Data, data visualization and interactives within news

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In this extract from a book chapter in the new Routledge Companion to Visual Journalism, I look at how the explosion of data as a source for journalists, and the separation of content from interface in online publishing, have combined to lay the foundations for a range of new storytelling forms, from interactive infographics and timelines to charticles and scrollytelling.

Although the term ‘data journalism’ is a relatively recent one, popularised around 2010, data has been part of journalism throughout its history, from early newsletters covering stock prices and shipping schedules in the 17th century, to The Guardian’s 1821 first edition front page table of school spending, US investigations of politicians’ travel expenses in the 1840s and campaigning factchecking of lynching in the 1890s.

The introduction of computers into the newsroom in the 20th century added a new dimension to the practice. After some early experimentation by CBS News in predicting the outcome of the 1952 presidential election by applying computer power to data, a major breakthrough came in the 1960s with Philip Meyer’s use of databases and social science methods to investigate the causes of riots in Detroit.

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Double counting: how to spot it and how to avoid it

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Double counting — counting something more than once in data — can present particular risks for journalists, leading to an incorrect total or proportion. Here’s how to spot it — and what to do about it.

Look at the following chart showing the gender of teachers in UK schools, based on data on teacher headcounts. Notice anything wrong? (There are at least two problems)

Pie chart: Sex of teachers in UK schools There are three visible slices: male, female and 'total', which takes up more than half of the pie.

The most obvious problem is that the chart appears to be ‘comparing apples with oranges’ (things that aren’t comparable). Specifically: “male”, “female”, and “unknown” are similar categories which can fairly be compared with each other, but “total” is a wider category that contains the other three.

I’ve used a pie chart here to make it easier to spot: we expect a pie chart to show parts of a whole, not the whole as well as its parts.

But the same problem should be obvious from the same data in a table before visualising it:

Table showing headcount of teachers in the categories: female, male, unknown, total, plus a grand total of all four at the bottom

The table shows us that we have both a “Total” and a “Grand Total”. This is a red flag. There can only be one total, so if there’s more than one that’s a strong sign of double counting.

Why is this happening? We need to take a look at the data.

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7 técnicas de design de prompts para IA generativa que todo jornalista deveria conhecer

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Ferramentas como o ChatGPT podem parecer falar a sua língua, mas, na verdade, falam uma linguagem de probabilidade e suposições fundamentadas. Você pode fazer-se entender melhor — e obter resultados mais profissionais — com algumas técnicas simples de prompting. Aqui estão as principais para adicionar ao seu kit de ferramentas (Este post foi traduzido do inglês original usando o Claude Sonnet 4.5 como parte de uma experiência. Por favor, avise-me se encontrar algum erro ou traduções incorretas).

Técnicas de design de prompts para IA generativa Prompting de função Prompting de exemplo único Prompting recursivo Geração aumentada por recuperação Cadeia de pensamento Meta prompting Prompting negativo

Prompting de função

O prompting de função envolve atribuir um função específico à sua IA. Por exemplo, você pode dizer “Você é um correspondente experiente de educação” ou “Você é o editor de um jornal nacional britânico” antes de delinear o que está a pedir que façam. Quanto mais detalhes, melhor.

Há pesquisas contraditórias sobre a eficácia do prompting de função, mas no nível mais básico, fornecer um papel é uma boa maneira de garantir que você fornece contexto, o que faz uma grande diferença na relevância das respostas.

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At its best, AI can help us to reflect on our humanity. At its worst, it can lead us to forget it

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Frankenstein's monster and Maria

Almost all conversations around AI come down to these hopes and fears: that at its best AI can help us to reflect on our humanity. At its worst, it can lead us to forget it — or subjugate it.

When AI is dismissed as flawed, it is often through a concern that it will make us less human — or redundant.

The problem with this approach is that it can overlook the very real problems, and risks, in being human.

When people talk about the opportunities in using AI, it is often because they hope it will address the very human qualities of ignorance, bias, human error — or simply lack of time.

The problem with this approach is that it overlooks the very real problems, and risks, in removing tasks from a human workflow, including deskilling and job satisfaction.

So every debate on the technology should come back to this question: are we applying it (or dismissing it) in a way that leads us to ignore our humanity — or in a way that forces us to address our very human strengths and weaknesses?

No, the explainer isn’t dead. It just needs a reason to live.

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A collection of explainer headlines

Marie Gilot says the explainer is dead. Because AI.

“Today, our readers query AI for all that stuff,” she writes. “They like the AI answers well enough and they don’t click on article links.”

Here’s the type of content losing to AI: explainers, how-tos, evergreens, aggregated news, resource lists, hours of operation for government offices, recipes.

