4 Storytelling Techniques to Bring Your Data to Life

Every dataset your organization tracks started with people. Each purchase, support ticket, and canceled subscription is somebody making a choice. Find those people in the data and you’ve found the story that gets decision-makers to move.

That’s what data storytelling is: turning numbers into a story your audience understands and can act on. At the core of every story is a likable hero who encounters roadblocks, which makes the audience root for them to succeed. When you include stories about the people behind the numbers, you’ll give the data more meaning. This can motivate decision-makers to act.

Below are four data storytelling techniques to help shed light on challenges and opportunities in your data: identify the hero and adversary, talk with the people behind the data, name the conflict and how to solve it, and give decision-makers context, including the emotional arc of the numbers.

1. Identify the hero and adversary

The first step to gaining insight is to understand the individuals whose actions are behind the numbers. These data-generating people are the characters in your story. For businesses, heroes could be employees, customers, or partners who interact with your organization in various ways.

For instance, imagine the head of marketing for a fast-growing retail coffee chain noticed a 40% drop in sales last quarter. To uncover the cause, she must find out more about the people behind the numbers. Then, her task is to determine how she can motivate that “hero” to turn things around.

As she digs into the data, the marketing head sees that new buyers have been visiting their coffee shops in greater numbers. But the company’s loyal repeat customers have been visiting less often than in the past, and spending less, too. Their most faithful coffee fans must be running into a barrier, or adversary, that’s tamping down their spend. What could it be? Now she’s got a data storytelling challenge on her hands.

2. Talk with the people behind the data

Data can’t speak for itself. It needs a data storyteller to be its voice. To understand why the numbers are what they are, talk with the people who are producing the data. You can gain insights by visiting online forums, reading results from customer surveys, or by consulting with outside professionals who understand your market.

But the most effective way to learn about people is to speak with them directly. Identify some individuals who can serve as your data heroes and reach out for an interview. Ask them about their goals, pain points, motivators, and blockers. And really listen to what they have to say, with an open mind. You’ll discover things no spreadsheet could tell you.

For example, the marketing leader for that coffee chain could interview a random sampling of repeat customers from a few different store locations. She might be surprised to learn that the drop in sales was likely caused by a recent change her team made to the company’s mobile app. The new, and supposedly improved, loyalty rewards notification feature made it harder for existing customers to see special offers. Turns out, this created less of an incentive to buy on the fly. But through the course of her interviews, the marketing leader learned about another feature her loyal customers really wanted in the app. Talking to real people will reveal both obstacles and opportunities in the data that need to be addressed.

3. Name the conflict and how to solve it

In every story, the hero faces a conflict. It could be interpersonal conflict, like clashing with another character in the story who gets in the way of their goal. Sometimes the protagonist is at war with a group of people or an entire system that’s broken.

The same thing can happen to humans in a business setting. The heroes in your data could be a sales team that’s in conflict with other people, like reps from competing companies who are calling on their accounts. Or the heroes could be customers who conflict with a flawed system, like an outdated software application. A hero can also have conflict within themselves, such as a CEO who doubts their recommendations and avoids making a major ask of the board.

It’s important to identify any conflicts your hero is facing and include that context in your data storytelling. That information will help decision-makers identify what exactly they can do to help the hero get unstuck.

4. Provide context and follow the emotional arc

Data points don’t exist in a bubble. When you include context about how the data has evolved, you’ll shape a big-picture story that reveals even more insights about heroes and adversaries and how you can move them forward.

Returning to the scenario of the coffee chain, the marketing leader could choose to show her team only the data from the past quarter’s revenue dip. That, coupled with anecdotes from her customer interviews, would surely light a fire that gets them to fix the notifications feature fast. But she also wants her team to prioritize other features that would grow sales quickly. So she zooms out to show how past app updates lined up with revenue increases, and the team sees the positive impact those updates had on the business. The Recommendation Tree can help you connect that bigger picture to the recommendation you’re making.

Context also includes the shape of the data over time. Every story has an emotional shape: a rise, a fall, a turn, and a payoff. Your data has one too, once you look for it. When revenue climbs, dips, then claws its way back, that’s an arc, and your audience feels the tension and the relief right along with the line on the chart. Map the highs and lows the way a storyteller maps a plot, and let people sit in the hard part before you show them the way out. That’s what makes a chart land in the gut and stay with people after the meeting. And how you display that data in a presentation decides whether the arc reads at a glance.

Presenting data to an analytical audience

Some rooms are built to poke holes. When you’re presenting to analysts, engineers, or a finance team, too much emotion reads as spin and they’ll tune you out. This is where the old rhetorical trio earns its keep: ethos, pathos, and logos. Lead with logos, the logic and the rigor, so they trust your method. Build ethos by showing your work and your sources, so they trust you. Then bring in a measured dose of pathos, the human stakes, so they care enough to act. With an analytical audience the story runs underneath the logic. Get the numbers airtight, then let the human thread give people a reason to move.

Remember, it’s people that move data in a positive direction, so you’ll need to build data-driven storytelling skills to bring their tales to life. Every statistic, pie chart, or line graph can tell a heroic story. By understanding the people behind the numbers, recognizing their conflicts, and providing context, you help decision-makers make a meaningful connection to issues and opportunities in the data. If you want to see where your team stands today, the Data Leadership Skills Diagnostic is a good place to start.

Help your team find the story in every dataset

Presenting Data Insights helps your team turn analysis into clear, confident recommendations, and Data Storytelling Training for Teams builds these techniques into how your people work with data every day. Talk with our team and we’ll help you find the right fit.

A version of this article originally appeared in MIT SMR.