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How to Write Effective Data Stories

September 2024
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An analysis isn't complete once you get your results: the end of the project is when you've communicated those results and action has been taken on them. Writing about your results, whether in a simple message, an email or a report, is an important form of communication. In the second of three data storytelling sessions, a leading data journalist will teach you how to write about data. You'll learn how to tailor your language for different audiences, how to structure reports, and how to ensure that action is taken from words.

Key Takeaways:

  • Learn how to write clearly about data.
  • Learn how to adapt your content for different audiences.
  • Learn how to structure data messages and reports to maximise your impact.

Resources

Summary

Effective data storytelling combines narrative and data insights, underlining the role of storytelling in data-driven decisions. Professionals like Delphine Reuter and Lea Pica believe that while dashboards indicate potential stories, the storyline is essential to engage and persuade audiences. Delphine, from the International Consortium of Investigative Journalists, articulates how data stories can enhance journalism by providing context and evidence. Simultaneously, Lea emphasizes the emotional connection and narrative arc in storytelling to drive decision-making. They both concur on the need for structure, clarity, and emotional engagement to turn data from mere numbers into compelling stories. Additionally, they recognize the role of AI in data storytelling while emphasizing the necessity of human oversight for accuracy and impact. Adjusting the story to the audience's technical level and maintaining transparency are also essential elements in crafting effective data stories.

Key Takeaways:

  • Data stories require a narrative arc to guide the audience through the data.
  • Engaging storytelling combines emotional appeal with data insights.
  • Dashboards indicate potential stories but are not stories themselves.
  • Adjusting data stories to the audience's technical understanding is essential.
  • AI can aid in data storytelling, but human oversight is necessary for accuracy.

Deep Dives

The Role of Narrative in Data Storytelling

Data storytelling is more than numbers; it involves weaving a narrative that makes those numbers meaningful and engaging. Lea Pica emphasizes that a story needs a narrative ...
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arc, which includes a beginning, middle, and end, to guide the audience through the data in a structured manner. This approach contrasts with the typical data presentation, which often involves inundating the audience with information without context. By structuring a story with a clear conflict or challenge, data storytellers can hold the audience's interest and guide them towards a conclusion. For instance, using a narrative arc in a presentation helps decision-makers understand not only the data but the implications and necessary actions. As Lea puts it, "No conflict, no story," highlighting that the narrative element is what distinguishes storytelling from mere data presentation.

Emotional Engagement in Data Storytelling

Humans are inherently emotional decision-makers, and data storytelling leverages this by creating stories that connect on an emotional level. Lea notes that while data provides the foundation, it’s often the emotional appeal that inspires action. She explains that by identifying a "villain" in the data story – a problem or conflict – storytellers can create a sense of urgency and engagement. This approach is similar to sales strategies, where the emphasis is on the potential loss or missed opportunity if no action is taken. The narrative should make the audience feel that the recommended actions are a "no-brainer." This emotional connection is essential in ensuring that the story not only informs but also persuades the audience to act.

Transparency and Adjusting to Audience

Transparency and audience adjustment are vital in creating effective data stories. Delphine Reuter emphasizes the need to offer clarity on the methodology and limitations of the data used. This transparency builds credibility and trust with the audience. Additionally, understanding the audience's technical expertise allows storytellers to adjust the complexity of the story. Delphine shares that in journalism, it's essential to separate the main narrative from the supporting data, providing options for the audience to explore further if they choose. Similarly, Lea suggests customizing the story for different stakeholder roles, ensuring that the core narrative remains compelling while technical details are available as supplementary material. This approach prevents inundating the audience and maintains engagement.

The Use of AI in Data Storytelling

AI is increasingly becoming a tool in data storytelling, aiding in the processing and presentation of data. Delphine discusses how AI can assist in managing large datasets, allowing journalists to focus on crafting the narrative. However, she warns that AI-generated results require rigorous fact-checking to ensure accuracy and reliability. Lea adds that AI can also help in brainstorming and refining the story structure, offering new perspectives and analogies. Despite the potential benefits, both speakers agree on the necessity of human oversight to validate AI outputs, emphasizing that while AI can enhance storytelling, it cannot replace the critical thinking and creativity that humans bring to the narrative process.


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