Unleashing the Power of Data in Journalism

Data in Journalism

Data in journalism involves treating numbers as sources to complement human witnesses, officials and experts. It can include anything from a sports journalist’s graph comparing on-base percentages to a tech reporter’s spreadsheet of sales figures.

Without the help of reporters, some stories would go untold, connections between powerful people or entities might remain hidden, and environmental policies hurting our world could persist.

How to get started

It’s never been easier to get started with data journalism. Thanks to the open data movement, there’s no shortage of newsworthy datasets available. Regulators, consumer groups, charities, scientific institutions and businesses all release data that can be used for stories. You can find it on government websites, on sites like What Do They Know or on organisations’ own disclosure logs.

Once you have the data, it’s important to understand how it was compiled and if there’s any bias in it. You can do this by using free online tools like TextWrangler and OpenRefine.

Aspiring data journalists should also try to find out as much about the people behind the numbers as possible. This will help to contextualise the data and give a human face to the story. In turn, this will help readers to engage with the information and make sense of it. In a world where misinformation is on the rise, getting this right could be the difference between an ill-informed article and one that is accurate.


The data used in data journalism can be found from a variety of sources. Charities, NGOs and unions often gather their own indicators of social or economic issues, such as homelessness or social mobility which can be visualised with maps and charts. Regulators and professional bodies also release their own statistics on a regular basis, as do universities. There are a number of data aggregators, including Knoema, Datahub and Data 360 which can be useful for tracking down datasets.

Journalists can also use Freedom of Information requests to request data in structured formats, for example XLS or CSV, although it might not always be received in that format. However, much of the raw data that journalists will need is not structured and may have to be manually reshaped in order to be useful for storytelling. This requires journalistic stamina to sift through often puzzling, sometimes boring, raw data and see the story hidden within. This data could be anything from the number of doctors per city district to the modal age of birth in a specific country.


A lot of journalists work with data to find and tell stories that matter. The skills and tools they use are vast, varied and complex. But the core tools that any journalist needs are a solid understanding of how to structure and write a story, and access to the right sources and digital technology.

The best digital journalism tools can be a journalist’s secret weapon, from apps that help them transcribe video and audio files to software that helps them understand the meaning behind charts and maps. They can help them create interactive stories that will captivate readers.

A good tool to use is Zenodo, which enables journalists to search for research data that has been made public through an Open Science depository. Journalists can also find a variety of mapping tools that help them turn raw data into engaging reports and infographics. These tools include Google Data Studio, Pinpoint and the Global Investigative Journalism Network’s map toolkit.


The close relationship between data journalism and technology poses challenges for traditional ethical values and formal codes of conduct. During interviews and workshops, expert data journalists emphasized the importance of transparency and open access to data. They also stressed the importance of ensuring that they are not biased by their data sources (who might be individuals or organizations) and that their conclusions are independent of those sources’ opinions or biases.

One promising trend in data journalism is the creation of sites that hold technology companies accountable for the impact their algorithms have on our lives—from decisions on jail sentences to prices charged based on zip codes. This is a form of data journalism known as “algorithmic accountability” (Angwin 2018; The Markup).

Another challenging trend is the use of personal-sensor data. This can reveal information about individuals that may violate their expectations of privacy. For example, it can reveal where they live, their work, or their social relationships.

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