Category Archives: data journalism

FAQ: On data journalism and open data

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In the second part of this FAQ (first part here), I respond to more answers to questions from a Turkish PR company (published on LinkedIn here)…

Q: What skills do you think a journalist must absolutely have when working with data?

There are three core skills I always begin with: sorting, filtering, and calculating percentages (proportion and change). You can do most data journalism stories with those alone.

Alongside those basic technical skills it’s important to have the basic editorial skills of checking a source against other sources (following up your data by getting quotes or interviews), and being able to communicate what you’ve found clearly for a particular audience.

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FAQ: How has journalism been transformed?

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In the latest FAQ, I’m publishing here answers to some questions from a Turkish PR company (published on LinkedIn here)…

Q: In your view, what has been the most significant transformation in digital journalism in recent years? 

There have been so many major transformations in the last 15 years. Mobile phones in particular have radically transformed both production and consumption — but having been through all those changes, AI feels like a biggest transformation than all the changes that we’ve already been through. 

It’s not just playing a role in transforming the way we produce stories, it’s also involved in major changes around what happens with those stories in terms of how they are distributed, consumed, and even how they are perceived: the rise of AI slop and AI-facilitated misinformation is going to radically accelerate the lack of trust in information (not just the media specifically). I’m being careful to say ‘playing a role’ because of course the technology itself doesn’t do anything: it’s how that technology is designed by people and used by people. 

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Visualisation as an editorial process

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In the second part of this extract from a book chapter in the new Routledge Companion to Visual Journalism, I look at the editorial processes involved in data visualisation, along with the ethical considerations and challenges encountered along the way.

Decisions around what data to visualise and how to visualise it involve a range of ethical considerations and challenges, and it is important to emphasise that data visualisation is an editorial process just as much as any other form of factual storytelling.

Journalists and designers employ a range of rhetorical devices to engage an audience and communicate their story, from the choice of the chart and its default views or comparisons, to the use of colour, text and font, and animations and search suggestions (Segel and Heer 2011; Hullman & Diakopoulos 2011).

Chart types are story genres

The chart that a journalist chooses to visualise data plays a key role in suggesting the type of story that is being told, and what the user might do with the data being displayed.

If a pie chart is chosen then this implies that the story is about composition (parts of a whole). In contrast, if a bar chart is used then the story is likely to be about comparison.

Line charts imply that the reader is being invited to see something changing over time, while histograms (where bars are plotted along a continuum, rather than ranked in order of size) invite us to see how something is distributed across a given scale.

Scatterplots — which plot points against two values (such as the cancer rate in each city against the same city’s air pollution) — invite us to see relationships.

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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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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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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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How to (not) write about numbers

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Image by Andy Maguire | CC BY 2.0

If you’ve been working on a story involving data, the temptation can be to throw all the figures you’ve found into the resulting report — but the same rules of good writing apply to numbers too. Here are some tips to make sure you’re putting the story first.

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How to ask AI to perform data analysis

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Consider the model: Some models are better for analysis — check it has run code Name specific columns and functions: Be explicit to avoid ‘guesses’ based on your most probably meaning Design answers that include context: Ask for a top/bottom 10 instead of just one answer 'Ground' the analysis with other docs: Methodologies, data dictionaries, and other context Map out a method using CoT: Outline the steps needed to be taken to reduce risk Use prompt design techniques to avoid gullibility and other risks: N-shot prompting (examples), role prompting, negative prompting and meta prompting can all reduce risk Anticipate conversation limits: Regularly ask for summaries you can carry into a new conversation Export data to check: Download analysed data to check against the original Ask to be challenged: Use adversarial prompting to identify potential blind spots or assumptions

In a previous post I explored how AI performed on data analysis tasks — and the importance of understanding the code that it used to do so. If you do understand code, here are some tips for using large language models (LLMs) for analysis — and addressing the risks of doing so.

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