In July The Data Lab Community hosted a member-led session with Georges Corbineau, a Data Visualisation Specialist at Moody’s Corporation to discuss how he and his team use data visualisation techniques to turn data into compelling storys that the reader can understand.
The creation of such impactful data-driven content demands a blend of time, dedication, and a diverse skill set. During the event, Georges Corbineau, an expert in data story production, provided a behind-the-scenes look at the process of crafting a captivating data narrative.
What is storytelling in data?
Data storytelling is the art of crafting narratives from data analyses, making complex insights accessible and influential for diverse audiences. By presenting data findings in a relatable manner, storytelling bridges the gap between intricate analyses and actionable insights.
In this follow-up guest-article to the event, Georges delves deeper into the art of data storytelling, exploring its significance and unraveling the key takeaways from his presentation. If you missed the event, you can watch the full recording below:
Unveiling Effective Data Storytelling Practices:
Imagine a data-story as a blend of journalism, storytelling, and data presentation. It uses tools like interactive charts and bespoke designs to grab attention and make information stick in our minds.
This format is designed to reach many people through social media and be understood by a wide audience. Journalists find it handy for references, while experts use it to simplify explanations. Plus, when data-stories are shared and talked about, publishers gain trust and credibility.
Collaboration in crafting with experts
The process begins when someone in the company comes up with an idea. The data visualisation team works with them, and the group includes:
- Data Visualisation Specialists: They make the article supporting the data visually appealing and easy to understand.
- Analysts and Research Writers: They ensure the data is accurate and meaningful. They also write the article while working hand in hand with the DV team to keep the content accessible.
- Communication Coordinator: This person handles the publishing timing and events within the company.
- Others: Sometimes, extra experts join to add more knowledge or technical help, which might be needed to handle the data if it’s coming from an external source.
Working together, they make a data story that’s meaningful and easy to get into.
Composing data messages
When they shape the story’s message, the group focuses on several things. They choose a message that’s interesting and likely to be shared. They make sure the content is easy to discuss and understand. Data supports the message, making it trustworthy. They also make sure the data is presented clearly and consider the work the data visualisation team needs to do. Finally, the DV team checks if it has time and resources available.
Starting the data storytelling journey
Creating the final data story has a few steps and a lot of iterations. The group turns the message into sections, using a shared document. On one side the analysts write the text, edits it, fetches the appropriate data, and shares their expertise with the DV specialist. On the other side, the DV specialist experiments with designs and explores different ways to present the data.
Final checks before sharing
After many iterations, before publishing, some final checks need to be done. The analysts share the nearly-finished version within the company to get feedback to make sure all the facts are right. The DV team checks that the content is accessible for everyone (colours, language, …), testing the project on different devices or even the overall loading performances. The communication coordinator checks if a press release needs to be prepared and syncs the project publication with a company event.
Once all this is done, the data story is accurate, accessible, and ready to be published!
To see how well the data story is performing after publication, the team look at a few things:
- How many people visit the data story content
- How long people stay on the page/content
- Where visitors come from, e.g. social media, news websites, etc.
- Track how many times others talk about, reference, or use the data story
By looking at all of this, the team figures out how much impact the data story is having and if it’s getting the attention they hoped for.
The Data Lab Community would like to thank Georges for his time and expertise.
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