One of the most common clichés in the viz space is that “data visualizations are only as effective as the insights they reveal.” In this context, effectiveness is a function of careful planning. Any meaningful visualization is a two-pronged one. It requires analytical perfection and correct rendering of statistical information, as well as a well-orchestrated balance of visual design cues (color, shape, size, and so on) to encode that data with meaning. The two are not mutually exclusive.
Data visualization is a place where science meets art, although the jury is still out on whether the practice is more a scientific endeavor or an artistic one. Although experts agree that a com- pelling visual requires both, it tends to be something of a chicken and egg scenario. We haven’t quite come to a consensus as to whether science comes before design or we design for the science—and the decision changes depending on whom you ask, who is creating the visualization, and who its audience is. That said, whichever side of the argument you land on, the result is the same. We need statistical understanding of the data, its context, and how to measure it; otherwise, we run the risk of faulty analysis and skewed decision making that, eventually, leads to risk. Likewise, our very-visual cognition system demands a way to encode numbers with meaning, and so we rely on colors and shapes to help automate these processes for us. An effective visual must strike the right balance of both to accurately and astutely deliver on its goal: intuitive insight at a glance.
This might sound like an easy task, but it’s not. Learning to properly construct correct and effective data visualization isn’t something you can accomplish overnight. It takes as much time to master this craft as it does any other, as well as a certain dedication to patience, practice, and keeping abreast of changes in software. In addition, like so many other things in data science, data visualization and storytelling tend to evolve over time, so an inherent need exists for continuous learning and adaptation, too. The lessons in this book will guide you as you begin your first adventures in data storytelling using data visualizations in Tableau.
Visual Data Storytelling
With all the current focus on data visualization as the best (and sometimes only) way to see and understand today’s biggest and most diverse data, it’s easy to think of the practice as a relatively new way of representing data and other statistical information. In reality, the practice of graphing information—and communicating visually—reaches back all the way to some of our earliest prehistoric cave drawings where we charted minutiae of early human life, through initial mapmaking, and into more modern advances in graphic design and statistical graphics.
Along the way, the practice of data visualization has been aided by both advancements in visual design and cognitive science as well as technology and business intelligence, and these have given rise to the advancements that have led to our current state of data visualization.
In today’s data-driven business environment, an emerging new approach to storytelling attempts to combine data with graphics and tell the world’s stories through the power of information visualization. For as far back as we can trace the roots of data visualization, storytelling stretches further. Storytelling has been dubbed the world’s oldest profession. Likewise, it is now and has always been an integral part of the human experience. There’s even evidence of the cognitive effects of storytelling in our neurology. It’s a central way that we learn, remember, and communicate information—which has important implications when the goal of a visualization or visual data story is to prepare business decision makers to leave a data presentation with a story in their head that helps them both remember your message and take action on it. We’ll discuss the cognitive and anthropological effects of stories more in later chapters.
Graphing Stories
Graphing stories is the intersection of data visualization and storytelling. American author Kurt Vonnegut is quoted as having famously said, “There is no reason that the simple shapes of stories can’t be fed into a computer—they have beautiful shapes.” Likewise, we could restate this to say that data stories provide the shapes to communicate information in ways that facts and figures alone can’t. Just as much as today’s approach to data visualization has changed the way we see and understand our data, data storytelling has equally—if not more—been the catalyst that has radically changed the way we talk about our data.
Learning to present insights and deliver the results of analysis in visual form involves working with data, employing analytical approaches, choosing the most appropriate visualization techniques, applying visual design principles, and structuring a compelling data narrative. Also, although crafting an effective and compelling visual data story is, like traditional storytelling, a uniquely human experience, tools and software exist that can help. Referring back to Vonnegut’s quote, stories have shapes. In visual data storytelling, we find the shape of the story through exploration of the data, conduct analysis to discover the sequence of the data points, and use annotations to layer knowledge to tell a story.
This material is excerpted and adapted from Visual Data Storytelling with Tableau by Lindy Ryan and is reprinted with the permission of Pearson (c) 2018. For more information, visit informit.com/Tableau.