data storyteller. Likewise, you do not need to be a data visualization expert or come armed with deep technical expertise in visualization software packages. Although this book utilizes Tableau as the primary mechanism for data visualization, you need not be a power user or expert prior to getting started—you don’t even have to have a purchased license on your machine! You can simply download a free trial of Tableau Desktop version 10 or higher to get started—and we’ll go through that process together, too. Finally, you don’t even need to bring your own data to play just yet (although you certainly can). The sample datasets used in this text, as well as information on where you can find other free datasets, are available to you through the resources listed at the end of this book. Tableau also provides a large selection of sample datasets that you can work for practice, too.
I realize that the idea of visual analysis or data storytelling might sound intimidating to many and that learning new software is always a challenge. Therefore, this book is designed in a way that a professor or a tutor might teach visual data storytelling (and in fact is the approach I take in my classrooms for undergraduate- and graduate-level students) using what I call the 1-2-3 Method (see Figure P.3). This breaks down like this: (1) grounding easy-to-understand principles (2) reinforcing these through real-world examples, and (3) guided hands-on work to incrementally develop skills. By the time you have worked your way through each of the chapters and exercises in this book you will walk away with something tangible: competency as a visual data storyteller using your own data in your own dashboards and presentations. And you’ll have some great visuals on your Tableau Public profile that you can add to your resume!
Figure P.3 The 1-2-3 Method.
Assumptions
To write a comprehensive text on any of the topics covered in this book would take several volumes and the combined mind power of multiple subject matter experts and scholars across industry and academia. Thus, this book makes some assumptions about the skillset, expectations, and needs of its audience to limit the scope to the most valuable content. My goal is to distill complex topics to the most effective level of detail necessary to help you learn how to communicate business-relevant implications of data analyses using the visualization and storytelling capabilities of Tableau. Additional learning resources to expand on the concepts covered in these chapters are provided at the end of this book.
First, this book is focused primarily on storytelling and presentation. While analysis is an intrinsic part of this, in this book you work from a perspective of communicating insights rather than statistical analysis—data explanation rather than exploration, if you will. Thus, the way you curate visualizations for storytelling purposes may be slightly different than how you would approach these tasks if you were designing analytically accurate data representation not intended for presentation. Additionally, while I will touch on subjects like data preparation and wrangling tasks associated with getting data ready for analysis, the full scope of what is involved in all the steps necessary to transform raw data into a workable format is beyond what is covered in this book. Instead, I provide some basic information on these tasks and lean on the capabilities of Tableau as well as other available software, such as Alteryx and Excel.
Last, but not least, this book assumes that the reader has access to Tableau Desktop 10, which is currently available to install on either Windows or Mac operating systems Free trials are available for business users or general audiences, while students and educators can take advantage of the Tableau for Teaching program, which offers free licenses to the full desktop version, as well as the Alteryx for Good program, which is similar.
note
Here are a few caveats and notes:
Versions of Tableau prior to version 10 support much of the same functionality as version 10. However, the interface is notably different and may affect navigability of instructions in this text.
Alteryx runs solely on Windows; Mac users will need to install Parallels or a similar program (most offer free trials) to follow along.
All sample data files are provided in Excel-ready formats; SPSS files may also be available.
Data not available for public use or sharing will be noted as such.
Why Tableau
If you browse the shelves of your local bookstore, you’ll find several wonderful books available that teach data visualization and data storytelling skills in a tool-agnostic manner. (I’ve included some of these titles in the recommended reading resources in Chapter 10.) There is a good reason for this. To borrow the aptly stated words of Cole Nussbaumer Knaflic, author of Storytelling with Data: A Data Visualization Guide for Business Professionals, “No matter how good the tool, it will never know your data or its story like you do.” With any software there will always be weak points to balance out the strong ones. However, my goal in this book is to not only give you the information you need but the application to use it. For that, we need a tool.
Many software packages are available on the market that would serve as capable platforms to support this book, including Excel, which is still the most ubiquitous, if unexciting, analysis tool, with the capability to functionally create basic charts and graphs. However, although many of the more advanced available technologies meet the rigors of building beautiful data visualizations, few provide the end-to-end capabilities that Tableau does. What we’re looking for is a best-of-breed tool that delivers an approachable, intuitive environment for self-service users of all levels to prepare, analyze, and visualize data, as well as delivery platforms like dashboards and story preparation. All of these are native to Tableau.
Today, Tableau is the world's leading data visualization analytics software company with 57,000 customer accounts ranging from small to large organizations across all industries and 160 countries (as of Q1 20171). Tableau provides a suite of licensed and free software products—including a recently released mobile product called Vizable—and excels at displaying data visually, using a drag-and-drop canvas on top of embedded analytics to help users explore their data. Although Tableau can mimic Excel by providing the capability to analyze rows and columns of numbers, its focus is on interactive, visual data exploration through complex analytical capabilities as well as dashboarding and storytelling features not found in other tools. For more advanced users, Tableau supports a complete formula language and robust data connections: Tableau's live query engine enables users to connect to more than forty difference data sources; its in-memory data engine leverages the complete memory hierarchy from disk to L1 cache and shifts the curve between big data and fast analysis. And according to Tableau’s aggressive product roadmap, the fine-tuning of some of these capabilities as well as several worthwhile new features are on their way.
Another aspect of Tableau that I find impressive and worthwhile to a larger data storytelling movement is its focus on building a community of data users. I encourage you to join the Tableau Community and connect with the resources and peers you will find there.
