Gone are the days when journalists simply had to find and report the news. Today, journalists have to comb through several data sets to find newsworthy information and report stories accurately.
The evolution of the internet has brought us to this point: an increasingly ‘datafied’ world. Everyone who interacts with the world today leaves data trails, and in these trails are embedded important stories for journalists to tell as humanity navigates its existence.
The question now is not about the usefulness of data but how to collect and report the data accurately.
Communicating data effectively has now become an ethical responsibility of every journalist.
Data is just that: data.
Numbers don’t say anything except what we interpret them as. As such, raw data is always very messy and so needs to be ‘cleaned’. Dirty data may be outdated, insecure, incomplete, inaccurate, inconsistent, or inflated. Cleaning bad data means fixing this bad data to ensure that the updated data is relevant, accurate, and useful for your purposes.
The content and organization of data matter, but its context is also important.
And this is perhaps the most demanding aspect of data journalism. Data is often considered objective fact, but this is not a given. Removing any piece of data from its context makes it cease to be objective. And subsequent stories based on that compromised source would be jeopardized.
Thus, as a data journalist, your work of collecting data is both forward and backward. Forward, building up your story reporting, and backward, examining sources for contextual accuracy and coherence. This means finding missing items and filling the gaps to ensure that the ensuing story stands to reason.
Filling gaps is especially important in genres like business reporting, where organizations are known to embellish the data to their own benefit. As such, data journalists must consider the full scope of their sources, how the business was founded, its operational structure, its contextual environment, etc.
It also involves paying attention to the metadata (the data about the data). The metadata repository (data dictionary) describes the dataset and explains what’s behind the numbers. This is key to understanding how the numbers were sourced and whether you can trust the data.
You simply can’t take data at face value, no matter how reliably it was sourced. The aim of putting data in its correct context is to rid it of stereotypes and biases and ensure that your journalism is indeed factual and objective reporting. What is the point of journalism if it does not achieve this? Thus, effective data journalism plays right into the ethics of the profession and must be taken seriously.
The point of data in journalism is to help you:
Data is playing a huge role in how the world is approaching the COVID-19 pandemic. The numbers have helped the public understand the scale of the challenge we are facing. And the various fact-based narratives concerning response measures have helped governments, stakeholders, and individuals manage the spread of the virus.
For instance, the New York Times was awarded the 2021 Pulitzer prize in public service for “filling a data vacuum that helped local governments, health care providers, businesses and individuals to be better prepared and protected.”
Many other projects have recorded similar impacts via data-backed reporting of the COVID-19 pandemic.
Now, communicating data does not mean dry reporting of statistics. The best data-based stories do not have readers combing through numbers and trying to figure out the math. In fact, such dry reporting is a sign that you do not communicate data effectively.
Rather, what journalists report is what the data says. Guide your readers carefully through the numbers by presenting characters and events while focusing on the implications of the data and why such a story should be public knowledge. This doesn’t mean not reporting numbers at all; the key factor, rather, is relevance.
This is where illustrations come in.
Data visualization helps you to present your data findings in a digestible format, while the written text focuses on the story the numbers tell. In this way, data journalism makes your stories more accessible to readers.
This data should be illustrated correctly and succinctly so as not to cause confusion. That means using appropriate visualizations for each type of data and including only visual elements that contribute to the story meaningfully.
In the age of social media, visual illustrations are far more likely to make stories spread wider than long-form written text. This is not to say that journalists should publish for the sake of virality.
But if there is an important story that requires utmost public attention as well as the immediate efforts of the stakeholders, there is certainly no harm in ensuring that your story is engaging and ‘attractive’. Thus, data journalism helps you to tell important stories better.
Data journalism helps you uncover previously unobserved patterns in data and tell stories that have gone unreported or under-reported.
As such, data does not only lead to stories; data can also be the very story you tell. This should not turn journalists into glory hunters, analyzing data to find a particular story or derive a particular perspective. This is a form of bias, which compromises your story.
Communicating effectively as a data journalist begins with keeping an open mind when accessing and analyzing the data. In simpler words, let the data tell its own story.
Journalism is all about publishing narratives, but the best stories are the ones underpinned by rigorous analytical research and thought. Data journalism is in the background checks, in reporting numbers, and in gathering facts. Basically, journalism is data, and data journalism should not be considered a subset of the field. As Brené Brown famously said in her famous TEDx talk,
“Maybe stories are just data with a soul.”
All journalists should aim to communicate data factually and effectively. Therefore, as technology has evolved new innovations for collecting and managing data, modern journalism must evolve to integrate data analysis into traditional reporting.
Modern journalists should be well-equipped to deal with data as that’s the primary way to understand today's world. Of course, this is not a call to every journalist that they pivot into full-time data analysis. But the collaborative nature of data journalism, bringing together journalists, data analysts, programmers, etc., can help us tell richer and more accessible stories.