Data-Driven Storytelling: 9 Techniques for Effective Visualization

Data is central to many business decisions and operations. Whether you’re communicating with an internal team or a customer, data can help you tell a compelling story that supports your position and highlights the value of your products or services.

However, raw data can’t do the job on its own. You can’t simply give the audience a handful of spreadsheets with rows of numbers and hope that they’ll figure out your key message or take action. To get the point though and achieve your objectives, you need to present the information in a compelling way to engage your audience.

This is where data-driven storytelling comes in to turn data into an effective, engaging and convincing communication tool. Let’s look at what data-driven storytelling is, the benefits of using this method, how you can use data to tell a story effectively and some data visualization examples.

What’s Data-Driven Storytelling?

Data storytelling is the practice of blending hard data with human communication to craft an engaging narrative that’s anchored by facts. It uses data visualization techniques (e.g., charts and images) to help convey the meaning of the data in a way that’s compelling and relevant to the audience.

Data-driven stories are created through the process of analyzing and filtering large datasets to uncover insights and reveal new or different ways to understand the information. They’re tailored to a specific audience and the context in which they’re consumed. This can help communicate information or a point-of-view most effectively while generating the least cognitive load, which affects the mental energy the audience needs to spend on grasping your message and therefore, impact how well it’s received.

The Benefits of Data-Driven Storytelling

In today’s environment in which we’re besieged by data and struggle to make sense of them, data-driven storytelling offers a valuable tool that not only presents the data, but adds context, meaning, relevance and clarity to help the audience interpret the information and extract value from it. 

Here are some benefits of using data storytelling as a communication tool:

• Data stories add value by assigning data meaning and context so the audience can connect the dots and turn numbers into insights that they can use. The insights, in turn, facilitate decision-making and spur actions.
• By using numbers and facts, you can anchor your claims and increase the credibility of your content. This builds trust with your audience and increases the likelihood that they’ll be convinced by your point-of-view.
• Data stories crafted with internal and proprietary data help you stand out and capture attention. The original insights, useful perspectives and unexpected angles allow you to cut through the clutter in a world full of regurgitated content.
• The graphic elements are attractive to the media, increasing the chances that your content gets picked up by high-profile publications or influencers. This will help you generate brand awareness, reach new audiences and position your brand as a thought leader.
• The combination of narrative and visual elements activates both sides of the brain, delivering an experience that’s analytical and emotional at the same time to help your audience cement the information through comprehension, retention and appeal. 
• The use of various techniques, such as interactive data visualization, can further foster engagement with the audience. For example, by guiding them to arrive at a conclusion or exploring the part of the data story that’s most relevant to them.
• Data-driven storytelling is versatile. It can be incorporated and repurposed in many external and internal communication channels, such as annual reports, brochures, case studies, presentations, videos, website content, white papers, social media posts and more.

How to Tell an Effective Data Story With Data Visualization Best Practices

Data visualization is central to crafting a data story that triggers emotions, engages the audience and inspires action. Here are 9 top techniques to augment data-driven storytelling with effective visualization:

1. Define Your Objectives and Know Your Audience

First, you need to define the purpose of your data story. Doing so will help you identify specific trends, focus on a subset of data, categorize the information to support a thesis, illustrate the effectiveness of a strategy or highlight the value of your products.

Also, you may need to tell different stories from the same set of data depending on the audience and their priorities. For example, what’s important to board members probably isn’t the same as what the floor staff cares about. What appeals to the marketing team is likely to be different from what the finance department finds useful. 

You also should consider what your audience already knows about the topic and frame your data visualization around their current understanding of the subject matter so you can meet them where they’re at. Then adapt your narrative and visual elements to highlight the key message that’s relevant to the audience and elicit the desired actions. 

2. Identify a Compelling Narrative

Good stories have strong plotlines — and that’s no different when it comes to telling a data story with visualization techniques. For example, you may start with a hook (e.g., a question or a conundrum,) then take the audience through a journey that builds momentum and ends with arriving at a solution. 

Here are 5 main data-driven narratives and associated data storytelling examples:

• Trends: Trend stories typically focus on how numbers rise and fall over time. However, a flattening trend can also reflect key insights that invite the audience to look deeper. Here’s an example of a trends graph from MarketWatch.

• Comparison: By comparing different sets of data and how they change over time, you can analyze trends within a broader context. Here’s an example of a comparison chart from the BBC.

• Rank order: Also called a league table, it’s a useful tool for communicating hierarchy based on a variety of factors to make a large amount of data digestible. Here’s an example of a rank-order table from ResearchGate.

• Statistical Relationships: By exploring the correlation between sets of data, you can predict how one factor may impact another. Here’s an example of a statistical relationship chart from Pennsylvania State University.

• Counterintuitive data: Surprising or counterintuitive data stories and visualization can capture attention, pique curiosity and invite your audience to further engage with the content. This graph from Oppo Boppo is featured in a “Data is Beautiful” post by content marketing software company Scoop.it.

3. Incorporate Key Elements of Analysis Storytelling

To craft a convincing data story, incorporate these key elements:

• The plot: This involves the questions you’re addressing, how you plan to answer them and how you’re going to help the audience arrive at the conclusion. Your data visualization should take your audience from point A to point B in the most efficient and effective way possible.
• The setting: This is the context that will impact how your audience interprets the data. Examples include historical data, current processes and industry benchmarks. Meeting your audience where they’re at helps position your data and insight for the highest impact.
• The characters: The audience you’re addressing will determine how you tell the story. The story should adopt the right tone, address the audience’s priorities and explain how the insights can help them achieve their goals.
• The end: Reach a conclusion that’s congruent with the topic or challenges you set out to address. It could be a summary of what the audience has learned from the data story, how they can achieve results or what they should do differently in the future to improve outcomes.

