As data scientists, we look for stories within data. We use math, statistics, programming, and learning algorithms to uncover these stories. We love to discuss our explorations into data with those who will listen, but because of the esoteric nature of our work, our discoveries may not be widely heard or understood. To engage an audience, we should be great at visualizing and telling these stories.
Some stories can be thought of as a reporting of past events. At times, our goal is to dive deeply into a previously collected, complete, and static dataset and find relevant information within it. For this type of research, we can hope to find many stories in the data—static stories that can be definitively told—and we tell these stories with excitement and interaction by weaving a narrative into our storytelling. With precise conclusions in mind, we can use the best means to interest and engage our audience to focus our process. This recent New York Times example of static storytelling blends together photos, video, and interactions using the web as its vehicle to engage the audience and convey conclusions reached by the scientists.
But not all data is static. Sometimes, our datasets are continually expanding. With this continuous expansion of data comes stories that are ever-changing and evolving. These dynamic stories cannot be told using the same strategy as static stories. We aren’t able to use precisely defined conclusions to focus our storytelling. Often, we have automated jobs constantly uncovering important events and information within these datasets.
We can create dashboards displaying all of the stories we uncover to enable real-time decision making. Think of dashboards that display trends or alerts that are uncovered through the jobs of hard working data scientists. The focus of these dashboards is to display the stories constantly uncovered by our jobs. Sometimes, however, stories aren’t the primary focus of the end product. For instance, consider Netflix, Pandora, and Amazon, products that execute jobs that constantly refine the story of what a user likes or dislikes. This story is used to improve upon an audiences’ interaction with a product.
Still, the way the story is told is vital in its role and success. These stories become embedded into the product, allowing the user to make better real time decisions. Dynamic datasets force us to tell a story in a dynamic and explorative way that may be subtly or explicitly incorporated into a product. We must allow the audience to search through different stories in different states while feeling engaged and at ease.
Tap into your inner artist or work closely with data savvy artists to make your storytelling appealing. Here are some steps to bring your stories to life.
First, envision an exciting way to interact with your output. Use your creativity and imagination to think up fun and exciting displays for your data. Don’t confine yourself to images, but work with fluid displays on a dynamic medium. The web is an excellent storytelling vehicle as the browser allows for movement, videos, and interaction. Browse the Internet to look at other storytelling interfaces and incorporate some of those ideas into your own.
As you come up with ideas, it is crucial to sketch them out on paper to quickly uncover what works and what doesn’t. Think storyboard layouts, interactions, and flows. This will allow for early feedback and collaboration. After coming up with your storyboards, determine a color scheme. Colors are important to setting a tone. There are many websites that suggest color schemes.
As you continue to evolve your storytelling experience, listen to feedback. What may make sense to you, may not to others. Watch users use your tool, or install analytic software on your site to understand how users are interacting with your story. Continue to improve upon the user experience by adding more features that make your story more interactive and appealing.
—Written by Jason Farbman