State of Data Journalism Globally
First insights into the Global Data Journalism Survey
Data journalism is an emerging discipline that brings together knowledge from several disciplines, including journalism, social sciences, information science, data and computer sciences, data analytics, information design, and storytelling.
To study the current state of data journalism globally, and the associated best practices, myself and Mirko Lorenz ran the 2017 Global Data Journalism survey earlier this year.
In this post, I present the initial results of the Global Data Journalism survey, which I wrote about and presented at the European Data and Computational Journalism Conference in 2017. A summary of the results is published in the conference proceedings as well as in Springer’s Lecture Notes in Computer Sciences.
To cite: The most complete account of the results is published in the Journalism & Media journal. This paper is open access and should be used for academic citations.
Method
The survey was launched on the 3rd December 2016 and closed on the 10th May 2017. It was open to all data journalists and journalists globally, but was limited to those who identify as having worked as a journalist or a data journalist in the past year. In addition, as a qualifying but not mandatory criteria, we asked if journalism or data journalism formed a significant part of participants’ income.
The survey was carried out using the online Google Forms, and was circulated and promoted as broadly as possible through various platforms and channels. A link to the survey was distributed widely through social media channels and relevant listservs, two Slack groups — News Nerdy and DJA 2017 — and a number of articles about the survey featured in the media such as on Data Driven Journalism, Deutsche Welle Innovation blog and Silicon Republic.
The survey consisted of 48 questions in 7 sections.
Findings
Two hundred and six participants from 43 countries participated in this survey between 3rd December 2016 and 10th May 2017, with 181 respondents filling it out to completion. Considering the small community of Data Journalism we believe this is a considerable sample size. In terms of gender balance 57.5% of our participants identified as male and 42.5% as female.
Participants of the Global Data Journalism survey — 43 countries
Of all participants 64% were in full time employment, 18% freelance, 12% part time and 4% casual/retainer.
32% of participants worked in large organisations of 500+ employees, 22% in organisations of size 10–49, 17% in organisations with 100 to 499 employees, 15% of these in small organisations of 2 to 9 employees and only 8% in mid-sized organisations of 50–99 employees.
42% of participants work in national organisations, 20% in local, 18% in International and the rest in a combination of these types, or other types of organisations.
Out of all participants 43% produce content for online platform of broadcast or print media outlet and 34% produce content for online only publications. This makes a total 77% of all participants producing content for online publications. This figure is followed by print newspaper (8%), Radio (4%), TV (4%), print magazines (3%), personal blog (2%) and producing content for news agency makes only 1% of the total.
In terms of experience as a journalist a majority of our respondents (78%) were individuals with 1 to 10 years experience as a journalist with breakdown of 2% having less than a year experience, 41% having 1 to 4 years experience and 26% 5 to 9 years. 19% of our participants have 10 to 19 years experience and only 11% have over 20 years experience as a journalist.
We asked our participants about the status of data journalism in their organisations. Forty-six per cent claimed that they have a dedicated data desk/team/unit/blog/section. This figure is followed by 29% who expressed that they do not have a dedicated data desk/team/unit/blog/section, but publish data driven projects on a regular basis. 7% of participants noted that they plan to work with data in the next six months and 7% expressed that they have no immediate plan to start working with data.
Of those who do have dedicated data desk/team/unit/blog/section, 40% have a data team consisting of 3 to 5 people and 30% have a team of 1 to 2 people. This means a vast majority (70%) of organisations with data teams operate with small teams of 1 to 5. On the other side of the spectrum 22% of participating organisations have data teams of 6 to 10 people and 3% have a team of 11 to 15 people and 5% have large data teams of more than 15 people.
While 86% of our participants consider themselves to be data journalists, in terms of data journalism proficiency only 18% rate themselves as experts in data journalism, while 44% of respondents identify as having a better than average knowledge in data journalism and 26% identify as having average knowledge in the field. 13% of participants identified as novice or below average level of expertise in the field. Half of or our participants (50%) had formal training in data journalism and the other half did not.
In terms of a wider understanding of formal training in knowledge areas used in data journalism, most of our participants demonstrate a high degree of formal training in journalism, while a lower and varying degrees of formal training in the more data oriented and technical aspects such as data analysis, statistics, coding, data science, machine learning and data visualisation. Figure 2 depicts the breakdown of formal training in various related fields between our participants.
Level of formal training in related knowledge fields
In terms of general education level, 96% of our respondents had a university degree, with a breakdown of 40% at undergraduate (bachelor) level, 53% postgraduate level and 3% with a doctorate or above degree. This shows that data journalism community is a highly educated community composed of 96% university graduates, 50% of whom have a postgraduate university degree.
Looking into the degrees obtained by these participants 62% are formally educated in Journalism at the university level. While Journalism is by far the most prevalent obtained higher education degree between our participants, it is followed by a combination of other degrees Politics (15%), Computer/Information/Data Science/Engineering (12%) and Communication and Language/Literature each 10.5%, with 26% listing a combination of other degrees. This shows that while most participating journalists have formal higher education training in Journalism, Communication, Politics and related degrees such as Literature, only 12% have higher education training in the more data related and technical topics. This further reflects on the basic underlying reasons behind the level of training demonstrated in Figure 2. It further denotes that formal training between the participants seems to have been mainly obtained through higher education and university degrees, and highlights the importance of including data related courses and modules in relevant higher education Journalism and Communication programmes.
In terms of values associated with journalism we asked our participants a series of questions, a number of which are briefly discussed in this section.
Sixty-five per cent of the respondents somehow agree or strongly agree that data journalism allows them or their organisation to produce more stories. On the end of the spectrum 13% somewhat disagree (10%) or strongly disagree (3%) with this statement.
Moving on from quantity to quality, 90% of respondents agree somewhat (21%) or strongly agree (69%) that data journalism adds rigour to journalism, with only 5% expressing the opposite.
Similarly 91% agree or strongly agree that data journalism improves the quality of journalistic work in their organisation, with only 4% believing the opposite.
Tapping into traditional journalistic values, while leaving the definition of these values to the participants, 83% of participating journalists disagree somewhat or strongly disagree that data journalism undermines traditional journalistic values, while only 11% agree somewhat or strongly agree that data journalism is undermining these values.
On a final note, 70% of participants expressed that they will not be able to carry out their work without data as a source.
Conclusion
Data journalism has evolved tremendously in the past few years, and is rapidly becoming an integral part of many newsrooms. The results of the Global Data Journalism Survey show that the data journalism community is a highly educated community, while it has its roots mostly in Journalism and Communication degrees, and less so in data/information and computer related disciplines. Additionally, journalists engaged in data journalism form a younger cohort of journalists, with less than 10 years experience as a journalist. While technical, data analytics and statistical skills do not seem to be the strength of participating journalists put next to their journalism background, it appears that many newsrooms already have dedicated data team and produce data driven stories on a regular basis. This study further reveals that despite debates in the use of data for producing journalistic work, both in terms of quantity and quality, a vast majority of journalists believe that data journalism allows them to create more stories in terms of quantity, which also are of higher rigorous and of higher quality.
This paper presented a small fraction of the data collected in the Global Data Journalism survey in a descriptive manner. A further, more detailed, and multivariate analysis of the results is yet to take place in the future.
Note: This article is a republication (with minor edits) of a number of academic papers.
To cite please visit the open access paper on the journal website.
