Skip to main content

Seven Tricks for Better Data Storytelling: Part II

In a recent episode of DataFramed, Andy Cotgrave, technical evangelist at Tableau, shared the importance of data storytelling in driving change with analytics. In this two-part blog post, we deep dive into seven concrete tips that Andy provided on telling better data stories. Read Part I if you haven't already!
Jan 2022  · 4 min read

4. Use a narrative structure in data stories.

Stories generally follow a common arc – the protagonist faces a challenge, goes on to resolve it, then returns to normalcy. An example of this arc is the Hero’s Journey.

Figure 1: The hero’s journey

The audience is 22 times more likely to remember a fact when told a story. Clearly, data visualizations stand to benefit from adopting the age-old narrative structure.

Adopting the narrative structure, a data story starts with a background on the current situation, continues with evidence that builds up to the central insight, and ends with a call to action. (Figure 2)

Figure 2. The narrative structure of a data story (Source: Effective Data Storytelling By Brent Dykes)

An excellent example of a data story, the Goalkeeper’s Report by the Gates Foundation demonstrated how the pandemic exacerbated inequality. (Figures 3A, 3B, and 3C)

Figure 3A: To set the scene, the data story first established the impact of COVID-19 on poverty-stricken countries. [Source]
Figure 3B: The data story provided rising insights by highlighting the widening chasm between the haves and the have-nots pre-pandemic and post-pandemic.
Figure 3C: The data story closes with a call to action–for greater investments in women, communities, and innovations.

5. Adapt data stories to the medium

It is tempting to reuse existing charts across multiple mediums. Data storytellers who do so are doing themselves a disservice.

Charts that are not optimized for their medium might fail to deliver their message effectively. A chart originally designed for email might contain minuscule texts that cannot be read on a screen.

The Economist portrayed how data stories can be adapted from print media to social media. The printed chart (designed for serious readers) was dense in information. It was streamlined for Instagram (designed for casual scrollers).

Figure 4: The Economist’s printed chart (left) is adapted for social media (right) [Source]

6. Declutter your data stories

Clutter makes data stories harder to read. The additional cognitive load distracts the audience from the main message.

The onus is on the data storyteller to identify and remove superfluous elements that do not contribute to the intended message. The result of decluttering is striking.

In particular, our whitepaper “8 Rules for Better Data Storytelling” suggests data storytellers to:

  • Remove chart borders
  • Remove gridlines or axes
  • Clean up axis labels
  • Label data directly (as opposed to using a legend)
  • Remove data markers
  • Use special effects (bold, underline, italics, shadows) sparingly

7. Explain data stories in stages

When presenting complex data stories, one practical tip is to reveal the elements of the chart gradually. When the information is presented in digestible pieces, the audience is more likely to engage with the presentation.

A prime example is the talk “The Joy of Stats” by Hans Rosling. Watch how Rosling masterfully told his data story.

Data storytelling is here to stay

Data storytelling will soon become a necessary skill set for all in the coming decade as data insights become a cornerstone of organizations. To learn more tips and tricks on data storytelling, listen to Andy’s podcast, or learn from his most recent webinar on DataCamp.

Related
Data Science Concept Vector Image

How to Become a Data Scientist in 8 Steps

Find out everything you need to know about becoming a data scientist, and find out whether it’s the right career for you!
Jose Jorge Rodriguez Salgado's photo

Jose Jorge Rodriguez Salgado

12 min

YOLO Object Detection Explained

Understand YOLO object detection, its benefits, how it has evolved over the last couple of years and some real-life applications.
Zoumana Keita 's photo

Zoumana Keita

5 Ways to Use Data Science in Marketing

Discover five ways you can use data science in marketing. Get ahead of the game, improve your data skills, and work on a data science marketing project.
Natassha Selvaraj's photo

Natassha Selvaraj

DC Data in Soccer Infographic.png

How Data Science is Changing Soccer

With the Fifa 2022 World Cup upon us, learn about the most widely used data science use-cases in soccer.
Richie Cotton's photo

Richie Cotton

_Quote.png

The Deep Learning Revolution in Space Science

Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions. 

Richie Cotton's photo

Richie Cotton

53 min

Regular Expressions Cheat Sheet

Regular expressions (regex or regexp) are a pattern of characters that describe an amount of text. Regular expressions are one of the most widely used tools in natural language processing and allow you to supercharge common text data manipulation tasks. Use this cheat sheet as a handy reminder when working with regular expressions.
DataCamp Team's photo

DataCamp Team

See MoreSee More