What is the difference?
Data alone doesn’t necessarily tell a story, or better yet, isn’t appealing to look at unless you’re a numbers person. However, even then, data should draw you in, show a trend, reveal something about what you’re looking at on screen or paper. Enter data storytelling, but like data that supports, it too is not the end all. This blog is about data storytelling and how it can be persuasive. However, before we get into it, we need to ensure we’re using common terminology. The terms data analysis, data storytelling, data visualization, and infographics are sometimes used interchangeably. Let’s define each term for clarity.
Data Analysis
Data analysis examines the data and interprets what the numbers are showing to help drive decisions. Essentially, it means looking at the number side of data, running prediction models, and determining what the data shows. During the analysis a pattern or trend is revealed as outcomes or findings. So data analysis shows “here are the numbers and this is what their telling us.”
Data Storytelling
Data storytelling takes it a step further, building on the data analysis. Based on the outcomes presented, a narrative is crafted to engage the audience through emotional connection. Through the narrative and emotional connection data storytelling employs one of two types of visualization methods to show the trend and influence decision-making; one is data visualization, the other is infographics.
Data Visualization
Data visualization is a subset of data storytelling that supports the narrative. Tools like Datawrapper, Tableau, and PowerBi, allow a designer to create visually appealing charts, graphs, and maps based on the data they input. Therefore, without the narrative surrounding the visualization, it’s simply text and numbers on a chart, graph, or map. Sure you can gain insight and see patterns or trends, but there’s no anchor, no emotional tie to the data. During her webinar How to turn data into stories, Cole Nussbaumer Knaflic (Founder of Storytelling with Data), walked through taking disparate graphs and visualizing them into great graphs to support the story you are telling. Knaflic walked through a scenario step-by-step and showed how making deliberate changes to graphs they can be improved.
Infographics
Infographics are yet another way to visualize and support the data story. The difference between data visualization and infographics is that the latter is usually presented in a poster format with images, text and narrative together. Essentially an infographic can stand alone and convey the story. Take a look at the COVID-19 Infographic below from the CDC showing how the virus is transmitted.
CDC Infographic – How COVID-19 Spreads
Benefits of data storytelling
Now back to data storytelling. Pratibha Kumari, Chief Digital Officer at Data Thick, said in her LinkedIn article Data Storytelling & Data Visualization there are definitely benefits to data storytelling, which include better communication, improved decision-making, increased engagement, greater impact, and enhanced data storytelling skills. She goes on to say “By using data to tell stories, we can convey important insights and information in a way that resonates with people on an emotional level” (Kumari J., 2023).
The Microsoft Power BI article, What is data storytelling? discusses the benefits of data storytelling. Among them are adding value to your data and providing insights into complex information. Other benefits include interpreting complex information, highlighting essential key points to your audience, and putting a human touch to your data. Additionally, you offer value to your audience and build credibility as an industry and topic thought leader. The article goes on to discuss the importance of ensuring your data is valuable and staying objective about your data. That is, you want to look for both data that supports your theory and data that doesn’t support it. The goal is to add data that helps guide your narrative without bias.
Another important point in the Microsoft article is to ensure your data story points to where it can help support an action or bring about a change. During your story, you’re helping to unveil patterns and trends while providing context insight, streamlining the data processed by your audience, and ultimately gaining or improving your audience’s engagement. According to the article, data storytelling has three vital elements: building a narrative, using visuals to provide that “ah-ha” moment, and showing the supporting data to create an emotional connection and response with your audience.
“Sometime reality is too complex. Stories give it form.”
― Jean Luc Godard
The elements of data storytelling
In a short video for the Harvard Business Review, Telling Stories with Data in 3 Steps (Quick Study), Scott Berinato, author of Good Charts, discusses the power of telling a story using your data in this. Berinato breaks it down into three easy steps: Setup, Conflict, and Resolution. Much like traditional storytelling for books, he demonstrates how data has those three elements as well. He shows how the setup is the “before state” of the data. The conflict is “how the data changes,” and you can ask what is the cause? Finally, the resolution is the “after state” or what the change creates as the “new reality.” Berinato goes on to show how you break down a chart to show the different parts of the story. Each story chart should have descriptive titles based on its data and not be generic. In his example about the global housing market, he shows how to break the data down and tell where the story is.
3 Examples of Effective Data Storytelling
It’s one thing to discuss all the key components of what makes data storytelling persuasive, however its another to show examples. Below are three very different examples of how data can frame a narrative and help tell the story. The story doesn’t always drive a call to action, but can help bring awareness, level set preconceived ideas, or bring a dose of reality to a particular topic.
