By definition, this form of writing is based on analysing and filtering large data sets for the purpose of creating a news story. Some argue that this assists people understanding patterns in data so they can make informed decisions. Cynics counter by saying data could be misleading or omissions might occur to suit the kind of article needed to sell newspapers.
But what does data-driven journalism look like? It is more than simply an article containing stats and facts.
Getting important points across
Last week, PhD researcher at the Massachusetts Institute of Technology Erhardt Graeff published an article looking at the impact of data-driven journalism in Palestine. Of particular interest is his links to various data visualisations that show everything from politician's salaries across the Arab world and Africa to what happens to the human body when you go on hunger strike. This provides a clear example of how data can be packaged to get important points across quickly.
Such is the impact of data-driven journalism that The New York Times last year launched The Upshot, a politics and policy website designed to help readers better navigate the news using data, graphics, and technology. Pairing experienced journalists with graphics editors, the site is focused on providing analysis of the world in which we live.
This coming together of what was once seemingly disparate forces (newsroom versus design) is becoming more commonplace. Certainly, publications like Wired have long been trying to come up with more innovative ways of packaging content to not only differentiate themselves from their competition but also give their readers punchier content. However, the extent at which this is permeating the traditional news mainstream means readers, and the journalists themselves, need to get used to a more data-intensive reading (and writing) experience.
One of the bastions of journalistic training, Columbia Journalism School, published a fascinating piece on the Art and Science of Data-Driven Journalism. While you can read this at your leisure, the points come down to how data is becoming a strategic resource for media and tools are being released on a continuous basis that will continue to democratise data skills. It also makes the point that readers will expect more transparency on how data is collected and used further placing publications under pressure to insure the accuracy of information.
Given the real-time news cycle (thanks to social networking), editors can ill afford to make mistakes. Damaging editorial principles is one thing, but the impact on readers who have become notoriously brand 'disloyal' has the potential to make or break many publications.
Of course, in the monitoring and analysis world, this data-driven news structure also drives further innovation on packaging content more effectively. In a way, the very nature of media monitoring and analysis lends itself to agencies becoming data-driven content providers themselves. It is no longer good enough to send a client a report containing headlines, sentiment analysis, and other tables and charts. Today, they not only expect to have visual analysis done in a way that informs and provides insight at a glance, but also actually get insights that informs and guides strategy.
For monitoring and analysis agencies to truly be effective they need to find a balance between supplying infographics and offering insight that means the client can make informed decisions quickly and adapt communications strategy accordingly. To do anything less means the client will simply move to an agency that can meet this requirement. And given the ultra-competitive landscape, can you afford to lose clients because of that?