Data journalism, also known as data-driven journalism, is the process of finding, understanding, and processing information in order to produce news stories.
It’s always been part of the news production workflow but has increased in importance since the development of computers and the internet.
In the past journalists used to analyse numbers by hand trying to make sense of what they had jotted down in their notebooks when out covering a story.
By just asking the basic journalistic questions of what, why, when, how, where, and who, journalists were gathering data. This would result in collecting important data such as:
- What has happened?
- Event type and frequency: Crash, fire, riot – is it this the first time, the 10th time – how many times?
- How many people were affected?
- Number of people killed or injured, ambulances, police deployed
- When did this happen?
- Time and date, rush hour, drive time, overnight, morning.
- Where did it happen?
- Location – street, town, intersection, map reference, accident blackspot, area of known tension perhaps
- Who could have more information?
- Local authority or police records, facts and figures regarding similar events in the past.
In the example above the reporter would have jotted down any information they could find about the story they were covering. Those notes contained data which would be an essential part in telling the story.
That data, if processed and then analysed, could help the journalist and their team dig much deeper. But there was limited access to that data.
It would be contained in the reporter’s notebook, in the next edition of the newspaper, or broadcast in the next news bulletin, and stored in a newsroom archive as a physical cutting – but it would be hard to retrieve or be of much further use. (See – The importance of keeping records)
Perhaps a diligent journalist, who was specialising in a particular area, or working on an investigation, would create a simple hand-drawn spreadsheet to try to crunch the numbers, but often they were soon sent off to cover the next story and the data they had gathered would be put to one side.
Then came computers. This enabled journalists to store data and make sense of it using spreadsheets to look for patterns in terms of frequency, size, time, and any relationships between events.
With the development of the internet it became easier to find and share large amounts of data. Computers could be used to connect the data in ways that would have been impossible for a journalist in the past.
This resulted in the development of computer assisted reporting (CAR) which uses technology to analyse data and helps journalists find hidden stories and investigate complex issues such fraud and corruption.
By examining large datasets – structured collections of related data revealing patterns, trends, and relationships – journalists are able to produce more accurate and impactful journalism.
Computers also enable journalists to display the data they had gathered in graphs, charts, and maps – this is called data visualisation – which means that complex datasets can be displayed in easy to understand ways.
Data journalism is now an important part of news production with many journalists using advanced tools to find complex stories. And they are able to share their data so everyone can see where the information came from. This also leads to collaboration between different teams of journalists working together on a complex and important investigation.
In summary, data journalism has progressed from being a specialist practice, to an integral part of modern news reporting in several ways:
- Data analysis: Collecting, organising, and examining large amounts of data to uncover trends, patterns, and news angles.
- Storytelling: Using the insights uncovered to create compelling and informative news stories, and presenting complex information in a clear and easy to understand way.
- Visualisation: Creating charts, graphs, and maps to help audiences understand the stories behind the data.
- Tools: The use of spreadsheets, statistical software, and data visualisation platforms to process data in order to make it more useful in the news production process.
- Evidence: By including reliable and rich data in stories, data journalism can provide a more objective and evidence-based approach to reporting.
- Quantity: Data journalism enables a journalist to sift through large amounts of data – such as survey results, financial figures, football results, and government records to find stories hidden within that data.
- Accessibility: The journalist can then present those stories in a clear and easy-to-understand way using charts and graphs.
- Reliability: Instead of just relying on someone’s opinion, as has often been the case in the past, the journalist can use facts and figures to back up their reporting.
Data journalism – further thoughts
Journalism has always been a pursuit of truth, sifting through the noise to reveal what matters. At its core lies the fundamental task of gathering, analysing, and presenting information in ways that help society make sense of the world.
Over time, the methods used by journalists have evolved, but one constant remains: data has always been central to storytelling, whether jotted in a notebook or embedded within sprawling digital databases.
What has changed dramatically is the scale, speed, and sophistication with which journalists can access and interrogate information. The digital age has transformed raw data from fragmented observations into powerful tools for accountability, insight, and public understanding.
Where once reporters might have tallied casualty figures by hand or kept mental notes on patterns they noticed over time, they now wield vast datasets – crime records, health statistics, financial disclosures, social media activity – as both sources and subjects of their investigations.
The shift is not merely technological but philosophical. Data-driven journalism reframes the journalist’s role. They are no longer just a chronicler of events, they are also an investigator uncovering patterns invisible to the naked eye.
A single incident becomes part of a larger puzzle: a crash is not just an accident but potentially a symptom of systemic infrastructure failures; a spike in evictions reveals deeper housing inequities; electoral results expose demographic shifts and political realignments.