Example 1: Great Storytelling through Visualization
In his TED Talk, The Best Stats You’ve Ever Seen, Hans Rosling describes data and explains the importance of data through data storytelling. One point he makes is that looking at the “average” data doesn’t help see what the data is honestly telling. His use of data storytelling was powerful and at times humorous, but importantly, it drove home the the importance of data and the ability to visualize and make better decisions. At one point he used South Africa as an example. In the example, he shows how even within Africa, the data can be disparate between neighboring states such as Uganda, Niger, and South Africa. When looking at the quintile sets, the data tells a different story. Therefore, we must look at “raw,” not aggregated data, when telling a data story. This helps to keep your narrative unbiased. Rosling finishes by saying that access to all data worldwide should be consumable for everyone to gather, animate, and determine the story being told by the data.
Example 2: Visualize NBA High School Drafts
This is an interesting example of telling a story using data. In the article How Many High School Stars Make it in the NBA?, the authors wanted to find out “are these top 100 recruit lists any indication of making it to the NBA, let alone becoming a star?” (Thomas & Samora, 2019). Through the use of data they were able to determine 92% of those ranked and drafted continued their careers in the NBA. On the flip side, looking at the unranked players, 73% of them continued their careers with only seven making it to Superstar Status, like Stephen Curry.
Example 3: Perspective on Gun-related Deaths in US (2018)
This example is a little different in that there is very little narrative text until you dive deeper in the section below the fold labeled What This Data Reveals. From the start it is clear the data itself is the story. The data visualization is interactive and allows us to view more or less data, depending on what we the audience want to see. As we look at the data, we see the total number of deaths in the upper left. However, what’s interesting is the stolen years number shown in the graph. This number is a cumulative estimate based on how long each of the victims may have lived had they not been shot (see image). The data lines themselves are interactive and show the ethnicity, age, and gender of each victim. Additionally, the data shows when, where, who, and how the victim was killed, and an approximation of how long they might have lived (see image).
Conclusion
In conclusion, data storytelling is a powerful tool for persuasion because it goes beyond mere data presentation to create a narrative that resonates emotionally with audiences. By crafting a story around data analysis, it helps to engage people, making complex information more relatable and memorable. This approach not only highlights essential trends and patterns, but also provides context that aids in decision-making. Through the use of data visualization and infographics, data storytelling adds a human touch to data, making it more accessible and impactful. By employing key elements of traditional storytelling, such as setup, conflict, and resolution, data storytelling maintains objectivity while creating an emotional connection with the audience. This method can enhance communication, improve engagement, and ultimately influence decision-making by providing insights that are both informative and emotionally engaging.
Resources
Berinato, S. (2019, October 30). Telling Stories with Data in 3 Steps (Quick Study). https://www.youtube.com/watch?v=r5_34YnCmMY
Cote, C. (2021, November 23). Data storytelling: How to tell a story with data. Havard Business School Online – Business Insights Blog. https://online.hbs.edu/blog/post/data-storytelling
Dykes, B. (2024, July 2). The future of data storytelling is augmented, not automated. Forbes. https://www.forbes.com/sites/brentdykes/2024/02/27/the-future-of-data-storytelling-is-augmented-not-automated/
Kesari, G. (2024, January 17). The enduring power of data storytelling in the generative AI era: Ganes Kesari. MIT Sloan Management Review. https://sloanreview.mit.edu/article/the-enduring-power-of-data-storytelling-in-the-generative-ai-era/
Kumari J., P. (2023, May 3). Data Storytelling & Data Visualization. LinkedIn. https://www.linkedin.com/pulse/data-storytelling-visualization-pratibha-kumari-jha/
Mattison, R. (2023, March 14). Data storytelling: How to tell a great story with data. ThoughtSpot. https://www.thoughtspot.com/data-trends/best-practices/data-storytelling
Nussbaumer, C. (2023, October 25). How to turn data into stories. Storytelling with Ddata. https://www.youtube.com/watch?v=Hfx1X9WSGYQ
Periscopic. (2019). United States Gun Death Data Visualization by periscopic. U.S. Gun Deaths. https://guns.periscopic.com/?year=2018
Rosling, H. (2006, February). The best stats you’ve ever seen. Hans Rosling: The best stats you’ve ever seen | TED Talk. https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen?subtitle=en
Team, P. B. (n.d.). What is data storytelling?. What is Data Storytelling and Data Storytelling Examples | Microsoft Power BI. https://powerbi.microsoft.com/en-in/data-storytelling/
Thomas, A., & Samora, R. (2019, February). How many high school stars make it in the NBA?. The Pudding. https://pudding.cool/2019/03/hype/