Data journalism is more important than ever, but what is data journalism and why has it come to the forefront of newsrooms throughout the globe.
Data journalism is the gathering and analysis of data, followed by presenting it within a compelling story that audiences can digest.
Some critics argue all modern journalism is data journalism. After all, when a news story is printed, it should contain facts and those facts can be quantified as ‘data’. However, the term ‘data journalism’ brings to mind something more than traditional reporting.
As Aron Pilhofer, the visual data editor at The Guardian put it, data journalism “means something different to just about everyone”.
Most people can agree on one characteristic of data reporting; it is a type of journalism that interprets raw data and uses that information as a critical source for a story.
This point was discussed in an American Press institute article entitled ‘How data journalism is different from what we’ve always done’.
This article also points out how data reporting can be broken into three overlapping categories: acquisition, analysis and presentation.
In some ways, this is not dissimilar to how a journalist would work on most stories; first, find a source, then analyse the information being presented and then distribute this information in a way that’s easy to digest to the public. Although the steps may be similar to the work done in newsrooms, the processes involved are not.
Before reading on, check out our guide to the best journalism tools.
The initial acquisition of data is an essential part of any data journalist’s job. There are skills involved that don’t go hand-in-hand with what many people consider journalism.
For instance, the ability to find abnormalities within data sources, such as spreadsheets, is a far cry from interviewing whistleblowers and witnessing global events and reporting on them.
However, this skill can and has helped expose and display some of the most important stories that have happened during our time.
The New York Times published an article which looks at the value of knowing how to investigate a spreadsheet and how their journalists have learned to love the process, which helps them find and engage with a story. It’s not just spreadsheets where journalists can find information worthy of the news.
Other data acquisition methods include website scraping, filing freedom of information requests, and hunting and gathering different complex data formats.
More often than not, the story will not come from simply getting the data but rather from analysing that data. This is especially true of big data, which may require computer-assisted reporting or some other formulas to decipher the story within the datasets.
Of course, data analysis was once purely the remit of data scientists. However, nowadays, with the amount of information available, data science has become an essential tool within the newsroom and is far from an ignored social science.
The ability to spot patterns and a compelling story within the complexities of data has become a weapon within the arsenal of today’s journalists.
Although some argue journalism is dying, there are now more ways than ever to tell stories, partly due to data visualization being a valued tool within most major newsrooms.
This is where data teams work together to present graphs, infographics and other visual presentations to help demonstrate the fruits of their collective investigative reporting.
An excellent example of the visualization of data stories is The Guardian’s presentation of Edward Snowden’s leak of NSA files.
Here, they made the story more interesting to the average reader with their ‘What the revelations mean for you’ visualization.
Instead of this large leak of data being displayed in a way that would seem overwhelming to readers, they humanized it. They did this by giving their audience the chance to see how the data affected the things they cared about the most.
It was a great use of data and it distributed the story to a much wider audience than it would have reached otherwise.
Data Is Everywhere
An early example of data acquisition, data analysis and data visualization was Philip Meyer and his computer-assisted reporting in 1967. After information was being distributed regarding the demographics of people rioting in Detroit, Meyer analysed survey data. He showed that the people rioting were just as likely to be college graduates as high-school dropouts.
Now, there is so much data out there that it’s almost impossible for one reporter to take on all three aspects like Meyer did.
In the Data Journalism handbook, this change in workflow of data journalists was discussed. Meyer discussed this point himself, once stating:
“When information was scarce, most of our efforts were devoted to hunting and gathering. Now that information is abundant, processing is more important.”
So much information exists today, so it’s a science finding compelling stories from large amounts of data.
Where is Data journalism?
At the end of last year, DataJournalism.com did research on the state of data journalism and where is it going. It had over 1,250 responses from journalists worldwide and published the results.
One of the most exciting findings was that over a quarter of data journalists got into the field because of the pandemic.
Another interesting finding was that the most commonly used programming language being used by data journalists is Python (63%), followed by HTML/CSS (51%) and then R (46%).
What’s New In Publishing is another news organization that is attempting to answer the question of ‘where is data journalism today?’. They have held discussions with several reporters from this field, with their conversation with Jacopo Ottaviani as part of their IJNotes podcast of particular interest.
Mr Ottaviani is a Pan-African ICFJ Knight Fellow who works at Code for Africa as its Chief Data Officer. He is helping newsrooms on the continent create data desks and use data more efficiently in their reporting.
One of the points he makes within this discussion is that data news stories need to be humanized. As we saw with the Guardian and the Edward Snowden released files, this can be the difference between your stories finding an audience or the masses of data being viewed as difficult to understand.
Challenges For Data Journalism
Data analytics and being a data journalist presents several challenges for those considering a journalism career. Here are just a few…
Access to Quality Data
Access to quality data isn’t always as easy to find when crowdsourcing news. In fact, in the aforementioned survey from DataJournalism.com, data journalists said that this was their biggest challenge.
Over half of those surveyed said that access to good data was the biggest issue.
Time Constraints Within Traditional Newsrooms
After accessing the data, many journalists feel as though they don’t have time to examine it and visualize it properly. Working as an investigative journalist on large data sets takes time and unfortunately, that time takes people away from other elements of their job.
It is harder to spend weeks and months on individual stories with today’s rolling-news mentality. That is one of the reasons why ‘time constraints’ was listed as the second most significant issue for data journalists in that survey.
Almost half of the journalists surveyed said that lack of resources was their biggest issue. This is because newsrooms often don’t have the financial, time or talent resources required to follow up data-related stories.
The Value of Data Journalism
About 70 per cent of data journalists believe that data brings “reliability and contextualisation to a story”. These journalists also pointed toward other values of the field, such as the ability to find both relevant and unique stories and bring them to the public.
The American Press Institute also discussed the value of data journalism, pointing toward its impartiality. It states:
“The clearest advantage data has over other sources is that it’s a fact… It’s an actual counted number of fatalities, for instance, or tax dollars or potholes. There’s not as much need to rely on anecdotal evidence when you have the real evidence in front of you.”
They also point out that data journalism allows reporters to verify claims, tackle more critical stories, and offer detail and distance while being more efficient.
The advantages of engaging in this type of reporting are plentiful, and it is clear that data journalism is a field that is here to stay. Data analysis has always been an element of a journalist’s work.
With the abundance of big data available today, it is now a social science that virtually every major newsroom needs to embrace.
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FAQs About Data Journalism
What is the meaning of data journalism?
Data journalism is a type of reporting that interprets raw data and uses that information as a critical source for a story.
What is a data journalism example?
The Upshot on The New York Times’ website publishes examples of data journalism regularly. These include an examination into finances the USA is contributing to Ukraine, the American public’s view on abortion, and the effects of anxiety on today’s youth.
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