Over the weekend, I’ve been boxed up in a student newspaper office. We’ve all been there: the stale smell, the lack of space, and the heavy eyelids. But we do it anyway, for whatever reasons, and I’ve ultimately enjoyed this weekend’s production, as I’ve been working on a really successful data story.

Sometimes when I’ve worked on data stories before (think stuff like arms funding and investment figures, and library late fees) the story can either be so obvious, or you can find yourself looking for something too eagerly and end up sensationalising the numbers in your efforts to find a scoop.

This time I was working on the student union’s account from the past 3 years. I approached it as more of exploration of the data, and aimed to highlight the overall incomings and outgoings for the last year, and then to pick out a few interesting trends from the bigger picture.

Getting into the data with preconceptions about what they might reveal was really important. Nothing was forced, and we weren’t tempted to try to find trends where they didn’t exist. I also think that one element to its success was not biting off too much. I wanted to demonstrate the overall spending from the last year only, rather than trying to deal with three years in all circumstances.

However, this was limiting in some situations, and can prove awkward. When I wanted to illustrate a trend such as the steady decline in profit made by the Graduation Ball over the last 3 years, it was harder to make it clear what we weren’t talking about just the accounts from 2010/11.

I’d recommend using colour when dealing with numbers. For the story, we presented a lot of the figures in infographic form, which is always nice, but I’d suggest using colour right from start to finish. Bring a pack of highlighters and make sure you know what means what.

Another tip: clarify everything. While you’d do this with any story, I think this is even more important with data, as it’s easier to make mistakes and create a false number as result of confused calculations. We had a problem when tackling the data that involved the salaries of sabbatical officers vs. their actual “cost” to the union, and clarifying the meaning of the figures within the accounts allowed us to distinguish.

Finally, if possible, I’d suggest working on a data story with other people. Mathematically minded people, ideally. Proofing a data story isn’t like reading a normal page of the paper, and having someone else who knows the numbers inside out is invaluable.

If you’re interested, you can check out the story here.
And if you’re keen for more data journalism tips, check out this recently released data journalism handbook, from MozFest.