Type of data journalism
In 2017, Simon Rogers, Jonathan Schwabish, and Danielle Bowers published a report on the state of the field1 of data journalism. The findings showed data journalism outputs generally fell into three categories: investigative journalism, stories that explain data, and stories that are enriched by data. As in 2021, the largest share of journalists tend to work on stories where data is used to support the narrative (58%). Investigative data reporting and data explainers are instead common practice for two out of five data journalists, respectively.
Analysing data is carried out by three out of four data journalists, followed by data gathering (62%). Just over half of respondents (54%) create data visualisations as part of their job, and less than half clean data (45%). Programming and technical skills such as web design and developing are much less common, with less than one in four coding for work (23%).
By gender, women are underrepresented in most tasks, partly due to selecting fewer answers. While around half (49%) carry one to two tasks, the share of men that does so is much lower (41%). Non-binary / genderqueer respondents are overrepresented among coders (29%), those who clean (57%) or visualise data (67%).
Earning a higher salary means being more likely to carry out any of the tasks listed. The areas with the largest gaps between bottom and top income earners are web design, data cleaning, and developing applications. Top earners also disproportionately chose “Other” as an answer, suggesting additional types of tasks in their work routine (e.g., managerial tasks, teaching, reading).
Around half of data journalists cover stories of national interest. We also find that the same share of respondents work in organisations with national geographic scope. For international and local scales, we found that there are more data journalists producing stories with this geographic focus than there are working in organisations of the respective scope. In fact, in instances of a mismatch between personal and company geographic scope, six out of ten reporters work in a company with national coverage. Among those, the largest number (75 respondents) works on stories of international calibre. On the other hand, 51 reporters cover national stories while working in an organisation with an international scope.
More than half data journalists cover Government and Politics (53%), confirming it for the second year in a row as the most common beat type in data journalism. It is closely followed by the Environment (46%) and the Economy (42%). Compared to last year, there has been a decrease in the number of respondents covering Health (-5 percentage-point difference), suggesting a slowing down in reporting on the pandemic. On the other hand, a wide range of smaller beats is gaining territory in data journalism, with Climate, Energy, Education, and Opinion at the top (a three percentage-point difference each).
While the highest share of data journalists are specialised in covering one beat (20%), 40% of respondents cover between two to four beats. The remainder 40% covers an astonishing five beats or more. We find Technology, the Environment, Business, and Politics comprise a larger share of coverage at international organisations. On the opposite end Crime, Education, Agriculture, and Opinion are beats that appear relatively more in companies with a local focus.
The big three in terms of publication mediums for data journalists are all digital-based: nearly half of data journalists work at an online-only digital outlet, while 39% work at print or broadcast media outlets with a digital site. 29% publish their work on social media. Only one in seventeen works for print-only newspapers or magazines, while on average one in ten works in TV or radio.
The majority of respondents (56%) work for one specific medium. Combining social media with online-only outlets or with outlets with a print / broadcast format as well as a digital presence were the most common multiple-answer combination we found.
By far the most used type of data by data journalists is public official governmental data (71%). Just about one in five has used FOI obtained data in 2022. Retired data journalists and full-time employees are those who tend to work with more data types, with students on the opposite end. The narrowest gap between students and industry can be found with crowd-sourced data. Educators are the ones who work with survey data the most, while both full and part-time freelancers use more social media than the average.
More than half of respondents working in Mexico stated they have used FOI data in 2022, followed by Sweden (45%) and the United Kingdom (40%). A reflection of the fact that Canada’s and the United States’ Census data was released in 2022 and 2021, meant that they top usage of this type of data: nearly three in four data journalists in Canada worked with Census data in 2022, while the figure was 63% for the United States. Pakistan was the least likely country to work with public official governmental data (53%), a reflection perhaps of the quality and easiness of access to this type of data in the country.
Income positively correlates with working with any of the data types listed, particularly hard-to-get types of data such as scraped data and FOI obtained data, whereas social media data is the most evenly spread out across income groups. The gap between usage of public official governmental data and other data types does not vary across respondents with different years of experience in data journalism: as with income, usage of each of the data types positively correlates with years of experience.
Dedicated data unit
The largest share of data journalists does not work in a dedicated data unit (34%). Those who do are one in four, and their unit size tends to be small, with two in three being in a team of less than five people. Dedicated data units exist predominantly in large companies, but even in organisations of 500+ employees they are uncommon (36%). The few large data units are mostly found in companies of this size, while smaller units are more evenly spread out across company size.
As in 2021, we found that most stories (69%) are produced between a week and a month or more. Only one in ten produced a story in a day or less. Despite being insignificantly small, we saw a shift towards quicker produced stories this year. Three out of four respondents share their projects with five people or less, and only 8% work in teams of six or more.
As last year, collaborations are relatively uncommon in data journalism, as only 26% have worked with other news companies / organisations in 2022. Among those, the largest share (43%) involved separate content production. In terms of duration, 56% were a one-time collaboration.
- Rogers, Simon, J. Schwabish, and D. Bowers. “Data journalism in 2017.” Google News Lab (2017).