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Storytelling with data visualization

Uncover both the difference and similarities of data visualization and data storytelling—and how they best work together to decipher your data into clear, concise, and actionable information for your organization.
A couple of professional talking on a topic and one is presenting some data on board

What is data visualization?

 

Data visualization conveys information through images—a visual representation of the data you’ve collected. By offering raw numbers and figures, it can help support better organizational decisions through charts, graphs, diagrams, spreadsheets, maps, and numbers.

 

There are two types of data visualization to explore when crafting your data story: explanatory and exploratory. Explanatory visuals, also known as informative, deliver specific aspects or the entire story to the audience. On the other hand, exploratory visuals are used when you’re unsure what questions you need to answer with the data you collected.

 

By using data visualization to tell your story, you offer a more complete story to your audience and, thus, allow them to connect correlations, recognize trends, and draw their own conclusion or form their own opinions. It’s important to note that data analytics presented in your visualization without contextual storytelling can lead to your audience prolonging a decision, coming to the wrong conclusion, or getting lost in the vast amounts of data.

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What is data storytelling?

 

Data storytelling is the concept of creating a narrative using the data and analytics you’ve collected that help support the hypothesis of your story. Like with telling a story orally to someone else, you present a cohesive narrative that provides a final message and action to take. Only with data instead of your voice.

 

Data storytelling uses several types of data—including scatter plots, geographic maps, timelines, line graphs, pie charts, bar charts, heat maps, and tree charts—to craft a great story.

 

But when creating your data story, you’ll first need to decide:

 

  • What do you want your users to know?

  • What do you want users to do?

  • What’s the narrative that’ll help drive that action?

  • And how is your data helping drive those decisions?

Some of the benefits of data storytelling are that it can:

 

  • Add value so users can connect the dots in your story.

  • Increase the credibility of your organization and build trust with the audience.

  • Combine an enticing narrative with visually stimulating elements so users can read and retain information.

  • Foster engagement with relevant data.

  • Provide proprietary data to create new and original stories.

  • Versatile so it can be incorporated into other forms of digital media.

  • Generate brand awareness to position your organization as an industry leader.

Why are data visualization and data storytelling important?

 

To build a compelling story, you’ll need to provide your hypothesis, reasoning, and data to back it up. Data is great, but too much can be overwhelming. Even data visualization specialists might struggle to give meaning to their visualization efforts without the proper context that a focused narrative brings.

 

The best approach to data storytelling is featuring the right data in the right visual format. If there are too many images in your story, it becomes more of an infographic, lacking the context needed to be engaging. When telling a story, you need to create a narrative and know when to bring in data, when to highlight complex numbers, and when to just offer a written explanation.

 

Data visualizations play a part in adding information to the narrative, but they don’t tell the whole story.

How data visualization and data storytelling work together

 

Data storytelling and data visualization may sound the same, but they are both very different. You can tell an engrossing story, but you need proof points to solidify your hypothesis. Raw data alone can’t provide an audience with the takeaway message or action. To spotlight your objective, you need to present your data in a compelling yet easy-to-understand format that engages your audience. Data-driven storytelling turns your information into an effective communication tool.

 

To craft a successful data story, enlisting data visualization helps put the pieces together and builds a narrative that your users will understand. By providing context, attention to details, critical insights, and a call-to-action, data visualization offers the data to back up your claims. At the story level, it creates an understanding and an explanation of the metrics you provided, way beyond numbers and charts.

The differences between data visualization and data storytelling

 

It’s best not to look at this as data visualization vs. data storytelling. But instead, how they work in tandem. Both are strategies in how you bring your concept, hypothesis, or theory to life. Yet, there are very distinct differences.

 

Data storytelling differs from data visualization because it requires that the narrators of your story deliver a more significant, encompassing view of your message in a way that your audience can easily understand.

 

On the other hand, data visualization is a tactic used to enhance your storytelling. This standard business communication tool takes vast amounts of complex and valuable data and translates it into something that people can more readily understand. To grab your audience’s attention, you need to use intentional visuals that are:

 

  • Appropriate

  • Legible

  • Not misleading

 

Data visualization and storytelling work together to help tell the best story possible, using data that helps bring clarity, truth, and validity to your narrative.

How data visualization and storytelling are similar

 

Data storytelling and data visualizations work together with the same goal in mind: to build an engaging, factual, and contextual narrative. They’re not interchangeable, but two sides of the same coin. The visualization provides the proof that your narrative needs, while the storytelling presents all your information as something logical and coherent.

Creating a data storytelling framework

 

When creating a data story, you need to ensure it has a beginning, middle, and conclusion. You know the story you want to tell and how you want to get there. Understanding the context and results of your data analytics are the pieces of information you’ll need to tell your story.

 

To achieve this, you’ll need to enlist some best practices to create the framework. Some practical ways to ensure your data visualization and storytelling are effective are:
 

  1. Define your objectives.
     

    Understand the purpose of your data story, audience, and priorities.
     

  2. Present a compelling narrative.
     

    Know what your audience wants to hear.
     

  3. Incorporate key elements of analysis storytelling.
     

    These elements include:
     

    • Plot: The types of questions presented, how you’ll answer them, and how you’ll help your audience arrive at the conclusion.

    • Context: How your audience interprets the data you present.

    • Characters: The tone and insights into how you’ll tell the story.

    • The end: The conclusion and subsequent actions resulting from your story—what the audience learned, how to achieve the results they want, or what they’ll need to do differently to improve for the future.
       

  4. Be objective.
     

    Present your data transparently and objectively, avoid ambiguity, and make sure your designs and visualizations don’t compromise the integrity of the data or story.
     

  5. Choose suitable types of visualizations.
     

    You’ll need to present your information appropriately with the right imagery.
     

  6. Follow graphic design best practices.
     

    So your audience completely understands, you’ll need to choose a presentation that reduces the most friction between reading and interpreting the graphical elements.
     

  7. Use the proper data visualization tools.
     

    To effectively communicate data through visualizations, you need to use the right tool. For example, Microsoft Visio can help bring data to light with digestible and clear imagery.
     

  8. Make an insightful and human story.
     

    If your story is relatable, engaging, and offers high-value content in bite-sized pieces, you’ll keep your audience’s attention.
     

  9. Create a story that complements both storytelling and visualizations.
     

    Adding supporting, self-explanatory graphics to conceive a consistent storyline adds interest and increases engagement.

     

Some data visualization storytelling examples are:

Data visualization and storytelling solutions

 

There are numerous solutions available to help you tell a full and enriching narrative with your data. But when deciding on a solution, you need to be sure it’s agile enough to work with your critical business data from across the organization so see the whole picture and gain even deeper insights.

 

With Microsoft Power BI, you’ll help bridge the gap between your narrative and data experiences. It will help you discover relevant content, uncover critical business intelligence, and collaborate and share data reports with your team with the right data visualization and storytelling business tool.

 

Learn more about Power BI

Frequently asked questions

  • Data visualization conveys information through images, while data storytelling creates a narrative with the data
  • By organizing the raw data into data visualizations, data storytelling helps create an easy-to-understand narrative that drives business decisions forward.
  • The data you’ve collected helps craft a narrative, provide supporting information, and supports a hypothesis that the data supports.
  • Data visualizations include scatter plots, geographic maps, timelines, line graphs, pie charts, bar charts, heat maps, and tree charts.
  • Data storytelling is a tactical way to tell your story, while data visualization is a tactic used to enhance your storytelling.

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