Storytelling with Data

The best data scientists do more than crunch numbers and generate insights from complex data sets. They are proficient storytellers who create engaging narratives and communicate valuable insights to a receptive audience. These professionals reveal the ‘how’ and ‘why’ of data analysis, so businesses can comprehend and get more value from data. 

If you plan to become a data scientist, it’s critical to understand the storytelling techniques that will help you present data to marketers, directors, investors and stakeholders. You won’t just tell your audience about algorithms and hard numbers but explain the phenomena behind them. Learn more about data storytelling below. 

What Is Data Storytelling?

Here’s a great definition of data storytelling[It’s] the process of translating data analyses into understandable terms to influence a business decision or action.

As a data scientist, it’s your job to explain the meaning behind data to those who lack your analytical skills. That means providing context for data and presenting data in a way that resonates with your audience. 

Data storytelling isn’t a new concept. Books like “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic discuss, in great detail, how to use data to create a compelling narrative. However, few data science programs teach students how to communicate information from data analysis, resulting in a labor force that’s competent at determining and evaluating data sets and variables but unable to express information to audiences. 

Some data scientists are, by nature, introverted and would rather analyze data sets than talk about them. However, properly communicating your ideas and findings will help your audience understand what lies behind your insights. Most people won’t understand complex processes like data wrangling, programming, deep learning, data manipulation and statistical models, so it’s your job to clear up the confusion and explain why you made the analytical decisions you did. 

How to Tell Stories in Data Science

Think of the last great book you read. The author presented a logical flow of events, taking you on a journey. There was a story with a beginning, middle and end. 

Storytelling in data science also requires a flow of events that uncover the meaning behind data. That means creating a narrative that explains complicated concepts through the use of powerful data visualizations like reports, graphs, charts and infographics. These visuals will help you communicate your story to an audience and keep them engaged. 

Say you work for a large business and discover a way to save the company money after many months of data analysis. Instead of printing off and giving spreadsheets to directors, you can tell a story through visualizations that explain your findings. Showing directors graphs and charts, for example, will help them learn how you came to the conclusion of saving them money. 

Think about that last great book again. It almost certainly had a setting, characters and storyline. You can apply these elements to your data story to engage your audience.


The setting of your data story might be the business your work for. Or, if you work independently, a client using your services. 


The characters in your story are those who need your data science skills to solve a problem. For example, a sales manager who wants to identify the most valuable customers to move through their pipelines. You, the data scientist, will also be a character in your own story. 


Your storyline will be how you solved a problem in your setting. For example, you used a data algorithm that discovered different ways to identify the most valuable customers for the sales manager. You will describe how you created this algorithm, the challenges you encountered and the results you generated.

Tips for Telling Stories with Data

Here are a few tips for more effective data storytelling:

Know your audience

The type of stories you tell with data depends on the audience. When communicating with marketing teams, you could use more advanced statistical models if marketers are already familiar with them. Presenting ideas and insights to directors might involve the use of simpler charts and graphs that convey the most important facts and figures. 

Highlight key data points

There’s no use presenting your story with data insights if your audience can’t understand your visualizations. Showcase key data points that illustrate complicated concepts rather than handing your audience all of your data findings. 

Be prepared to answer questions

After telling your story through data, your audience might ask you to explain aspects of your data reports or why you used a particular data model or algorithm. Answering these questions correctly is fundamental if you want your audience to understand the information you are presenting to them.

Final Word About Data Storytelling

Data storytelling is all about communicating the power of data through a compelling narrative. Data visualizations can help you achieve this goal and make it easier for your audience to understand the analytical decisions you made and how they benefit their business. 

How The Data Incubator Can Help

Before you tell stories with data, you need to learn how to analyze it! TDI’s programs let you develop skills from the country’s top instructors, paving the way for your future data science career. The Data Incubator can help you learn real-world data science skills regardless of your educational experience:

The Data Incubator is here to guide you through your data journey! Get in touch with the admissions team with any questions about applying for a data science program.

Related Blog Posts


Career Enablement at TDI

TDI is more than your typical bootcamp. We provide robust career support to ensure exceptional outcomes for our students. Learn more about our career enablement options here!

Read More »
is it too late to become a data scientist

Is It Too Late for Me to Become a Data Scientist?

You might think data science is a young person’s game. After all, this is a relatively new discipline that might not have been around when you were in school. But research shows nearly half of all data scientists are 40 years and older. Whatever your age, it’s never too late to pursue your dreams of becoming a qualified data scientist. Learn how to succeed in this profession in this blog.

Read More »