How are data visualization and data storytelling different?
Editor’s note: Harriet Jowett is the research director at Sapio Research. This is an edited version of an article that originally appeared under the title “What is data storytelling & why is it important.”
From detailed reports to eye-catching infographics, there’s no shortage of data trying to tell us something. But how do we make sense of it all? Two popular terms in this space – data visualization and data storytelling – are often used interchangeably, yet they serve distinct purposes.
Data storytelling and visualization both help translate complex information into digestible formats, but their goals are not the same. While data visualization is essential for displaying facts clearly, data storytelling takes a broader view, weaving facts into a narrative that engages and resonates with audiences.
This article explores the unique roles of data visualization and storytelling, highlights the added value of storytelling in market research and provides practical steps to turn research data into compelling narratives that stand the test of time.
What is data visualization?
Data visualization is the practice of turning raw numbers into visual elements such as graphs, charts and maps. This technique plays a fundamental role in modern communication by allowing complex information to be presented in a way that’s easy to understand. Effective visualization can make vast datasets accessible, helping audiences see patterns, trends and insights immediately.
The purpose of data visualization is straightforward: To present data quickly and clearly. However, despite its value, visualization has its limitations. It often lacks depth and context, and while it can show audiences what’s happening, it doesn’t always communicate why it matters or how it connects to broader issues. In many cases, data visualization is a single snapshot of a moment in time – a valuable tool for sharing insights but not a complete narrative on its own.
What is data storytelling?
Data storytelling goes beyond simply presenting facts. It combines data with narrative elements to build a story that engages and informs audiences. Effective data storytelling is not just about showing the “what” but revealing the “why” and the “how,” drawing readers into a richer, multidimensional experience.
Unlike visualization, storytelling doesn’t require graphics to be compelling. A well-crafted data-driven story can resonate even without visuals, relying on narrative techniques like context, character and setting to make data relevant and memorable.
Data storytelling is flexible too. It can be expressed in a range of formats, from articles and blog posts to podcasts, interviews and videos. By adapting to different mediums, storytelling offers versatility that enhances how data insights reach diverse audiences.
How are data visualization and data storytelling different?
While data visualization and data storytelling are both essential for communicating insights, they’re not the same.
Data visualization: Focuses on presenting information in a clear, concise way, showing the facts without embellishment. It’s about letting the data speak for itself through an accessible format.
Data storytelling: Incorporates visualization where necessary but adds a narrative framework, providing context and depth to the data presented. It invites audiences to explore why the information is important, how it connects to real-life issues and what it might mean for the future.
A common misconception is that data storytelling always requires visuals. While charts can enhance a story, they are not essential. For instance, imagine a simple bar chart showing changes in consumer spending over the past year. This visualization might illustrate the data accurately, but it may not convey why these changes occurred or how they reflect broader shifts in consumer behavior. In contrast, a story that contextualizes this trend – perhaps by discussing how spending changed due to economic factors – adds meaning that a standalone chart cannot.
Implementing data storytelling to market research
Data storytelling is particularly powerful in market research, where insights need to resonate with stakeholders, clients or the public. A well-crafted story can take thousands of datasets and statistics and transform them into an impactful and relevant narrative, bridging the gap between raw data and actionable insight. By translating complex information into a story, researchers can help stakeholders understand not just the numbers but the implications behind them, making it easier for decision-makers to engage with and act on the findings.
In market research, data storytelling can serve several purposes. It can be used to shape marketing strategies, seize emerging opportunities or in thought leadership and content marketing. For example, a data-driven story might take findings from a survey on consumer attitudes towards sustainability and show not only how these attitudes are changing but also how they align with broader environmental trends. This approach gives the audience a narrative that’s relevant to current discussions, making the data feel more meaningful and relatable.
Where data visualization might provide a clear picture of current survey results, storytelling brings the bigger picture into focus. It’s the difference between showing a static moment in time and guiding the audience to see a pattern, prediction or movement unfolding – a valuable distinction in market research.
Best practices for effective data storytelling
Creating an effective data story requires a thoughtful approach. Below are some best practices to turn research data into a compelling narrative.
Identify the core narrative
Before building the story, identify the main message or theme that the data supports. This core narrative serves as the foundation for the entire story and should connect with the audience on a personal or professional level.
Provide context and insight
Data is rarely meaningful on its own. By adding context – including industry trends, historical comparisons or case studies – you give audiences a fuller understanding of why the data is important. Context can transform raw figures into insights that have practical implications.
Understand the audience
Tailor the story to the intended audience, considering their level of familiarity with the topic. For instance, a story aimed at C-suite executives may focus on strategic implications, while one for general consumers may prioritize simplicity and direct relevance.
Choose the right mediums
One advantage of storytelling is its adaptability. Depending on the audience and message, data stories can be shared internally or externally through various channels and formats like blog posts, social media or podcasts. Choosing the right medium helps ensure that the story reaches the intended audience effectively.
Incorporate data wisely
While visuals can enhance storytelling, they should support – not drive – the narrative. And remember, you don’t even have to use charts.
By following these principles, companies, brands and individuals can craft data stories that engage and resonate, ensuring that insights are understood, remembered and acted upon.
Data storytelling and longevity: How to build lasting narratives using research
A significant advantage of data storytelling is its potential to build lasting narratives that evolve over time.Rather than being confined to a single report, a well-developed story can be revisited and enriched with new data, adding fresh layers of insight while preserving its core message.
For instance, when a company conducts a recurring survey, data storytelling can help track how attitudes or behaviors shift year by year. This approach allows the audience to see trends unfold, providing a deeper understanding of ongoing changes. By revisiting the same story with updated data, organizations can build a continuous narrative that reflects real-world developments, creating a dynamic and evolving insight resource.
This strategic longevity also makes it easier to repurpose content. You can then adapt these strong stories across platforms using new formats (turning a report into a series of blogs, infographics, posts, events or speaking opportunities) or refresh them with new data, extending the life of research findings.
In contrast, data visualization typically captures a single moment, making it harder to recycle or reframe in new contexts without a broader narrative. By focusing on storytelling, you can make your findings relevant well beyond the initial report.
Data storytelling in action
Consider a brand that tracks consumer attitudes towards sustainable products. If the brand relies solely on data visualization, it might produce a chart showing yearly increases in sustainable purchases. This approach would certainly convey the data but might miss the broader implications.
Instead, with data storytelling, the brand could craft a narrative showing how economic shifts, environmental events and media coverage influence consumer choices over time. By telling a story, the brand adds depth to the data, showing stakeholders not just the numbers but the forces driving change. This enriched narrative could be shared across various channels, allowing for updates as trends evolve and creating a long-term story arc that strengthens the brand’s connection to sustainability.
The importance of data storytelling
Data visualization and data storytelling are distinct but complementary tools for making sense of information. Visualization offers clarity, while storytelling provides depth and emotional resonance. In market research, where the goal is often to translate data into insights that drive action or generate media attention, storytelling is invaluable. It allows you to build narratives that engage, inform and inspire, turning raw numbers into meaningful stories that stand the test of time.
As we move further into a data-saturated era, effective data storytelling will only grow more important. By combining the precision of data visualizations with the power of narrative, you can create stories that not only capture the current moment but resonate far into the future.