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This article was written by Zhang Hua (Zhang Wen), is a senior founding member of the Data Literacy Association and writes a column for EJTech
Today, data has become one of the most valuable business assets. If you search the web, you will find that the term “data is the new oil” is often used to describe the great value of data in today’s world; however, raw data itself is useless, and the real value is realized when data is collected and integrated every day, and connected with other relevant and important data. Companies that can effectively transform data into insights and insights into knowledge will be smarter and better than their competitors. In this data-driven world, data storytelling is a powerful tool that can help organizations succeed because we believe that innovation is distributed to everyone in the organization.
In an effort to demystify data storytelling and make it more than just an empty buzzword, it is often positioned as an extension of data visualization. Furthermore, the narrative aspect of data storytelling is largely overlooked or seen as a simple supporting role to the visuals. While many advocate the merits of data storytelling, few can explain how and why it works.
Three basic elements of data storytelling
Data storytelling involves the clever combination of three elements: data, visuals, and narrative. Data is the main building block of every data story and should be the basis for narrative and visual elements; however, organizations often face the challenge of data silos, with different versions or time series of data located in different locations, such as the cloud, on-premises, or even on a colleague’s own hard drive; data needs to be integrated and appropriate quality control measures should be established for everyone in the organization.
Visuals are how you give your data a voice. When applied to data, they can reveal insights that would otherwise be hidden in the rows and columns of a table of data. Many interesting data patterns and anomalies would go unnoticed without the aid of a chart or graph.
In the end, narrative is about crafting a compelling storyline that engages and entertains your audience, as well as visuals. A great example is a movie that combines computer-generated special effects with an engaging storyline. When you combine the right data, narrative, and visuals, you create a data storytelling that can influence and drive your audience to action. You might be wondering what data points can prove this statement, which I will explain in the next paragraph.
Why we need data to tell stories
The need for storytelling with data becomes apparent when we look at a study conducted at the University of Colorado in the late 1980s. The researchers wanted to explore how storytelling can influence jurors’ perceptions of the evidence presented. They focused on a real 1983 Boston bar fight that resulted in the death of one man. There was a dispute as to whether the man was acting in self-defense or was involved in intentional homicide. When study participants heard the evidence presented in the actual trial, 63% believed it was intentional homicide. However, when the prosecutor presented the same evidence in story form, 78% were convinced he was guilty. On the other hand, when the defense attorney shared their evidence in story form, only 31% believed the man was guilty of murder. This mock trial demonstrated the powerful persuasive power of combining data and story to strengthen the argument for the guilt or innocence of the defendant. Ultimately, you want the narrative to support your opinion. As humans, we are reluctant to accept hard data and facts that go against our will. This reluctance makes it difficult to change someone’s mind. In Thinking, Fast and Slow, Kahneman (2011) explains how the human brain processes information. The human mind is made up of two cognitive subsystems. System One is fast, intuitive, and subconscious, acting as an autopilot that filters information to the next system, which we call System Two. System Two is slow, analytical, logical, and conscious, acting as a driver that monitors and evaluates the quality of information coming from System One. This system makes us reluctant to accept new things or changes based on data points alone. Interestingly, the art of storytelling pairs perfectly with data. Great stories have the power to change the world and connect people. From a young age, we learn about the world and ourselves through stories. Great stories can transcend time and allow us to experience similarities between ourselves and others. This is what we call resonance, and it helps to tap into System Two.
Effective Data Visualization
When it comes to effective data visualization, it is important to define the problem in four areas: comparison, relationship, composition, and distribution. For comparison problems, a bar chart may be an appropriate choice. For comparisons over a series of time, a line chart is recommended. If the problem involves a relationship, a bubble chart is a good choice. Histograms are suitable for distribution problems, while pie charts, treemaps, or stacked area charts can be used for composition problems. However, choosing the right visualization can be a challenge as it may vary for different configurations of the same data set. It is recommended to seek feedback from a sample audience before the actual presentation to ensure clarity and effectiveness.
Create a clearly structured narrative
Finally, data storytelling is an advanced presentation skill that often involves public speaking. To create a clear and organized narrative, storytellers should strive to engage the audience. Start with an interesting point that grabs the audience’s attention and provides a reason for them to invest their time. This could be a significant spike in a key metric or an unusual number you found. Then, dig deeper into the analysis, like peeling an onion layer by layer to discover more insights. The audience is usually interested in understanding the why behind the data. This involves exploratory and explanatory analysis. Ultimately, the storyteller should present a problem statement followed by a recommendation supported by the data. It is important to acknowledge any risks involved and outline the next steps. Finally, a Q&A session can be included to answer any questions.
- background
- Interesting
- Exploratory analysis
- Interpretive Analysis
- Problem Statement
- Data-backed recommendations
- risk
- Next steps
- question Time
By following this effective narrative structure, data storytellers can create engaging presentations that are easy for audiences to understand and, as a result, drive change.
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