How to Use This Text
Like traditional classroom instruction, this book’s chapters are organized as individual modules that will be your guide as you learn how to go beyond the dashboard to communicate business-relevant implications of data analyses using the analytic, visualization, and storytelling capabilities of Tableau. Some chapters provide complete coverage of a story, from data collection to a completed storyboard. Others are organized as granular exploration of a single concept.
Although you are not limited to working through this book cover to cover, it is recommended that you do so for incremental development of learning and reinforcement of skills. Each module builds on concepts and skills discussed in the preceding one, and may include advancements on working through an end-to-end data project that are necessary before taking the next steps forward.
note
With the exception of a very few, all visualizations and screenshots in this book are created using Tableau version 10 for Mac. Differences in operating system versions are negligible.
Supporting Materials
Beyond the modules of this text, I have developed several companion materials to support ongoing skills development and learning. These are intended to go beyond the confines of these chapters and to attempt to keep pace with innovations in Tableau functionality as well as review some of its more nuanced advanced features that are out of scope for this book. These resources are suitable for the workplace, although special attention has been given to classroom use:
Companion website (www.visualdatastorytelling.com): Includes presentation materials and an ongoing blog, as well as a library of visual data stories contributed by storytellers from industry to academia
Educator portal: Provides lecture materials as well as in-class assignments and rubrics for individual and group projects, as well exams that support student learning at the undergraduate and graduate levels
Recommended reading: A list of additional reading, organized by topic
Glossary: Provides a list of terminology and definitions used throughout this text
Contents of This Book
Each chapter in this book is intended to achieve two purposes:
Provide a foundation of knowledge that forms the basis for education on the concepts we’ll be covering. These are the chapters grounded in best practices and empirical evidence.
Give you the opportunity to get hands on with the data in Tableau and begin to develop these skills for yourself. These chapters provide step-by-step instruction with accompanying screenshots and examples of outputs to verify your work.
The following list provides a brief description of each of the chapters.
Chapter 1: Storytelling in a Digital Era
This chapter sets the context for the book with an introductory discussion on data visualization and visual data storytelling, how these two concepts are similar and different, and how both practices have transformed in the digital era, propelled by new technologies and more data. The chapter explores the value of visual data storytelling for data communication and establishes how data storytelling is the necessary skill that bridges the business-IT gap.
Chapter 2: The Power of Visual Data Stories
This chapter uses real-life and quintessential examples to analyze the power of visual data stories to communicate discoveries and insights hidden in data. You ground these lessons by taking time to understand what makes visualization and stories so powerful to the human brain from both a cognitive and an anthropological perspective by comparing the brain on data versus the brain on stories.
Chapter 3: Getting Started with Tableau
This chapter shifts focus to begin exploring the Tableau ecosystem. It reviews the different software products and provides a detailed tour of the new and improved Tableau user interface. Finally, you will walk through the Getting Started process to begin working hands-on in Tableau and review what users need to know about bringing data into Tableau to prepare for the analytic and visual process.
Chapter 4: Importance of Context in Storytelling
This chapter describes the importance of understanding data’s context and its role in helping data storytellers ask the right questions to build a story framework. It discusses exploratory and explanatory analysis; gathering stakeholder requirements; and strategies for successful storytelling, including repetition, narrative flow, considerations for spoken versus written narratives that support visuals, and structures that can support your stories for maximum impact. You will also explore helpful techniques in Tableau that guide you to crafting effective data narrative structure.
Chapter 5: Choosing the Right Visual
This chapter introduces the common types of visuals used to communicate data in a business setting, discusses appropriate use cases for each, and highlights their use through examples built from the catalog of charts available in Tableau. The chapter also covers techniques to help you assess when to use these graphs, when to avoid certain types of charts, and how to generate them according to best practices, along with some of the special features in Tableau designed to help you get the most from your visual.
Chapter 6: Curating Visuals for Your Audience
This chapter dives into human cognition and visual perception to frame the contribution of pre-attentive attributes like size, color, and position and how important they are to the storytelling process. You will explore how to use these strategically to help direct audiences’ attention and create a visual hierarchy of components to communicate effectively. This chapter provides the framework for curating story arcs and layouts with visualizations in Tableau that the following chapters explore in-depth.
Chapter 7: Preparing Data for Storytelling
This chapter covers the very beginning of the data storytelling process with the processing steps necessary to ready messy data for visual analysis and storytelling in Tableau. You will leverage the lessons learned thus far in the text by working through preparing data for analysis, connecting to data, and beginning to visually explore it in Tableau. The chapter walks you through this process beginning with exporting raw data from survey platforms, through manually preparing it in Excel, to using Tableau 10 and other external tools to get data just where it needs to be so you can start building a compelling visual data story.
Chapter 8: Storyboarding Frame by Frame
This chapter picks up where the previous chapter left off and looks deeper at the process of constructing a data narrative by covering how to build purposeful visualizations and organizing them to tell a story. It offers a closer look at building a sequence of data visualizations, reviews how to build dashboards and organize them as well as individual visualizations in story points, incorporating features like annotations and highlighting, and covers how to revisit earlier discussions on context to present a compelling visual data story.
Chapter 9: Advanced Storytelling Charts
This penultimate chapter explores advanced strategies for visual data storytelling beyond the basic charts and graphs provided in Tableau’s core functionality. It covers how to create advanced charts that require additional formatting and calculations, including timelines, Likert scale charts, lollipop charts, and more.
Chapter 10: Closing Thoughts
This chapter recaps the main lessons covered throughout the text. As a resource kit for life beyond the book, this chapter provides checklists of best practices and practical suggestions for continuing to master data storytelling, as well as discusses additional resources available to support the text.
Good luck! Let’s get started.
_____________