This time last year I was in a quandary. I was absolutely sure I wanted to be a journalist, but what breed of hack did I see myself as? The football fan inside me wanted me to do everything in my power to become the next Paul Hayward (the Telegraph’s chief sports writer), but as a Geography graduate I was also drawn to environmental journalism.
I had spent over a year writing largely about football and had built up a decent online presence as a specialist on the women’s game – where I regularly appeared on podcasts – but I was beginning to wonder whether sports writing might be too narrow a path for me to go down. I’ve always had a huge appetite for soaking up new knowledge, and as my arrival at City University (where I am currently studying for an MA in Interactive Journalism) drew closer I was leaning towards the environmental option, believing that it would prove to be a more varied field to work in. Little did I know that an entirely new option was about to present itself…
I first came across the concept of data journalism when I was at the Guardian and drafted in as part of a team of eager ‘work experience kids’ (some were actually still at school) to go through details of court cases following last August’s riots. I spent six few weeks poring over spreadsheets and helping to create interactive graphics, and when my stint came to an end there was no doubt about it – this was the kind of journalism I wanted to be involved in.
Happily, my experiences so far have only supported my decision to go down this route. It is difficult to imagine a more holistic role in a newsroom than that of a data journalist – I’ve worked on stories where I’ve got hold of the raw figures, analysed them to get the key statistics, interviewed the people most-acutely affected by what the numbers show, and then written the final copy. What more can you ask for?
Having said that, I won’t pretend that data journalism is everyone’s cup of tea. If you bade good riddance to maths the day after your GCSEs then a career that involves trawling through spreadsheets probably isn’t for you, but as one of the fastest-growing areas of modern journalism (both in the print and broadcast media) I think it’s very important that aspiring journalists are aware of the opportunities it offers.
Just the other week I spoke to a senior digital journalist at the Times, who bemoaned the difficulty in attracting top level, specialist data journalists. Just read that back to yourselves – a national newspaper struggling to find people to employ. I’m sure I don’t have to stress just how rare such a scenario is in today’s journalism job market.
A few weeks ago Jonathan wrote a great post on using data journalism in his student newspaper, but no doubt most of you are still relatively new to the concept. In an effort to rectify this situation, I’ve outlined a couple of the positions a data journalist might occupy at a news organisation, as well as a rough description of the kind of candidate who might get the job…
Data researcher: There is more publicly available data around us today than there has ever been. This is great news for journalists everywhere, but particularly for those of the data persuasion. Even at the highest levels, most hacks run a mile when presented with thousands of rows of numbers, but if you find the day’s biggest story buried within the spreadsheet, you’ll have your name on the front page.
Who fits the bill? Someone with a keen eye for a story hidden in numbers, a strong grasp of statistical analysis techniques and a capacity to spend hours (and sometimes days, trust me) going through information in full knowledge that the story might get spiked at any moment.
Data hacker: While the manual research methods described above are often the most practical way of finding a data story, there are plenty of situations when either the size or the format of a dataset makes an automated approach more practical. Some of the most interesting (and potentially splash-worthy) data around is stored on seemingly inaccessible web pages, but with a few lines of carefully-crafted code you can have it all sitting on your desktop ready for analysis. Check out Tony Hirst’s blog for some great examples of what you can dig up using these methods.
Who fits the bill? Someone who can get hold of the data other journalists only wish they could explore; a fluent coder with a good knowledge of where the really important datasets are hidden away.