Data storytelling – Overview and best practices


In today’s data-driven world, the ability to extract meaningful insights from data and compellingly convey them is a valuable skill. This skill is encapsulated in the art of data storytelling, a practice that bridges the gap between raw data and actionable insights. Data storytelling is not merely about presenting numbers and statistics, crafting a narrative that resonates with a specific audience, and combining data visualizations with context to make data insights. In this article, we will explore the essence of data storytelling and outline best practices to master this art.

What is Data Storytelling?

Data storytelling is the art of transforming complex data into a captivating narrative. It involves presenting data insights in a way that is tailored to a specific audience and makes the information easily understandable and relatable. The core of data storytelling lies in using data visualizations, such as graphs, charts, and tables, to support the narrative and convey the message effectively.

A well-constructed data story provides a clear picture of the data and explains why it matters. It takes the audience on a journey, helping them connect the dots between data points and drawing conclusions that drive action. Data storytelling involves three key components: data, narrative, and visuals. These elements work in harmony to convey complex information in an understandable and actionable way.


At the heart of data storytelling lies the data itself. Data storytellers gather, preprocess, and analyze data to extract meaningful insights. They use statistical analysis to uncover trends, patterns, and correlations that form the basis of the story. Data serves as the foundation upon which the narrative is built.


The narrative is the storytelling aspect of data storytelling. It involves creating a coherent and engaging story around the data. This narrative provides context to the key findings obtained from the data analysis. As Thomas H. Davenport, a prominent figure in business management, aptly puts it, Narrative is how 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.” A well-crafted narrative inspires the audience to take action based on the data.


Visualizations, the third component, play a pivotal role in data storytelling. They take the form of graphs, images, or videos and are used to enhance the narrative. Visuals are a powerful tool for conveying information quickly and effectively. As the saying goes, “A picture is worth a thousand words.” Visuals make data more accessible and memorable, reinforcing the message within the narrative.

Why is Data Storytelling Important?

The importance of data storytelling cannot be overstated in the modern business landscape. Here’s why it matters:

  • Meaningful Communication: Data storytelling ensures that data insights are communicated in a meaningful and relevant way to the audience. Instead of overwhelming viewers with raw data, it packages insights within a narrative that can be easily understood and remembered.
  • Engagement: Engaging an audience is crucial for effective communication. Data stories can capture the audience’s attention and maintain their interest throughout the presentation. When people are engaged, they are more likely to absorb and act upon the information.
  • Driving Action: The ultimate goal of data analysis is often to drive action or decision-making. Data stories are persuasive tools that can influence decisions, making them a vital asset for organizations seeking to use data to their advantage.

3 Major Components of Data Storytelling

To excel in data storytelling, one must understand and leverage these three major components:

1. Data Analysis

Data storytellers begin by gathering and preprocessing the data they need. They conduct statistical analyses to identify key data trends, patterns, and insights. This step is crucial because it forms the foundation for the entire story. It requires a keen eye for detail and a deep understanding of the analyzed data.

2. Narrative Construction

Crafting an engaging narrative is the next step in data storytelling. This involves weaving a story that provides context to the data, explains its significance, and guides the audience through the insights. A well-structured narrative simplifies complex data and makes it relatable. The narrative humanizes the data, making it relevant and actionable.

3. Visualization

Visualizations are the storyteller’s visual toolkit. They include various forms such as bar charts, line charts, scatter plots, and more. The choice of visualization depends on what the data storyteller intends to convey. Visuals should complement the narrative by highlighting key points, trends, and comparisons within the data. Effective visualizations enhance understanding and retention, making the data story more impactful.

How to Ensure That Your Data Tells a Story?

To create a compelling data story, data must be presented in a business context, with relevant data, comparisons, trends, and other key information. For example, a dashboard shows that your company sold 200 product units. To understand the significance of this figure, you should:

  • Provide Context: Compare the current sales figure with data from previous months to determine whether 200 units is above or below the average. This context helps interpret the data effectively.
  • Identify Sources: Determine the sources of the sales. Did a single team or multiple teams contribute to this number? Understanding the origin of the sales can offer valuable insights.
  • Align with Targets: Assess how the current sales align with your targets or goals. Are you on track to meet your objectives, or is there a gap that needs attention?

Mastering data storytelling requires a combination of skills and practices. Here are some key steps to become a proficient data storyteller:

Understand Your Audience

Tailor your data story to your audience. Consider their roles and what information is most relevant to them. Business executives may require high-level insights, while technical teams might appreciate a more detailed explanation of data analysis methods.

Choose Appropriate Visualizations

Select the right data visualizations to convey your message effectively. Different visualizations excel at highlighting various aspects of data, such as comparisons, relationships, distributions, or compositions. Choose the one that best suits your objectives and data complexity.

Avoid Clutter

Keep visualizations clean and clutter-free. Remove unnecessary information or elements that do not contribute to the story. Clarity should be your priority, and decluttering helps maintain focus on the essential data points.

Use Vibrant Colors

When using color in your visualizations, ensure it is accessible to all, including those with visual impairments or color blindness. Maintain sufficient contrast between colors and avoid placing similar colors next to each other to enhance readability.

Data Storytelling Best Practices

To craft compelling data stories that resonate with your audience, consider these best practices:

  • Audience-Centric Approach: Understand the specific needs of your audience based on their roles and responsibilities. Tailor your data visualizations to provide the most relevant context to them.
  • Choose Appropriate Visualizations: Different roles require different levels of detail and context. Select the right data visualization types that best convey your audience’s needs, such as tables, column charts, or pie charts.
  • Use Familiar Language: Communicate using language, terminology, and acronyms that are familiar to your audience. Avoid jargon or technical terms that might confuse or alienate them.
  • Simplify and Declutter: Eliminate unnecessary elements and clutter from your visualizations. Keep the focus on what is important to prevent distraction.
  • Design for Emphasis: Utilize design concepts to draw attention to key information. Use color, size, and formatting to highlight critical data points.
  • Clear Labels and Titles: Always title your charts, label your axes, and include legends where necessary. Clarity is essential for understanding.
  • Legibility: Ensure all text, including labels and numbers, is legible. Small or unclear text can hinder comprehension.

In conclusion, data storytelling is a powerful tool that transforms data into actionable insights by combining the analytical with the narrative. You can become a proficient data storyteller by understanding your audience, selecting the right visualizations, and following best practices. In doing so, you’ll unlock the potential of data to inform decisions, drive actions, and make a meaningful impact within your organization.