What Is Data Storytelling and Why Is It So Important?

Data storytelling, the process of weaving together a narrative based on data, is becoming one of the most important skill sets for the modern professional.
Matt Talbot
Matt Talbot
CEO of Superchart
April 12, 2023
What is Data Storytelling and Why is it so Important

What is Data Storytelling?

For many, when they think about the power of data, they begin to imagine heavy duty number crunching and data scientists.

While it’s true that great data analysis requires some number crunching skills, data has its greatest impact when it’s appropriately framed in context to help drive decisions. 

This means that everyone - regardless of your formal data training or experience - has the opportunity to influence action and decision making by using data. 

This great primer from Harvard Business School provides a great introduction to data storytelling, but for those of you looking for the TL;DR, there are really a few important things to remember. 

First, data storytelling is about combining data, visualizations, and narrative in an effective way to clearly communicate to your audience. 

Second, data storytelling is not just about your latest marketing report for the executive team. Rather, data storytelling can be effective across internal and external use cases. 

Third, storytelling activates and engages many parts of the human brain, leading to a deeper connection between the creator and audience. This emotional response to stories can make it easy for your content to be memorable and acted upon.

How to Build a Great Data Story

A great data story first rests on a hunch or an insight that you have discovered. 

This first step requires that you have access to the information that you need and that there has been some sort of spark to kick off your analysis. This spark could be a question from a team member or manager, or it could just be a hunch that enters your mind. You could also discover a spark of insight during your regular reporting and analysis process that justifies additional investigation.

After the spark, the next step is to source and organize the data that supports the investigation. This means clicking into the specifics of your insight and organizing the information that either supports or refutes the hunch or question at hand.

Once your data is organized, the next best step is to take your data and create a compelling data visualization. A great data visualization can help easily communicate findings to others in a way that a table or text cannot. 

Now that you have your data and a great visualization in place, it’s time to begin to craft your narrative. A great story tends to have the following elements: premise, plot, characters, prose, and a theme.

For a data story, it’s wise to use classic story elements to frame your narrative. Setting the context, the key players, the plot, and the theme are all critical. Further, the “prose” of your story (aka the style of how you deliver your story) is critical as well.

Make sure you take the time to bring your characters and premise to life through rich descriptions and real, specific examples as often as possible. The more that your audience, whether a live group you are presenting to or a group that is reading your story, can get specific details, the more the story comes to life.

Finally, and arguably the most important aspect of a data story in the business world, is to make your recommendation or conclusion clear. Sometimes the recommendation may be more of a question than a statement, but, either way, you need to make sure that you wrap up the story in a clear and understandable manner.

Data Without a Story Falls Flat

It’s worth emphasizing that, especially in a day and age when we have overwhelming amounts of data at our fingertips, the only way to really cut through the noise and drive change or key decisions is to share data insights in a manner that is memorable and clear.

To frame the importance, let’s compare and contrast two situations. One where a data story is missing and one where a data story is present. To lay out these examples, we will start by creating a shared context for both approaches, and then highlight how different approaches to storytelling can change the impact of the same information.

Let’s say we are a marketing manager for a software company that just launched a new product that is being promoted with paid ads across LinkedIn. The paid ads were targeted at three different buyer profiles, analysts, associates, and directors, and have created new customers with a cost of $100, $200, and $300 per buyer profile, respectively. 

In our first, non-story driven approach, we can choose to build a simple spreadsheet showing the total ad spend and the cost per customer by each of our profiles. This will give management a clear, objective, and reliable view of how our ad campaign is going. It should be immediately clear - we assume - that our analyst buyer profile is the most cost efficient. However, what this simple spreadsheet does not do is explain why the analyst profile is working well and what we should do next.

As a contrast, we could choose to create a great data visualization showing the cost to acquire by buyer profile visually. This would make the same data much more immediately understandable and clear. Further, we could include a narrative about why we think that the analyst profile is working the best, for example, that our ad copy was most focused on “efficiency” which seems to resonate with analysts, where as a more “effectiveness” message may work for higher ups in the organization.

This is an extremely basic example, but it highlights how adding some basic visualization and text narrative can make the difference between a boring and intimidating spreadsheet report and an engaging and memorable data story. 

In the Near Future, Everyone is a Chief Analyst for Their Domain

Looking forward, we are entering the decade of data where putting information to use and making it accessible in an organization is of paramount importance. The democratization of data access and better tools for all types of end data users will drive a renaissance in how organizations are able to leverage their critical data resources.

Those that will get ahead over the next ten years are those that commit to being the Chief Analyst for their specific domain. 

Being the Chief Analyst of your specific domain may sound like a daunting task if you don’t come from a background full of number crunching and data visualization.

However, being the Chief Analyst certainly does not mean that you need any sort of technical data chops. You may need to rely on partners in your organization to help with data sourcing and clean up, but most great business insights rest on top of simple calculations, common sense, and clear priorities.

As the tools for data analysis and communication continue to improve, the bar for technical chops will continue to be lowered, creating massive opportunities for everyone to improve their critical thinking and decision making.

This presents a unique window of opportunity for those that are striving for excellence to get ahead and display leadership on their teams and in their organizations. The best steps that you can take to take advantage of this opportunity is to focus your next analysis on producing a data story instead of just a report. By giving it a try, you’ll see that it’s extremely effective and, importantly, not very difficult to do! 

In Conclusion

Data storytelling is already an important discipline in business, and over the next ten years, it will take center stage as one of the most important competencies for every professional. We hope our guide helped emphasize the importance of data storytelling, but also helped show that it’s accessible and straightforward with a few simple tips. 

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