Data breathes life into these stories, adding context, nuance, and evidence that deepens public understanding.
With computational tools, journalists move beyond surface narratives to probe the why and how, not just the what. Algorithms, spreadsheets, and statistical models allow them to test hypotheses, verify claims, and uncover hidden relationships.
This capability becomes crucial in an era where misinformation spreads fast, and complex issues, such as climate change, global pandemics, economic inequality, demand rigorous scrutiny.
Equally transformative is the way data enables storytelling. Visualisations such as maps, charts, interactive graphics, help translate complexity into clarity. They allow audiences to see the scale of a crisis, the trajectory of a trend, or the impact of policy decisions in ways that words alone cannot achieve.
Good data visualisation doesn’t just display numbers; it creates an emotional and intellectual connection, turning abstract figures into human stories.
Another profound shift is the collaborative nature of modern data journalism. No longer confined to individual reporters. Many of the most impactful investigations today involve teams of journalists, data scientists, designers, and programmers working together across borders.
Global projects such as the Panama Papers or investigations into environmental destruction exemplify the power of shared datasets and collaborative analysis. Transparency in these projects – publishing methodologies, sharing datasets – also strengthens trust in journalism at a time when skepticism is high.
Ultimately, data journalism enriches the very purpose of the media: to inform, to explain, and to hold power to account. By grounding stories in verifiable evidence, it elevates reporting from anecdote to analysis, offering audiences not just opinions but actionable insights.
As data becomes ever more abundant, the journalist’s challenge is to remain not just a transmitter of information, but a skilled interpreter – someone who can connect the dots, surface the hidden stories, and empower the public to see the world more clearly.
Data is no longer a byproduct of reporting; it is a fundamental driver of journalism’s future.
Questions and Answers
- Question: What is data journalism, and how has its importance changed over time?
- Answer: Data journalism, also known as data-driven journalism, is the process of finding, understanding, and processing information to produce news stories. While it has always been a part of news production, its importance has significantly increased with the development of computers and the internet, allowing for more efficient and in-depth analysis of large datasets.
- Question: How did journalists gather and analyse data before the widespread use of computers?
- Answer: Before computers, journalists gathered data by hand, jotting down notes in notebooks and attempting to analyse them manually. They used basic journalistic questions such as “what,” “why,” “when,” “how,” “where,” and “who” to collect information. Sometimes, diligent journalists would create hand-drawn spreadsheets for simple analysis, but this was often time-consuming and limited.
- Question: What is Computer Assisted Reporting (CAR), and how has it transformed journalism?
- Answer: Computer Assisted Reporting (CAR) uses technology to analyse data, helping journalists uncover hidden stories and investigate complex issues like fraud and corruption. By examining large datasets, journalists can identify patterns, trends, and relationships that would be impossible to see manually.
- Question: What is data visualisation, and why is it important in data journalism?
- Answer: Data visualisation involves displaying gathered data in graphs, charts, and maps. It’s important because it allows journalists to present complex datasets in an easy-to-understand way, making it accessible to a wider audience and enhancing the impact of their stories.
- Question: How does data journalism contribute to a more objective and evidence-based approach to reporting?
- Answer: By including reliable and rich data in stories, data journalism provides a more objective and evidence-based approach to reporting. It allows journalists to back up their reporting with facts and figures, rather than relying solely on opinions.
- Question: How has the role of a journalist evolved with the rise of data journalism?
- Answer: The role of a journalist has evolved from simply chronicling events to also becoming an investigator who uncovers patterns and relationships within data. They now use tools to analyse large datasets, test hypotheses, and verify claims, providing deeper insights and accountability.
- Question: What are some examples of tools used in data journalism?
- Answer: Tools used in data journalism include spreadsheets, statistical software, and data visualisation platforms. These tools help journalists process and analyse large datasets, making the information more useful for news production.
- Question: How does data journalism enhance storytelling?
- Answer: Data journalism enhances storytelling by providing context, nuance, and evidence that deepens public understanding. Visualisations such as maps and charts help translate complex data into clear and impactful narratives.
- Question: How has collaboration changed in modern data journalism, and why is it important?
- Answer: Modern data journalism involves increased collaboration among journalists, data scientists, designers, and programmers, often across borders. This collaboration is crucial for tackling complex investigations and sharing datasets, strengthening trust through transparency.
- Question: What is the significance of data transparency in data journalism?
- Answer: Data transparency, such as publishing methodologies and sharing datasets, strengthens trust in journalism, especially in times of skepticism. It allows the audience to see where the information came from and verify the findings, promoting accountability and credibility.
