Data journalism has largely evolved in the last two decades, and continues to evolve from newsroom to newsroom across the globe.
Before the term data journalism came into existence, journalists and editors used another term — Computer Assisted Reporting (CAR). The Data Journalism Handbook, First Edition, July 2012, describes this process as “the first organised, systematic approach to using computers to collect and analyze data to improve the news.”
The first time newsrooms used CAR was in 1952 when CBS tried to predict the result of the US presidential election.
A few decades later, in the 1970s, the term precision journalism was used to describe a type of news-gathering techniques that used the application of social and behavioral science research methods. This practice was encouraged to overcome some of the gaping holes in journalism then, namely dependence on press releases or institutional statements, bias towards sources with authority, and so on.
An increasing number of readers wanted more information than just text blobs, and newsroom editors wanted reporters to provide it efficiently. That was how the term data journalism was born.
While it is said that people used data journalism techniques as early as during the Han Dynasty, a more realistic example is The New York Tribune article in 1849, which had chart to show the number of lives that were being lost to cholera at the time.
Though with the above examples we know that some sort of journalism was practiced across newsrooms, Guardian and The New York Times, brought the term into prominence when Wikileaks, in 2010, released airstrike footage and over 700,000 confidential documents pertaining to US operations in Iraq and Afghanistan.
The documents called ‘Iraq War Logs’ added 15,000 previously unknown civilians to the US public death count.
Julian Assange, founder of Wikileaks, calls the quoting and sharing of source material and data behind the story as one of the basic ways in which data journalism can improve journalism. He calls this “scientific journalism.”
There are many versions for the term data journalism, but Paul Bradshaw, a renowned data journalist, author and professor at Birmingham City University, keeps it simple and defines data journalism as “a journalism done with data.”
He goes on to say that there are three different stages at which a journalist can incorporate data journalism into the traditional news cycle: using programing languages to gather or mine datasets and information; using software algorithms to find patterns or connections between the data in the documents or datasets; and lastly, to tell a complex story with engaging infographics and visualizations.
In my next post, we can look at how data journalism is useful for journalists.