The Data Daily

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

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

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 Fixcovered 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.

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.

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.

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.

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. 

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, Jiraand Github.

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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