Data Storytelling: The Essential Skill for the Future of Analytics

Collecting data and performing analysis doesn't mean much if you can't find a way to effectively convey its meaning to an audience...

Collecting data and performing analysis doesn’t mean much if you can’t find a way to effectively convey its meaning to an audience. Oftentimes, audience members aren’t well-positioned to understand analysis or to critically think about its implications. To engage with an audience, you need to embrace storytelling. Let’s take a look at what that means when talking about storytelling with data.

How to Build a Story Arc

One of the simplest ways to approach the problem is to treat your story as a three-act play. That means your story will have:

  • An introduction
  • A middle
  • A conclusion

Each section of the story needs to be delineated so the audience understands the structure and the promise of a story that comes with it.

What Goes into an Introduction

In most cases, data is hidden before being subjected to analysis. That means you have to set the scene, giving the audience a sense of why the data is hidden and where it came from. You don’t necessarily want to jump right to conclusions about the data or even any basic assumptions. Instead, the data should be depicted as something of a mysterious character being introduced.

If the storytelling medium is entirely visual, then you need to find a way to present the data. The Minard Map is a classic example of how to do this. It uses data to tell the story of the slow destruction of Napoleon’s army during the invasion of Russia. Minard employs a handful of vital statistics to explain what’s going to happen as the story unfolds. These include the:

  • Sizes of the competing armies
  • The geographic proximity of the two forces 
  • Air temperature
  • Rainfall

The audience can familiarize themselves with the data quickly and easily understand what this story is going to entail just by reading the vital statistics. In this particular case, this story is going to be about man versus the elements.

Unfolding the Middle of the Story

Following the presentation of the story should guide the audience toward the conclusion. In the case of the Minard Map, the middle of the story is about a slowly shrinking French army and a slowly growing Russian army that tracks the French. Military engagements occur, and the weather starts to turn. Geographic elements are worked into the graph, too, as the armies cross rivers and march into towns.

Providing the Conclusion

A well-executed data visualization should let the audience get to the conclusion without much prodding. The Minard Map makes its point without beating the audience over the head. By the third act, it’s clear that the conditions have turned and the Russians are now close to matching the French in manpower. As the two armies reach Moscow, it’s clear that what started as a triumphant march has ended as an immense loss.

In its best form, data storytelling shouldn’t feel like a sea of numbers at all. People have seen numerous charts and graphs in their lifetimes, even over the regular course of a single day of business, and that means good-enough visualizations that are focused on presenting numbers tend to become white noise.


Good data storytellers make history. Florence Nightingale’s analysis of casualties during the Crimean War permanently changed the way all forms of medical treatment are provided. Her work is still required reading at many nursing and medical schools more than 150 years later. That’s the goal: to engage the audience so thoroughly that the story and the data long outlast your initial presentation.

Accomplishing that goal requires planning. You can’t just fire up your best data visualization software, import some info from Excel and let the bars and bubbles fly. That’s easy to do because many software packages can deliver solid-looking results in a matter of minutes.

Top-quality data storytelling occurs when the audience is given just enough information to set and understand the scene. Someone scanning the visualizations will then follow the information as it unfolds over time. As the audience approaches the conclusion, they should be left with a strong impression regarding what the data says and what they should learn from it.

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