By Naveen Gattu

Good stories have a profound impact on those hearing them, and the best of them not only serve to entertain, but also to influence. So, when paired with powerful data insights, the potential for informed inspiration and effective decision-making is endless. Whether it’s for consumers, business owners, or simply the curious–visual data stories make complex data much more accessible.

The power of data is never more clear than when its insights are visualized effectively. It was hard not to notice the 2018 Information is Beautiful showcase, which showcased a number of brilliant data visualizations from visual data storytellers across the globe.

But, can anyone and everyone do visual data storytelling? Maybe, if that someone sincerely follows best practices and visual storytelling principles. Let’s take a look into four techniques that can help you become a great visual storyteller. 

How to communicate visual data stories

When it comes to visual data storytelling, there are four ways of communicating it:

  1. Expose the data
  2. Show what’s happening with the data
  3. Explanatory storytelling
  4. Self-exploration of data for insights

Let’s explore each technique with an example.

  1. Expose the Data: This is simple. You can, without any hesitation, just expose the data directly to the consumer. Then, it’s totally up to the consumer to infer whatever insights they can from the data.

For example, Google is one of the biggest databases in the world. When we type a query in the search box, it shows us multiple keyword suggestions. What if all of that data could be exposed as an interface? This makes the task of the consumer easy. The consumer need not go through such hidden data on a daily basis. With such an exposed interface, one click is enough to know what people are repeatedly searching for in different countries.

  1. Show what’s happening with the data: Move a step ahead from the first technique to create narratives out of the data. Visually showcase the data using charts and graphs. This makes it a little easier for the consumer to digest.

For example, the consumer needs to know the day(s) of any month that has a high or low rate of births.

Let’s analyze the 15 years of the USA birth data from 1975 – 1990. The visualization explains the rest.

When the same analysis was done with India’s birth data, the pattern turned out to be different. In India, a surprising insight tells that many children are born on dates that are multiples of 5 (e:g; 5, 10, 15,20, 25…).

  1. Explanatory Storytelling: This means literally garnishing the insights for the consumer on top of the analyzed data. It makes the consumer infer the data from the point of view of the analyst or storyteller. You can use annotations or quotes to allow the data to show its value.

Let’s use Game of Thrones as an example. Which character do you think has the maximum screen presence throughout the series? Or which house, combined, appeared on-screen most frequently? You can easily answer these questions with this Game of Thrones visualization, complete with annotated worthy insights for easy consumption of complex data. By adopting this technique, you explain your story to a consumer and allow them to easily find what they’re looking for.

Another example is this visualization of a decade of budget day data to showcase how different sectors of the market react to it. Surprisingly, the tobacco sector was the only industry that was not noticeably influenced by the budget announcement. It virtually always grew exponentially, apart from during a couple of years. By providing annotations and labels for each part of the visualization, the consumer is immersed in the data while understanding each part of it.

  1. Self-exploration of the data: You can create an interface for the consumer that allows them to explore the data themselves, meaning they are able to seek out independently the answers that they’re looking for. It might take an effort on the consumer’s side, but it is as rich as an explanation could be.

By allowing the consumer to interact with autonomously with data visualization, they can create their own story. For example, an air cargo company wants to understand the reasons for its cargo delays. An interactive interface that includes all the necessary parameters such as days, shifts, and product categories makes the task easier for a consumer. They can play with it and change parameters to discover the exact insights they’re searching for.

Telling visual data stories to convey insights has no bounds when done with the consumer in mind. With these four techniques, you can not only equip your desired audience with the ability to comprehend complex data, but you can also captivate them and create lasting memories by using it to tell a story.

Naveen Gattu, COO of Gramener, a design-led data science company.

Visual storytelling stock photo by Artur Szczybylo/Shutterstock