I try to strike a balance in providing a simple, accessible takeaway without dumbing it down too much. When writing about COVID-19, I have found that readers can handle very complex topics. If I want to go in-depth on how hospitalization data works in the United States, then people are going to read it and engage with it, but some readers will still stop at the headline or after the first few lines of the story. You have to think about how you structure your work using those classic journalistic principles of writing an engaging lead, clear graphs, and more that all apply when writing a data story. And for those readers who are interested in the more complex, or are interested in knowing how you arrived at your conclusions, sharing your methodology, your source information, and acknowledging the caveats of the data or the analysis are all incredibly important and helpful.
Just like traditional journalism, delivering an effective data story is all about answering those who, what, when, where, why and how questions within your methodology. You should be able to identify the source data, explain where it is coming from, and what gives the data credibility. For example, if it's coming from a scientific paper, then you should investigate what institution it came from and if they have expertise on the topic you are covering. Then you need to share what you did with the data? Did you do an analysis or are you just presenting what exists in the data? Did you select a specific column or a specific field for some reason to present? You can even make a case for why that field seems the most important or why it might be the most relevant to your story. You also need to investigate how old the data is from, if anything is missing, any major caveats that you need to address.
One vital component of a successful data story is that the data shapes the overall narrative, not the other way around.
When delivering an effective data story, journalists must find the balance between complexity and simplicity to engage readers from all backgrounds.
When responding to critical feedback, data journalists must have the ability to differentiate between concerned readers asking honest questions, qualified experts giving good-faith feedback, and those trying to spread misinformation.
About Betsy Ladyzhets
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