4. Be Objective and Transparent

Your data visualization should present the facts in an unbiased manner, even if you’re using the data to support a specific point-of-view. Any untruthful manipulation, whether intentional or unintentional, can create inconsistency, diminish your credibility and reduce the audience’s trust in your information.

To present your data objectively, use proper labeling to avoid ambiguity, match graphic dimensions with data dimensions and ensure that the design elements aren’t compromising the data.

In addition, take an unbiased approach when selecting information for your data story. For example, don’t use discrete values when data is continuous, be clear about how you treat missing, outlier or out-of-range values and be transparent when you’re using arbitrary temporal ranges, capped values, volumes or intervals.

5. Choose the Right Data Visualization Method

Using a visualization method appropriate for your data can help display the information effectively to get your point through. Here are some common types of data visualization:

• Text: When working with a small dataset, you can use text to bring focus to the main message succinctly without ambiguity. Text also can be used in infographics to tell a more complex story and guide the audience to interpret the data.
  
• Line chart: It allows you to show changes in continuous data over a period of time. A line chart is an effective way to illustrate trends or linear progression within a dataset. Here’s an example of a line chart from Google.

• Bar chart: Thanks to its simplicity and familiarity, a bar chart is one of the most used methods for visualizing categorical data. Different types of bar charts (e.g., vertical, horizontal and stacked) can be used to suit the nature of your data and illustrate your idea. Here’s an example of a bar chart from WordStream.

• Table: Useful for displaying a number of categories simultaneously, it allows the audience to take in multiple levels of information at a glance. It’s also useful for presenting data to different stakeholders at the same time (e.g., at a corporate meeting.) 

• Map: It shows data spatially to illustrate concepts such as trends across geographic regions or a location’s impact on outcomes. Here’s an example of a data map from CNN.

6. Follow Visual Design Best Practices

Regardless of the visualization method you choose, simplicity and clarity are key to focusing your message. You should help your audience achieve maximum understanding at a glance by reducing the friction involved in reading and interpreting the graphic elements.

Use white space strategically to guide the readers’ attention, eliminate visual elements that don’t add value to the story and use bold colors to highlight data central to your thesis. Plan for visual hierarchy when deciding on the placement of the information. Most people in Western culture read in a Z-pattern. By incorporating this behavior into your design, you can help the audience better follow the information.

You also can use design elements to reduce the cognitive load so it’s easier for the audience to engage with your content and absorb the information. For example, by staying consistent throughout the presentation, using familiar visualization methods, paring the information down to the essential and priming the audience with the appropriate context or background information.

7. Use the Right Data Visualization Tools

Using the appropriate software can help you effectively visualize information for your data stories.

Here are some of our favorite free data visualization tools:

• G-Suite: You can use Google Sheets for plotting and Google Slides for annotation. In addition, Google Data Studio offers useful features for time series visualization and integrates with Google Analytics. 
• Microsoft Power BI: This software allows you to unify data from many sources to create interactive and immersive dashboards or reports.
• Leaflet: This is an open-source JavaScript library that can be incorporated into your data visualization framework for building interactive maps.
• Tableau Public: You can download the full version of this software for free, under the condition that everything you create with the tool is made public through Tableau Gallery.
• Datawrapper: The same tool used by journalists in leading publications, this software allows you to create charts, maps and tables from complex datasets.
• Open Refine: This platform goes beyond data visualization by cleaning and transforming data, as well as extending it with web services and external data.

8. Make Your Data Story Human and Insightful

Good stories are relatable. By acknowledging your audience’s needs and emotions, you can effectively engage them with the right level of detail and elicit the desired actions. For instance, you can illustrate abstract numbers and insights with real-life scenarios or a personal story to make an idea more concrete.

Good stories are also insightful. As most people struggle with information overload, delivering high-value content can help you cut through the clutter. For example, after reading your data story, will the audience be able to get the answer to their questions, make an informed decision or improve an outcome?

To capture your audience’s attention and keep them engaged, your data story should focus on a single concept and deliver unique insights that are useful and valuable to your audience. Remember, it isn’t just about the data, but how the insights can create an impact on your audience’s lives.

9. Create Synergies Between Data Storytelling and Content

Data analysis storytelling is a great complement to many types of content because it supports your ideas with facts while increasing audience engagement with visual elements. You can incorporate data visualization into articles, white papers, reports, ebooks, videos, presentations, infographics, social media posts and more to increase the credibility of the content and position your brand as an authority. 

Your data visualization graphics should be self-explanatory so they can either be embedded in a content piece or used as a standalone image (e.g., on social media.) The design should align with your brand image to deliver a consistent user experience. In addition, you can use interactive data visualization, such as animation, maps or word cloud, to add interest and increase engagement.

Also, tap into the power of social sharing to reach a larger audience and drive traffic to your content. You can ask your readers to share the data visualization on social media and make it easy for them to do so by including social share buttons on your website and a call-to-action in your posts. 

Data Is Everywhere — and Your Story Awaits

Data visualization and storytelling with data is an indispensable tool for augmenting internal and external communications. It can support your ideas with facts and add credibility to your content while engaging your audience to increase information retention and inspire action. 

If you’re wondering where to begin, the good news is that data can be found everywhere you look thanks to the many technologies we have at our disposal to collect information from various sources.

You can tap into internal sources such as website analytics, customer research, insights from different departments (e.g., sales, product development, human resources) or get inspiration from external sources including industry forums, Google Trends, studies published by think tanks, reports from research organizations and more.

By combining trusted data sources with a compelling narrative, unique insights, an understanding of your audience’s needs and effective data visualization techniques, you can craft engaging data stories to support your idea and enhance your content.