What Is Data Storytelling?

Written by Coursera Staff • Updated on

Learn more about data storytelling, including why it’s essential for effectively presenting data, the critical elements of developing compelling stories with data, and details about the types of jobs that may use this valuable skill.

[Featured Image] A team of colleagues in a brightly lit room listening as one person speaks and uses data storytelling to bring data-driven insights to life.

Data can provide valuable insights, but making sense of what data tells you requires the ability to communicate what the data says in a way that makes sense, even to those without technical knowledge. This is where data storytelling comes in. Data storytelling involves developing a narrative around the data and its analytical insights to share the evidence and create more engaging content that’s influential and easy to understand.

Although the two are similar, data visualization and data storytelling are, in fact, different things. Data visualization refers to creating visual or graphic representations of data. In contrast, data storytelling focuses more on the communication aspect, which can include visualizations, to help your audience connect with the data-driven details and insights you share.   

Why is data storytelling important?

Data storytelling is important because it helps communicate the insights within data so that everyone can understand and translate them into meaningful action. It can be challenging for our brains to remember information and distinguish what information is important enough to remember. Storytelling encourages the listener 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 decisions based on the data and coming to these conclusions sooner.

What is data storytelling used for?

You can use data storytelling in two different ways. 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 data storytelling

Effective data storytelling primarily comes down to three key areas: the 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.


You'll first need to grasp data and its information before constructing a data story. The insights the 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 available data to identify the most relevant data to include; then, you can move forward with your analysis. This makes it crucial to have robust data literacy and the ability to go in and analyze the data.


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


The narrative is where you can tie everything together in an impactful way. Before developing your story, be sure to 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, this is where understanding your audience is especially important. 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 comes with plenty of benefits. Not only will the data and its insights be easier to understand and remember for those in the audience, you can get them more engaged and ready to take action. It also encourages data-driven decision-making and helps to 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 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 differentiate is that data scientists are responsible for developing the specific tools that are in place to analyze data.

How to get started in data storytelling

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, you can find several libraries in Python and R for building visualizations with your data, such as Matplotlib in Python and ggplot2 in R.

Getting started with Coursera

On Coursera, you can find many highly-rated courses to help you develop the skills needed to analyze data and create visualizations. For example, IBM’s Data Analysis with Python course is a great option for growing your skills in preparing and performing data analysis while gaining experience with several Python libraries. Share Data Through the Art of Visualization from Google is another excellent option for learning more about effectively presenting data and data storytelling.

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