All Data Has A Story. Here’s How to Tell it

All Data Has A Story. Here’s How to Tell it 

As data scientists, we all know the power of data. It provides hard evidence for things that are working (or not), it allows us to test our assumptions, and it helps us make better decisions. But there’s one thing data can help with that often gets forgotten because of its scientific nature: storytelling. 

Sure, you can tell a story without data. But doing so makes it more fiction than fact. A story or presentation without data is all opinion. It is hard to sway people when you don’t have any facts, right? Data is a powerful tool in storytelling of all kinds. Good storytellers use data to back up their stories and good data scientists use storytelling techniques to tell stories obscured in their data. 

Data Rarely Speaks for Itself

Here’s the thing about data. In its raw form, it’s not consumable or compelling to an average person. You can’t just put up a bunch of charts or data points and expect them to explain themselves. You’ve got to craft a story that will gain the reader's attention and draw her in.

Storytelling is a natural human trait. From the time we're born until the time we die, we are told stories. We’re told stories at bedtime growing up, we’re told stories throughout the day, and we immerse ourselves in stories every time we turn on the television. 

Humans have been telling stories for thousands of years - even before the invention of writing. According to a study, 65% of our daily communication is based on stories. So how do we use the power of storytelling in data science? Here are some methods to try. 

Don’t just tell. Show. 

You know the saying, “a picture is worth a thousand words?” A compelling data visualization can bring data to life and help show relationships between data points. We’re not just talking pie charts and line graphs here. Putting some thought and creativity into your visuals can have a huge impact on the way data is presented. Not to say pie charts and line graphs are irrelevant because they do have their place. But some other data visualizations excel when dealing with complex sets of data.

Tools like Tableau make visualization easier, as long as you identify the relationships. How does each point relate to another? What patterns do you see? Good data stories are hidden in data relationships. 

The example below is a visualization created to help people in Japan evaluate the seismic risk of their area in the event of an earthquake. It’s not only easy to see the relationships and quickly understand, but it’s also interactive. Users can hover over individual neighborhoods to see where they fall in each category.

 tableau-data-visualization-storytelling

Data visualization is as much art as it is science. Tools like Tableau can help you whip up a visual in no time, however choosing that visual, its colors, typography, and other elements are key. Here are some tips.

  • You don’t have to visualize everything. What story are you telling? What points are you making? Think about the relationships and patterns you want to show. 
  • Keep it clean. Too much data can muddy your point. Use clear typography, simple labels, and stick to the data that matters.
  • One size does not fit all. Choosing the right format is important to tell the story and answer key questions generated by data. For example, bar charts compare categories while bullet charts show progress. Histograms show data clusters while maps are for location-specific data.
  • Use predictable patterns that are easy to understand at a glance: numerical, alphabetical, sequential, etc.
  • Use color cues to highlight information. For example, reds and blues are great for visualizing temperature. 
  • Get creative by using contextual clues like shapes. 

If all else fails, get design inspiration from infographics or from the Tableau blog itself. Tableau has a weekly learning project called Makeover Monday that thousands of data scientists join in on to practice their skills.

Data Visualization vs. Data Storytelling

Here’s the thing. Data visualization and data storytelling are not the same things. A picture may be worth 1,000 words, but a PowerPoint presentation with no story results in a room full of bored people. 

You may be good at data analysis, but if you’re not good at presenting the data then you lose your audience. According to Thomas Goulding, a Northeastern Professor, presenting your data is one of the most important skills in data science because the data is useless if the audience doesn’t understand it. Reports and dashboards can be overwhelming. You have to get them thereby adding a narrative - and that’s where data storytelling comes in. 

Storytelling Techniques

Data storytelling humanizes your data. It’s where facts and figures make the connections easier. Storytelling science has proven that the language processing parts of our brain are activated when listening to stories. So how can data scientists incorporate storytelling techniques to tell more data stories? Here are some tips.

Use Storytelling Arcs 

Joseph Campbell’s “Hero's Journey” is a tried and true method of storytelling. It’s used in fables, advertising, and some of our favorite movies (any Star Wars fans out there?). Campbell was a mythologist who studied the similarities of mythologies across cultures. He concluded that no matter the culture, the stories all had the same pattern. He called this the monomyth or hero's journey. The monomyth takes a character through a process in such a way that once your audience ends their journey, they understand the underlying meaning behind the tale. The journey includes 17 phases but the basic structure is this: a hero who goes on an adventure, and in a decisive crisis wins a victory, and then comes home changed or transformed. 

There’s also ‘Freytag’s Pyramid,’ a dramatic structure that outlines seven key steps in successful storytelling: exposition, inciting incident, rising action, climax, falling action, resolution, and denouement. 

Either way, these storytelling arcs can be used to help structure your data story and take the audience through a transformation that gets them to your final point.

Don’t jump around

You need your audience to follow you through the entire presentation. As fun as flashbacks are in movies, when presenting data, our brains prefer linear storytelling. Be sure there is a clear beginning, middle, and end. 

Create a cast of characters

Did you know that telling emotional and character-driven stories boosts our levels of oxytocin? Oxytocin is the “love hormone,” it helps create a feeling of empathy. No story is complete without characters. Who are yours? Are you presenting data on literacy rates? Tell a story about someone who struggled with illiteracy and how it affected them. Are your data points related to sales? Tell a success story from your sales team. Pulling in real-life examples brings your data to life. It gives it that relatable, human connection. We tend to dissociate with stats and numbers but when it’s real people we’re talking about, the mood changes.

Create a world

Worldbuilding is an important skill to have as a writer. Video game developers and fiction writers are excellent at building worlds for us to immerse ourselves in and escape for a bit. The same is true for data storytelling. Set the scene. Lay the groundwork. Why are we here? What problem are we trying to solve? Why does it matter? 

Know your audience

Marketers don’t write a single word of copy until they’ve studied their audience. Before they develop campaigns and collateral they know their audience's motivations, values, interests, demographics and much more. The same is true for data storytelling. You need to know your audience and what it cares about. What level of understanding doe it already have? You need to be able to create a rich framework where your findings will be understood. You can’t do that if you don't know your audience. 

Although storytelling is natural to all of us, we tend to forget it while using data and statistics. But if you can build a basic structure of a story with your data, you can win over audiences and persuade them to your perspectives. Keep telling those stories!

This guest post was submitted by RTS Labs, a data science consultancy and software development firm.

About the Author

A co-author of Data Science for Fundraising, an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.

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