Over the years, I’ve seen people debate whether data storytelling is more art or more science. If it is indeed more science than art, then there’s increased potential that it can be standardized or even automated. If the opposite is true, then maybe only creative people with visual design skills will be able to craft meaningful data stories.
Rather than arguing for a side in this debate, I’d like to step back and examine what we mean by the words—art and science. Art conveys something as being intuitive, creative, and subjective. Science suggests something is analytical, precise, and objective. While science emphasizes the acquisition of knowledge, art focuses on the expression of knowledge. Despite these stark differences, data storytelling combines both art and science.
Before people were forced to choose between these two disciplines for their careers, innovators such as Leonardo Da Vinci combined both art and science throughout their work. He was a sculptor and painter but also an engineer and architect. From painting the Mona Lisa to conceptualizing flying machines, Da Vinci’s diverse projects showed art and science can coexist and be complementary.
With data storytelling, you must similarly draw on both areas to communicate insights effectively. Let’s examine how each side plays an integral role in crafting and telling stories with data within a business context.
When it comes to the art-related aspects of data stories, almost all the following elements shape how you express your key observations and insights. Rather than working from theories or evidence, these art-based elements demand perceptiveness, judgment, and finesse.
Business acumen. If you want your data story to be meaningful, it must be aligned with the business strategy. Even if you uncovered a fascinating insight, it will be viewed as worthless by your audience if it isn’t related to their business goals or challenges. Discovering what’s most important to your audience is essential to creating relevant and useful data stories. While you would think business goals and strategic priorities would be clearly articulated, they often aren’t, which makes achieving and maintaining strategic alignment so challenging.
Audience empathy. One significant challenge for data storytellers is to empathize with their audiences. Frequently, the Curse of Knowledge can make it difficult to view your data from a less-knowledgeable perspective. If you want your data story to connect with your audience, you must adapt how you communicate your data so that it can be easily interpreted and understood by others. Shifting the focus from trying to be understood to helping others understand is crucial, especially when audiences can vary in background, knowledge, and expertise.
Narrative structure. Stories follow a specific structure that connects a series of events together. Turning your analysis findings into a coherent data story requires a preceptive sense of which details are important to the story, how much context is required, and what information will resonate the most with the audience. While a framework or model can guide this process, there’s still some artistic license in how each data story comes together.
Visual design. Frequently, there is more than one way to visualize data. Knowing which visual approach will work best for a particular dataset, audience, and intended purpose will be important to communicating your key insights effectively. A clean design ensures your key points are conveyed in a professional manner that builds credibility and trust. In addition, creative touches can make your content more memorable and persuasive for the audience.
Messaging. The written and spoken words that accompany and explain the data charts within your story also need to be carefully crafted. Sometimes, how you say something can be just as important as what you say. It is important to find the right balance between sharing too much and too little. An adept combination of visuals and narrative will inform what your audience takes away from your data stories.
In the pursuit of knowledge, you investigate or examine the data to unlock new insights. Every data story is the product of research and analysis. How you approach telling stories with data is also influenced by research studies from a variety of scientific fields.
Analysis. Before you have an insight to share, you explore a dataset for anomalies, patterns, and trends. This exploratory groundwork leads to key observations and insights that become the building blocks of a data story. In the process, you may also leverage the scientific method to run experiments and test hypotheses that contribute to the discovery of the insights that serve as the foundation of your data story.
Psychology. This social science field offers several useful theories and research, especially in the area of visual perception. How you choose to visualize your data will be guided by Gestalt Theory, pre-attentive processing, and other human perception frameworks. These models help you design visuals that are more compatible with how the human brain processes information. In addition, psychological models such as Cognitive Load Theory can also inform how you craft complex data stories so they are not mentally taxing for your audiences.
Behavioral economics. The main purpose of data stories is to inform business decisions. Understanding how people make decisions matters to data storytelling, especially when emotion plays a significant role in the decision-making process. Behavioral economics’ theories help explain the heuristics and cognitive biases that can influence how your audiences interpret the numbers, respond to the narrative, and make decisions based on the data.
Depending on your background and role, one of these two sides of data storytelling may come more naturally to you. If you work in analytics or data science, you may already possess strong analytical or data visualization skills—you’ll be comfortable with science aspects of data storytelling. On the other hand, if you’re on the business side such as in the marketing or human resources department, your domain expertise and communication skills will make the art side feel more familiar and intuitive.
Unlike other data-related work, with data storytelling, it’s essential to develop a well-rounded, balanced approach. If you are significantly deficient on either side—art or science—your success as a data storyteller will be limited. As computer scientist Ben Shneiderman noted, “Leonardo Da Vinci combined art and science, and aesthetics and engineering; that kind of unity is needed once again.” The same unity between art and science is needed with data storytelling.
I’m not arguing all data storytellers must be multidisciplinary geniuses like Da Vinci. However, to tell data stories effectively, you’ll need to draw on both disciplines as he did. The first step is to recognize both sides are integral to your success and then evaluate where you may have potential gaps in your current approach. Even if you don’t consider yourself to be a scientist or an artist today that doesn’t mean you can’t begin driving action and positive change with data stories. Taking a holistic, balanced approach will go a long way toward putting you on the right path as you begin your data storytelling journey.
Effective Data Storytelling teaches you how to communicate insights that influence decisions, inspire action, and drive change.