How data visualisation can add value to your stories – guide for the era of post-infographics

Information graphics are a universal language. Aided by new data-targeted tools and technologies that are fast and easy to use, data visualisation in journalism has advanced far in the last years, especially since the COVID-19 surge. The shift from print to interactive and multimedia-based data visualisation marks the era of post-infographics. Charts help people understand better topics they care about.

We at The Fix covered the characteristics of slow journalism – accuracy, depth, context, analysis, and expert opinion – and how they are crucial in countering overproduction of content and increasing trust in journalism. Recent studies show data visualisation in online news media might play this role better than mere text, allowing users to take control of their experience through interactivity with the content, either by splitting data in smaller portions or by adding a playful aspect to it, and making information easier to perceive. 

These defining aspects of the post-infographics counteract the drop in interest in news. Neutral, well-visualised stories tend to be more shareable on online platforms and to be seen by more users. 

Conveying information effectively through visual means, however, is not easy. A story will usually start with a question, which will lead to needing to find data to answer it, and finally narrating it to the public, as Ashkey Kirk, Visual Projects Editor at The Guardian, noted last year in an interview. Good data visualisations are those that provide either new contexts on timely issues or exclusive news paired with visual journalism practices: they can show the data isn’t the story, but rather, at the centre is the real world implication of what the data is telling you.

What makes a good example of data visualisation?

Good projects are those that show the answer of the story rather than tell it. This story by The Pudding about how pockets are different in man and women’s wear shows perfectly how a visual essay can be superior to words alone. Furthermore, the authors ventured into stores to collect data: not all data stories start with a computer.

Yet, data visualisations can also be static while remaining engaging and informative. Take this Bloomberg article about why forgiving student loan debt in the US is so complicated, where colours and simple shapes help navigate the amount of data reported by the Department of Education.

Data visualisation tools you need to know

You don’t need to be an expert coder to create effective graphics – when the story is sound, nothing else matters. The work of data visualisation practitioners varies, but it usually comprises the two main tasks of data analysis and visualisation. It can become a complex workflow, as Rosamund Pearce, Visual Data Journalist at The Economist, says in an interview with Giovanni Sollazzo: “I use R for data cleaning, analysis and visualisation, QGIS for maps, Adobe Illustrator for laying out and polishing up the design, and D3 for things I can’t do elsewhere (like force-directed graphs)”. How to choose the right tools for the right tasks then? We gathered suggestions by data designers.

Data cleaning and analysis

For those with no coding experience, Google Sheets or Excel do the job perfectly and also automatically provide exploratory visualisation of the data. The most common programming languages to perform customised, automatic data cleaning and analysis are instead R and Python.

Data visualisation

After having cleaned the data, you need to experiment a bit to find out the right visualisation for it. To get inspiration from other designers, or to see what people have done in similar cases, the go-to website is the “Explore” section of ObservableHQ, which also provides the code to reproduce the outcome. 

In general, data designers recommend:

• Pen and paper! Award-winning designer Tiziana Alocci explains in an interview with The Fix: conceptualising a work on paper is a crucial step before beginning any kind of design work. Then, she realises them in Adobe Illustrator and Figma. Doing everything by hand is time consuming, but allows to refine every detail of the project.
• Datawrapper, as suggested to us by Miami-based journalist, designer, and author of the bestseller “How Charts Lie” Alberto Cairo: it’s free and offers an easy-to-use interface, quick interactive charts (including maps) and customizable layouts with graphical guidelines that work for print as well.
• Flourish: like Datawrapper, no coding required here. Paste or upload your spreadsheet and choose the most suitable chart template for your data. The resulting visualisation can be further graphically customised, is interactive, and can be then either directly embedded on a website or be downloaded in different formats.
• RAWGraphs: an open source tool that is most suitable for static charts to be further customised through vector graphic editors (e.g. Adobe Illustrator, Inkscape or Figma). The gallery section of the website shows impressive examples on some of the most famous international newspapers, such as De Tijd, Wired UK, Corriere della Sera.
• Google Data Studio or Tableau, especially for what concerns dashboards.
• QGIS maps for custom maps, with a great community to ask for support.
• D3.js, based on Javascript: coding experience needed, but it’s quite easy to learn as it’s thoroughly documented online.
• Adobe Illustrator: the best tool to create custom data visualisations, but also great to use as a final step to improve something of a pre-existing chart, such as fonts and colours.

Keeping track of projects

Keeping track of projects is the last but crucial step in the workflow of data visualisation. Christine Jeavans, Senior Data Journalist at BBC News suggests the combination of Dropbox, Jira and Github.

Keeping up with data visualisation

Data designers form a strong community. To learn more about their activities and find places to seek inspiration and ask for suggestions, social media is a good starting point. The Data Visualisation Twitter community, for instance, aims to increase the value of data visualisation to the public. 

There are interesting newsletters as well, such as The Economist’s Off the Charts, The Washington Post’s How To Read This Chart, and the resourceful, all things data thinking one by technology leader Giuseppe Sollazzo, Quantum of Sollazzo. 

For paper aficionados, Market Cafe Mag is the first independent magazine about data visualisation, risen from the need to bring together different international voices from within the information design world.

Finally, worth following are graphic journalist Mona Chalabi’s unique handmade projects, socially-focused data design studio Sheldon.studio, winner of the 2022 National Design Award Giorgia Lupi, and Francesco Muzzi, who has often been successfully collaborating with The New York Times.

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