The post Computer-assisted reporting (CAR) first appeared on Media Helping Media.
]]>Since the development of computers, CAR has been used by journalists to uncover patterns and trends by examining data. Now, CAR has become a subset of the wider area of expertise known as data journalism – which includes coding, automation, and data visualisation for interactive storytelling.
In our article ‘What is data journalism?‘ we refer to CAR in the context of its role in data journalism. But what is CAR? And how does it differ from data journalism.
Differences between CAR and data journalism:
Feature | CAR | Data journalism |
Focus | Data analysis for investigative journalism | Data-driven storytelling & visualisation |
Tools | Spreadsheets, databases | Programming, APIs, visualisation tools |
Approach | Analysing structured data | Collecting, cleaning, analysing, and visualising data |
Evolution | 1980s-1990s | 2000s-present |
In short, CAR is an early form of data journalism. While CAR was about using computers for analysis, data journalism has expanded to include sophisticated digital tools, coding, and visual storytelling techniques.
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]]>The post Good journalism has always been about data first appeared on Media Helping Media.
]]>I can remember when I first realised I was a data journalist, or at least helping to produce data journalism.
It was in the summer of 1997 when we were getting ready to launch the BBC News website. (And by the way, I don’t get any marks for being perceptive, because as we point out in the article ‘What is data journalism?‘, all journalists are data journalists, whether they know it or not, so I had been one since the 1960s.)
Anyway, we were looking at how to produce and improve news stories – and all our assumptions belonged in the analogue age.
We were obviously aware that unlike television and radio, online news was not an ephemeral, one-word-at-a-time medium. Users could dwell on text and be directed to other information for valuable context and background.
We wanted to offer rich, instantly-available material that supplemented and enhanced every story.
But to produce that kind of material, we were used to relying on our own and our colleagues’ memories and archives, the BBC’s tape and audio libraries, a newspaper cuttings library and rudimentary newsroom systems that were not connected to the Internet.
In other words, it was a bit haphazard, almost certainly incomplete, relied on a lot of legwork and took ages.
Suddenly, as our tech guru patiently explained to us, we had electronic access to all kinds of valuable material. He called it “data”. The penny dropped.
We could automatically link to related stories. We could use search to produce the raw data for time-lines and fact files. We could pull down stories being written on primitive terminals in the BBC’s Moscow newsroom and automatically format them as web pages.
We even had a stab at a bit of software that would automatically create a timeline on important, recurring stories. It would search all our sources for, say, unrest in any particular country and produce a list of events.
To make the list usable, we had to instruct it not to put any two items too close together chronologically, unless they were very important, and to exclude items of lesser importance if the list was too long.
It was very ambitious and I cannot remember if we ever got round to implementing this functionality. If we did, then we almost invented an early version of artificial intelligence.
But now, the real thing is here, and the new capabilities that fascinated and thrilled us in those early years are now easily and freely available to everyone, in much more powerful versions, thanks to the power of large language models, neural networks and immense distributed computing power.
So now, not only are all journalists data journalists, we all have access to immense quantities of priceless data and the tools to make good use of it. We have listed many of those data tools and resources.
They are wonderful. But do not forget that in the term “data journalist” the second word is more important than the first.
We should all be thrilled and grateful for the things Artificial Intelligence makes possible, but the most powerful tools are still the human journalist’s instinct, judgement and training.
This text offers a fascinating glimpse into the nascent stages of digital journalism, particularly the moment when the author recognised the inherent data-driven nature of the craft. Let’s expand on this, adding depth, meaning, and perspective:
The assertion that “we are all data journalists” transcends a mere label. It’s a fundamental recognition of the information age’s defining characteristic: the sheer volume of data surrounding us. Even before the term gained currency, journalists were implicitly engaged in data analysis, sifting through facts, statistics, and records to construct narratives. The shift, as the author articulates, lies in the accessibility and utility of data.
The author’s recollection of the BBC News website’s launch in 1997 is a powerful illustration of this transition. The limitations of analogue methods – reliance on memory, physical archives, and disconnected systems – highlight the transformative potential of digital data. The “tech guru’s” revelation wasn’t just about accessing “valuable material”; it was about recognising the inherent structure and relationships within information, the ability to connect disparate pieces into a coherent whole.
The ambitious attempt to create an automated timeline generator speaks to the early recognition of AI’s potential in journalism. The challenges faced – managing chronological proximity and prioritising information – are precisely the problems that modern AI and machine learning algorithms address. This anecdote is more than a historical footnote; it’s a testament to the foresight of those who recognised the need for intelligent data processing.
The author rightly points out that the tools that were once the exclusive domain of tech-savvy journalists are now widely accessible. Large language models, neural networks, and distributed computing have democratised data analysis, empowering individuals to explore, interpret, and visualise information in unprecedented ways. This democratisation, however, does not diminish the importance of journalistic ethics and skills.
The emphasis on “journalist” over “data” is crucial. While AI can automate tasks and provide insights, it cannot replace the human element of journalism. The author’s “instinct, judgement and training” remain indispensable. This encompasses:
The modern data journalist is not merely a data wrangler but a storyteller, an investigator, and a communicator. They must possess a blend of technical skills and journalistic acumen. They must be able to:
As AI continues to transform journalism, it is essential to remember that technology is a tool, not a replacement for human intelligence. The focus should be on using AI to enhance journalistic capabilities, not to automate them entirely. The ethical implications of AI in journalism – including issues of bias, transparency, and accountability – must be carefully considered.
In conclusion, the author’s reflections provide a valuable perspective on the evolution of data journalism. The journey from analogue limitations to digital possibilities underscores the transformative power of data. However, the enduring importance of journalistic integrity and human judgment reminds us that technology is only as good as the people who use it.
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]]>The post Data journalism – resources and tools first appeared on Media Helping Media.
]]>We have compiled a list of some of the leading resources and tools that are available for those starting out in data journalism.
This list will be updated over time. You might want to consult our Data journalism glossary to look up some of the terms that appear below.
Below is a list of tools used by data journalists. They cover data gathering, cleaning, analysis, and visualisation. These tools are great for both beginners and experienced data journalists:
By combining these free resources, you can build a strong foundation in data journalism without breaking the bank.
The post Data journalism – resources and tools first appeared on Media Helping Media.
]]>The post Data journalism glossary first appeared on Media Helping Media.
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The post Data journalism glossary first appeared on Media Helping Media.
]]>The post What is data journalism? first appeared on Media Helping Media.
]]>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:
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 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:
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.
The post What is data journalism? first appeared on Media Helping Media.
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