Gilot is right, of course. But only partly.

It’s right that the commercial imperative to produce explainers — low cost, high traffic — is going to come under severe challenge at one end.

But that doesn’t mean the explainer is dead. It just means they need to have a reason to fight for their life beyond money.

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Telling stories with data: more on the difference between ‘variation’ stories and ‘ranking’ angles

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7 common angles for data storie: scale, change, ranking, variation, explore, relationships, bad data, leads
The 7 angles. Also available in Norwegian and Finnish.

One of the most common challenges I encounter when teaching people the 7 most common story angles in data journalism is confusion between variation and ranking stories. It all comes down to the difference between process and product.

That’s because both types of story involve ranking as a piece of data analysis.

We might rank the number of specialist teachers in the country’s schools, for example, in order to tell either of the following stories:

  • “There are more specialist science teachers than those in any other subject, new data reveals”
  • “New data reveals stark differences in the number of specialists teaching each subject in secondary schools

The first story reveals which subject has the most teachers — it is a ranking angle because it ranks teachers by subject.

The second story reveals the simple fact that variation exists, without focusing on any particular subject.

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“Journey prompts” and “destination prompts”: how to avoid becoming deskilled when using AI

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A road
Photo: Tiana

How do you use AI without becoming less creative, more stupid, or deskilled? One strategy is to check whether your prompts are focused on an endpoint that you’re trying to get to, or on building the skills that will get you there — what I call “destination prompts” and “journey prompts”.

In creative work, for example, you might be looking for an idea, or aiming to produce a story or image. In journalism or learning, a ‘destination’ might be key facts, or an article or report.

But prompts that focus only on those destinations are less likely to help us learn, more likely to deskill us — and more likely to add errors to our work.

To avoid those pitfalls, it is better to focus on how we get to those destinations. What, in other words, are the journeys?

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4 ways you can ‘role play’ with AI

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4 roleplay design techniques for genAI Rubber ducking Using AI for ‘self explanation’ to work through a problem. Critical friend/mentor Using AI for feedback or guidance while avoiding deskilling. Red teaming/ devil’s advocate Using AI to identify potential lines of attack by an adversary, or potential flaws/gaps in a story. Audience personas Using AI to review content from the position of the target audience.

One of the most productive ways of using generative AI tools is role playing: asking Copilot or ChatGPT etc. to adopt a persona in order to work through a scenario or problem. In this post I work through four of the most useful role playing techniques for journalists: “rubber ducking”, mentoring, “red teaming” and audience personas, and identify key techniques for each.

Role playing sits in a particularly good position when it comes to AI’s strengths and weaknesses. It plays to the strengths of AI around counter-balancing human cognitive biases and ‘holding up a mirror’ to workflows and content — and scores low on most measures of risk in using AI, being neither audience-facing nor requiring high accuracy.

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AI and “editorial independence”: a risk — or a distraction?

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Tools
When you have a hammer does everything look like a nail? Photo by Hunter Haley on Unsplash

TL;DR: By treating AI as a biased actor rather than a tool shaped by human choices, we risk ignoring more fundamental sources of bias within journalism itself. Editorial independence lies in how we manage tools, not which ones we use.

Might AI challenge editorial independence? It’s a suggestion made in some guidance on AI — and I think a flawed one.

Why? Let me count the ways. The first problem is that it contributes to a misunderstanding of how AI works. The second is that it reinforces a potentially superficial understanding of editorial independence and objectivity. But the main danger is it distracts from the broader problems of bias and independence in our own newsrooms.

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6 Wege, Datenjournalismus zu kommunizieren (Die umgekehrte Pyramide des Datenjournalismus Teil 2)

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Datenjournalismus: Daten kommunizieren Visualisiern Erzählen Herunterbrechen Personalisieren Audiolisieren/materialisieren Nutzen bieten

Die umgekehrte Pyramide des Datenjournalismus bildet den Prozess der Datennutzung in der Berichterstattung ab, von der Ideenentwicklung über die Bereinigung, Kontextualisierung und Kombination bis hin zur Kommunikation. In dieser letzten Phase – der Kommunikation – sollten wir einen Schritt zurücktreten und unsere Optionen betrachten: von Visualisierung und Erzählung bis hin zu Personalisierung und Werkzeugen.

(Auch auf Englisch und Spanisch verfügbar.)

1. Visualisieren

Visualisierung kann ein schneller Weg sein, die Ergebnisse des Datenjournalismus zu vermitteln: Kostenlose Tools wie Datawrapper und Flourish erfordern oft nur, dass du deinen Daten hochlädst und aus verschiedenen Visualisierungsoptionen auswählst.

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