Find out how top data-driven journalists collate, analyse and present vast amounts of facts and figures into interactive graphics, searchable databases and fascinating charts.
Wednesday, 22nd September at 07:00 PM
13 Norfolk Place
London W2 1QJ
What would you do if someone handed you 90,000 unfiltered documents and asked you to make a story out of it?
Simon Rogers (@smfrogers) from The Guardian talked about how data journalism involves treating numbers in a journalistic manner and not just publishing the press release that accompanies the release of data. Interestingly, he doesn’t think that data journalists necessarily need to code and that crowd-sourcing is not a realistic way of turning data into stories. I love the fact that he referred to his web developer as ‘Grand Master Flash’. I also think that The Guardian is now going to be the first place to go to for data not the ONS.
Julian Burgess (@aubergene) from The Times gave a really in depth presentation (as in depth as it can be for a coder to give to journalists) showcasing some of the Times work (sadly behind the paywall). I particularly liked their remembering 9/11 interactive which is just a notepad that reveals the texts and pagers sent from New York that day. I loved his rferral to scraping codes as ‘an infinite number of interns’.
David McCandless (@infobeautiful) from Information is Beautiful gave a hugely entertaining presentation saying you don’t need to be a designer to make information beautiful. He also pointed out that data needs a human filter so that you can structure your visual so that it tells a story. For him, the story is what matters. He’s right when he says you need to see what’s important in the data and concentrate on what people should focus on in the visual. Design is about reducing information.
And then there was Michael Blastland from the Frontline Club who can read data and pick apart its construction. For data journalists there are three stages: data hunger (where I am now), data savy (where I’m going), and data presentation (where I want to end up). Some of his pearls of wisdom included: “Finding your data is tough, knowing what your data does is even tougher” and “data is always wrong, the question is how wrong”. Watch the full event