How to Tell a Story With Data | Lucidchart Blog
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Every business wants to make good decisions. And good decisions rely on good information. But how you communicate that information matters.  

This is why understanding and translating data into meaningful insights is crucial. However, if you aren’t connecting that information to your audience, they will have little motivation to act on it. That’s where data storytelling comes in. 

Data stories help you communicate key insights clearly and compellingly—driving change and inspiring action in business. If telling a story doesn’t come naturally to your analytical mind, you’re not alone. Luckily, you don’t have to be an English major to tell a good story.  

Use the tips and steps below to craft compelling data stories that inspire, persuade, and motivate your teams and organization. 

What are data stories? 

Data stories are narratives that explain how and why data changes over time—often through visuals. But data storytelling isn’t just about making great charts and data presentations. It’s about communicating insights that deliver real value. 

Good data stories have three main elements:

  • Data
  • Visuals
  • Narrative

 Together, these elements put your data into context and pull the most important information into focus for key decision-makers.

Data stories vs. data visualizations

Data stories and visualizations are connected but distinct. Data visualization is simply a visual representation of information. 

Visuals can play an important role in telling a story and communicating key pieces of information. However, a data story is what puts that information into context and communicates why it matters and what actions to take. In other words, data stories connect the audience with the data. Data visualizations support and enhance data stories, helping you communicate your findings elegantly and effectively. 

Why data storytelling is important

Data storytelling is ultimately about understanding context and inspiring change or action. When data analysts review and present their data, a story can help them communicate complex ideas and simplify (and accelerate) the decision-making process for stakeholders. 

Tom Davenport, a thought leader in analytics and a professor of Information Technology and Management at Babson College, emphasizes the role and power of narrative in data analytics

“Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation—all the things that make data meaningful and analytics more relevant and interesting.”

In other words, a story ensures your data is memorable, persuasive, and engaging.  

How to tell a story with data and analytics 

So, how do you determine a good story? And more importantly, how do you tell it effectively?

Use the steps below to get started.

1. Identify your story

The first step to telling a good data story is to uncover a story worth telling. You can start by asking a question or forming a hypothesis, then compiling and digging into relevant data to find answers. 

As you consider different stories, ask yourself:

  • What are you trying to explain? 
  • What are your goals? 
  • Are you trying to get buy-in on a proposal?

There are several ways to approach data to uncover a story—and the story you set out to tell may not end up being the story you find. As you collect and analyze your data, consider using the following approaches to help you identify a theme and develop a structure for your story:

Look for correlations

What connections do you see between data points? Are there any interesting or surprising correlations? These relationships can provide a compelling foundation for a story.  

Identify trends

Trends indicate the direction in which something is changing or developing. 

For example, is there growth in a certain product or service your business offers? Or maybe you want to know your website traffic patterns over time—you may discover that certain days or times tend to be higher or lower volume. 

Identifying new or evolving trends in your business is crucial for understanding how the company should respond and prepare.  

Draw comparisons

Comparisons and rankings can help you uncover interesting correlations and understand how data relates to one another and why. 

For example, you might compare open rates for two different email subject lines to see which subject line was more effective. From there you can dig into what made one data set more successful and provide insights.  

Look for outliers

Data that doesn’t fit in with the rest of your data set can be just as useful to you. Outliers are any data that act unusually or outside the norm. Look for outliers and ask why. Why is the data behaving that way? What is the cause? You may uncover a more interesting (and useful) story. 

Pay attention to data that are counterintuitive

Similar to your outliers, pay attention to any data that is counterintuitive or surprises you. When you evaluate trends or compare data, are there any results that you didn’t expect? What might cause those results? Some of the most compelling stories are those that are unexpected. 

2. Be aware of your audience

Always be aware of your audience when developing and sharing your data stories. If the story you want to tell isn’t relevant or interesting to your intended audience, it won’t have the impact you want. 

As you build your story, ask yourself: 

  • Who is my audience?
  • Is this story relevant to my audience? Does it solve a problem they care about or provide needed insight?
  • Have they heard this story before?

Your audience’s age, demographics, job, and subject matter expertise will affect how they understand and respond to your stories (and should inform how you tell your stories).

For example, if you are speaking to a room full of engineers, you may want to provide more technical details and dig into the data sets more thoroughly as you tell a story. However, an audience of executives will likely have a broader range of professional experience and will be looking for simplified data with clear takeaways. 

Customize your story and approach it from different angles depending on the audiences you plan to share it with. 

3. Build your narrative 

With your data in hand and your audience in mind, you can start developing a narrative. 

Consider:

  • Who are you talking to?
  • What do you want your audience to know or do?
  • How can you use your data to make your point?

A narrative isn’t just an explanation of your data. A good data story should take your audience on a journey. To do this, your data story should follow this basic formula: 

  • Context: What is the situation? Why are you telling this story? Look for a hook to engage the audience. 
  • Characters: Who are the key players?
  • Problem: What is the conflict?
  • Solutions: How can the problem be solved? Or what key insights or actionable steps should we take? Place emphasis on value. Make it relatable. What will be gained?

Pro tip: Tell your story linearly. Start from the beginning (context) and build from there. Don’t start with your findings—those should be the most exciting part of your story. Save that for the climax at the end. 

4. Use visuals to present and clarify your message

Finally, a good data story needs visuals. Visuals are a powerful way to engage your audience and improve retention—especially when communicating with non-technical audiences.  

Visualizing your data story enhances comprehension at every level. Storytelling with data visualization helps you simplify the information, highlight the most important data, and communicate key points quickly. 

There are many ways to visualize your data including: 

  • Flowcharts
  • Bar graphs
  • Infographics
  • Road maps
  • Pie charts
  • Scatterplots

Choose visuals that will make it easiest for your audience to understand and engage with the data. 

Show off your data with the right visual.

Learn best practices for effective data visualization.

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What data storytelling is not 

Data storytelling is a powerful tool for engaging stakeholders and inspiring action. However, when done incorrectly, it can lead to incomplete or misleading information and conclusions. Data storytelling should never lie, mislead, or misrepresent data. As you develop your data stories and visualize your data, don’t:

  • Manipulate scale. When visualizing data, don’t pick arbitrary values to base your scale and units. Make sure you are representing the full context visually.  
  • Cherry-pick data. Don’t only show the data that best supports your ideas, show the whole picture. 
  • Be inconsistent. Don’t change colors, labels, and conventions between visuals. Inconsistencies across visuals and language can be confusing and make it difficult for your audience to follow the story and understand the data accurately.

Make sure you are telling the full story. Use good data from credible sources to inform your interpretations and conclusions, and always provide context. 

Data-driven storytelling is a powerful way to communicate complex ideas, create buy-in, and inform better decision-making for leaders at every level. By combining best practices in visualization, data analysis, and storytelling, you can create compelling data stories that drive change. 

Learn how you can link data to your Lucidchart diagrams to see important metrics in context and better tell your story.

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