6 tips from Google on making compelling visualisations

Some lessons to be learned from Simon Rogers and Alberto Cairo

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Journalists aren’t used to taking advice from tech companies. Indeed, the row between Facebook and the journalism industry has intensified over the last two months. A Vox article published earlier this month attacked Mark Zuckerberg, who has been called “the world’s most powerful editor” for abandoning his editorial responsibilities.

Recently, Facebook fired the human editors on its ‘Trending’ team, prompting a flurry of clearly fake news stories from its automated algorithm.

Meanwhile, Google seems to have been moving in the opposite direction. Its News Lab has now been around for over a year, and has the explicit intention of collaborating with and empowering journalists. The Lab, which is run by former Boston Globe reporter Steve Grove, frequently works alongside journalists.

News Lab hosts a monthly Data Visualisation Round-Up in the form of a live YouTube discussion between Simon Rogers, the Google Trends Data Editor and former Guardian journalist, and Alberto Cairo, the Knight Chair in Visual Journalism at the School of Communication of the University of Miami.

From their 31 October discussion, here are some key points:

1. Graphics need a human side

Data journalism sometimes gets a reputation for being cold and calculating, as a place where statistics matter more than humanity. But data journalists are more than just automated counting machines, who often bring their emotions and convictions to bear on their work, and it is vital for data journalism to reflect that.

In the video, Simon Rogers recommends the September 2016 book Dear Data, by Giorgia Lupi and Stefanie Posavec. These two information designers, physically separated by the Atlantic, spent a year befriending each other by sending weekly hand-drawn data visualizations on postcards back and forth.

The cards contain many examples of innovative ways of displaying data, but the project was about more than that. Rogers calls it “a reminder that graphics should feel human and warm”.

2. Imprecision is fine

The era of Big DataTM has encouraged the growth of imprecise data analysis. In days gone by, sampling was the only game in town, and it was necessary for data to be incredibly precise, since datasets were relatively small. Now that data analysis and data journalism is starting to use big data, the sheer sizes of today’s datasets eliminate any problems that might arise from occasionally imprecise points.

brexit-and-us-election
This image, for example, illustrates the relative interest around the world in Hillary Clinton and Brexit.

Google News Lab teamed up with Accurat, a data research firm, to create World Potus, a project that uses Google Trends to look at how people in countries around the world were discussing the US election, by analysing their Google Searches.

Naturally, when using data from every single Google Search, some data points will be unhelpful. Someone might misspell ‘Clinton’ in an unpredictable way, or search while on holiday, making their geographical data misleading.

But since Google Trends uses big data, this doesn’t matter. There are so many points in this dataset that imprecision pales into irrelevance.

3. Data journalism should be collaborative

While more traditional journalists jealously guard their scoops, and are full of stories about the ruthless methods they’ve had to employ to get to the scene of a story first, data journalists can often be seen asking for (and receiving) help on Twitter from their colleagues. What’s more, articles often come complete with a link to the original data, so that other data journalists can dig for their own stories.

This is why all the code used in projects like World Potus is available on the Google Trends Github page.

4. We have to think more about our audience

Data visualisation is no longer the insurgent force it once was in the journalism industry. These days, infographics are pretty much par for the course, so much so that Giorgia Lupi has described our current period as “post-peak infographic”.

Sure enough, the New York Times has announced that it will now be producing fewer huge visuals. Does this mean that we’ve got over our initial enthusiasm for data visualization?

Rogers has a more nuanced view: “People are fussier about what they’ll love.” In other words, because of the recent glut of infographics, there is more importance on ensuring that the visualization serves the story and serves the audience.

5. Print can be more powerful than online

It is often assumed that data visualization is native to the Internet. While it is true that the online medium brings with it huge potential for interactive features, print can still play a vital role in visualization.

Alberto Cairo explains that he still buys print newspapers, and enthuses about the New York Times’ double page spread listing people who have been insulted by Donald Trump. The online version  is impressive, and gives the reader the ability to click through to specific insults, but the size and physical presence of a double page spread in the New York Times really brings home the extent of Trump’s vituperative qualities.

Cairo also cites the National Geographic magazine as a perfect example, specifically highlighting sketches by the artist Fernando Baptista, made for a large pictorial illustrated infographic about the Sagrada Familia cathedral in Barcelona.

“It’s gonna be like people listening to music on vinyl.” This remark from Simon Rogers perhaps betrays nostalgia stemming from his journalistic background, but probably chimes with the views of many modern journalists.

6. Data journalists must think about posterity

Excitingly, Rogers and Cairo seem to be planning some kind of grand archive for data journalism. One pitfall for visualization is the expiration of online programmes. For this reason, when Google starts a new initiative, it always has a plan for making sure that projects made using that programme will survive even if Google discontinues it.

As with much online journalism, data visualizations can be ephemeral, fading away after their first publication. Data journalists need to think about preserving their work, much of which will remain relevant for long periods of time.

Watch the full discussion here:

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