What Is Data Storytelling? Definition, Examples, and Tips

Key takeaways

Data storytelling involves developing a narrative around the data to create more engaging, influential, and easy-to-understand content.

• Effective data storytelling includes three key elements: data, visualizations, and a narrative.

• Storytelling has been shown to activate brain areas that help develop long-term memories, making information presented through data storytelling more likely to be retained.

• You can develop your data storytelling abilities by building data analysis skills and learning programming languages, such as SQL or Python.

Learn more about data storytelling, including why it's important, and explore tips for telling your own data story. If you're ready to start building the skills you need to work with data, enroll in the Microsoft Data Visualization Professional Certificate. In as little as four months, you can create data visualizations, build data models, and prepare datasets. Upon completion, you'll earn a shareable certificate.

What is data storytelling?

Data storytelling is the practice of crafting compelling narratives to effectively convey data-driven insights to stakeholders. Its objective is to boil down complex information into only its most essential elements so that it is easily understood and grasped by others through a compelling, engaging narrative.

Data storytelling examples

Conceptually, data storytelling is similar to storytelling in general: a narrative unfolds as the natural consequence of a series of events. The difference is that in data storytelling, those events are data points (rather than characters or plot points) that, taken together, start to tell their story.

Some examples of data storytelling include:

• A social media marketer illustrates a particular post's positive impact on engagement by showcasing it beside others that performed less well. They organize their data so that the reason for its strong performance is clear to stakeholders.

• A public health agency releases a report detailing the personal experiences of individuals impacted by a disease alongside statistics about infection and hospitalization rates and demographic breakdowns.

Why is data storytelling important?

Data storytelling is important because it helps communicate data insights in a way that others can understand and encourages them to take meaningful action. Storytelling has been shown to activate certain areas of the brain that assist with developing long-term memories, making it more likely people will retain the information presented through data storytelling. 

In addition to making the information easier to remember, data storytelling allows you to present findings in a digestible way. Having people fully understand what the data suggests means they can feel more confident when making data-based decisions and come to these conclusions sooner.

Hear more about data storytelling in this lecture from Fractal's Data Storytelling course:

What is data storytelling used for?

There are several different ways you can use data storytelling. First, you can use data storytelling within your organization to share findings with other staff members. For example, if sales numbers of a particular product were falling, you could use data storytelling to construct a compelling story in a way that will help illustrate the need for a solution and inspire action.

You can also use data storytelling to influence people outside your organization, such as your target audience. For example, you could use data storytelling to help exemplify how selecting your business over a competitor will provide better results. 

3 key elements of storytelling with data

Effective data storytelling primarily involves three key areas: data, visualizations, and narrative. The following offers a closer look at those elements to help you properly utilize all three to assemble a quality story with your data.

1. Data

You'll first need to grasp the data and its information before constructing a data story. The insights the data analysis provides ultimately work as the basis of your story and give you something to center your narrative around. Before performing your analysis, you will have to sift through the data set to identify the most relevant insights. This makes it crucial to have robust data literacy and the ability to go in and analyze the data.

2. Visualizations

Data visualizations not only help make your story more interesting to your audience, but they’re also useful tools for helping to further explain and uncover data insights.

Selecting a visualization type that correctly represents the data is essential. You should consider factors such as who you’ll be presenting the data to, the question your data answers, how much data you’re working with, and the type of data used in the analysis. For example, you could use columns to represent quantitative data or implement maps when you develop a visual narrative surrounding geographical data.

3. Narrative

The narrative ties everything together in an impactful way. Before developing your story, consider who your audience is so that you can convey your message in a manner that will interest them. 

A good narrative should have several fundamental elements. Within your story, identify a “hero.” In this context, your hero could be the individual or team helping to work towards an established goal, such as improved customer retention metrics. The narrative should also have a beginning, middle, and end that’s easy to follow, rather than jumping back and forth between timelines. Lastly, construct a narrative that the listeners can relate to to make a real impact on your audience. Doing so will get them more invested and help them get more out of the information.

Pros and cons of data storytelling

Implementing data storytelling has plenty of benefits. Not only will the data and its insights be easier for the audience to understand and remember, but you can also get them more engaged and ready to take action. It also encourages data-driven decision-making and helps increase data literacy throughout your organization. 

However, this process has some challenges as well. For example, it’s critical that you implement the proper data visualization method, or you risk incorrectly conveying information. Additionally, while you can view this as a positive if you’re looking to enter the industry, another challenge associated with data storytelling is the lack of professionals with adequate data skills. By developing your data storytelling abilities, you can become a desired commodity by employers.

Who uses data storytelling?

Data scientists and data analysts often use data storytelling. These two positions share many similarities, with both analyzing data to find valuable insights that you can use to make educated decisions. Where the two largely differ is that data scientists are responsible for developing the specific tools that are in place to analyze data.

Data storytelling tools and skills for getting started

To develop your data storytelling abilities, you should start by focusing on gaining data analysis skills, including familiarity with computer programming and working with and creating data visualizations.

Some popular programming languages for working with data include SQL, Python, Julia, and R. To build informative data stories, add data visualization tools to your skill set. Tableau is one option that gives you the tools to develop charts, including interactive charts, histograms, and flow charts. Other options include Microsoft Power BI and Excel. Additionally, several libraries in Python and R, such as Matplotlib in Python and ggplot2 in R, can be used to build visualizations with your data.

Learn more: What Is Python Used For? A Beginner's Guide

Stay current with data analysis trends

Join Career Chat on LinkedIn to get timely updates on popular data analysis skills, tools, and certifications. Or if you want to learn more about data analysis, check out these free resources:

• Read this article: Getting Started with Data Visualization + Types and Tools

• Bookmark this page: Data Analysis Terminology

• Watch on YouTube: The Future of Data Visualization: AI &b Automation Trends

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