A conversation with CSH journalist-in-residence Will Grimond
What if you could create a news story using data?
That was the starting point for a hands-on workshop led by Will Grimond, FRONTIERS journalist-in-residence at the Complexity Science Hub. In the session, held in July, Grimond offered CSH scientists a behind-the-scenes look at how newsrooms operate and how journalists craft compelling, data-driven stories.
From where journalists source their data to how they decide which issues to cover, the workshop explored the practical steps involved in turning raw data into a meaningful narrative. Participants even had the opportunity to create their own data-based stories.
So—where do data journalists get their ideas? What role can scientists play in the process? And how do you transform numbers on a spreadsheet into something that matters to readers? We also sat down with Will to talk about the evolving role of data journalism—and the many ways it intersects with science.
What is a typical day like for a data journalist?
A typical day usually starts fairly early. I begin by checking the news to see what the major stories of the day are likely to be. I’ll also look out for any new data releases, reports, or scientific studies scheduled to come out, and check my inbox—which is often full of press releases from various sources.
We have an editorial meeting around 9:30, where the team goes around and shares what they’re working on. It’s mostly about flagging things that might be relevant or interesting to others on the team.
After that, the day can vary a lot depending on what’s happening. Sometimes I’ll be juggling shorter pieces with longer-term investigations. Other days might involve editing a colleague’s story or helping them learn how to use certain data tools. It’s quite dynamic, and often shaped by the news cycle.
Can you walk us through the process of turning raw data into a story?
Sometimes you stumble across a great dataset you didn’t even know existed. I remember discovering that you could access data on the energy efficiency of every individual building in the UK, since they all have to be rated. Once you have a dataset like that, you can analyze it from different angles and pull out key insights. In that case, I used the data to write a story about how school buildings in the UK tend to be energy inefficient, and I combined that with figures on government spending to provide more context.
Other times, you start with more of a story in mind and go looking for the data to support it. For example, after a series of far-right riots in the UK last summer, we launched an investigation to find out how many people were arrested, what they were arrested for, and their ages. The events themselves were already big news, but there were gaps in the data that weren’t publicly available. So we had to go to individual police forces to request the information we needed.
In that sense, data journalism can work in both directions—either the data sparks the story, or the story drives you to dig up the data.
Where do you get the ideas for your stories?
There are a variety of sources. I often start with places where statistics are aggregated, like Eurostat or Statista, which can be great for spotting trends or gaps worth exploring.
I also get inspiration from reading the news. In science journalism especially, following a range of scientific publications can lead to story ideas that incorporate data in interesting ways. It’s important to keep your news consumption as international as possible—sometimes you come across an investigation in one country that could be translated or adapted to another context.
When it comes to data specifically, what really matters is novelty. That could mean a dataset that reveals something previously unknown, or a brand-new dataset that opens up fresh angles for reporting.
And often, when you receive a press release about a study or piece of research, the headline or main finding isn’t the most interesting part. Sometimes, what stands out is that the researchers have compiled a dataset you didn’t know was available—and that in itself can be the starting point for a compelling story.
Why do you think data journalism is an important development for journalism?
Data journalism is becoming increasingly important, and data literacy is now an expected skill for many journalists. Over the past few decades, traditional newsrooms have accumulated vast amounts of data, and journalists need the skills to work with it effectively. Whether it’s government finance, environmental issues, or healthcare, reporters must be able to break down complex datasets to do their fundamental work.
One interesting trend is how data is transforming sports coverage—football, for example, now involves an incredible amount of data that’s shaping how the game is analyzed and reported.
I also think large language models are lowering the barrier to entry for working with code, which is making more people feel comfortable experimenting with data tools. That shift will likely help data journalism grow even further as a branch of the field.
How do you see yourself working as a journalist in the next ten years?
I see myself continuing to explore and write about the intersection of technology and society—especially through features and investigations. I’m fascinated by how emerging technologies are applied in different contexts and what that means for people, systems, and the future. I’m pretty flexible in terms of format or setting, but I’m consistently driven by the questions these developments raise. That’s part of why I applied for the FRONTIERS fellowship—I’m excited by the chance to engage with scientists who are actively trying to understand and shape these changes. I’m especially interested in both the technical aspects and the broader implications.
What can scientists do to collaborate with data journalists?
I think it’s a really exciting area for collaboration. One of the most helpful things scientists can do is to share as much as they can—especially datasets in accessible formats. That’s crucial if a data journalist wants to create visualizations or conduct independent analysis.
It’s also important to be transparent about the processes behind the research. In data journalism, as in all journalism, we aim to add as much depth and context as possible to a story. Sometimes, details that might not seem significant from a scientific perspective can actually be quite compelling or revealing in a journalistic one. So being open—even with what might feel like side information—can really enrich the final piece.
