Data journalism

Data journalism or data-driven journalism (DDJ) is journalism based on the filtering and analysis of large data sets for the purpose of creating or elevating a news story.

Data journalism reflects the increased role of numerical data in the production and distribution of information in the digital era. It involves a blending of journalism with other fields such as data visualization, computer science, and statistics, "an overlapping set of competencies drawn from disparate fields".^[1]

Data journalism has been widely used to unite several concepts and link them to journalism. Some see these as levels or stages leading from the simpler to the more complex uses of new technologies in the journalistic process.^[2]

Many data-driven stories begin with newly available resources such as open source software, open access publishing and open data, while others are products of public records requests or leaked materials. This approach to journalism builds on older practices, most notably on computer-assisted reporting (CAR) a label used mainly in the US for decades. Other labels for partially similar approaches are "precision journalism", based on a book by Philipp Meyer,^[3] published in 1972, where he advocated the use of techniques from social sciences in researching stories. Data-driven journalism has a wider approach. At the core the process builds on the growing availability of open data that is freely available online and analyzed with open source tools.^[4] Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings. As such, data driven journalism might help to put journalists into a role relevant for society in a new way.

Telling stories based on the data is the primary goal. The findings from data can be transformed into any form of journalistic writing. Visualizations can be used to create a clear understanding of a complex situation. Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development. This connection between data and story can be viewed as a "new arc" trying to span the gap between developments that are relevant, but poorly understood, to a story that is verifiable, trustworthy, relevant and easy to remember.

^ Thibodeaux, Troy (6 October 2011), 5 tips for getting started in data journalism, archived from the original on 9 October 2011, retrieved 11 October 2011
^ Michelle Minkoff (24 March 2010). "Bringing data journalism into curricula". Archived from the original on 10 March 2020. Retrieved 6 October 2011.
^ "Philipp Meyer". festivaldelgiornalismo.com. Archived from the original on 4 March 2016. Retrieved 31 January 2019.
^ Lorenz, Mirko (2010) Data driven journalism: What is there to learn? Edited conference documentation, based on presentations of participants, 24 August 2010, Amsterdam, the Netherlands

[1] Thibodeaux, Troy (6 October 2011), 5 tips for getting started in data journalism, archived from the original on 9 October 2011, retrieved 11 October 2011

[2] Michelle Minkoff (24 March 2010). "Bringing data journalism into curricula". Archived from the original on 10 March 2020. Retrieved 6 October 2011.

[3] "Philipp Meyer". festivaldelgiornalismo.com. Archived from the original on 4 March 2016. Retrieved 31 January 2019.

[4] Lorenz, Mirko (2010) Data driven journalism: What is there to learn? Edited conference documentation, based on presentations of participants, 24 August 2010, Amsterdam, the Netherlands

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