• Post category:Data Journalism
  • Reading time:3 mins read

The Problem with Data Journalism…Time

As in not enough of it.

Big datasets take time to go through and understand and find the stories in. Not easy when you have to write a lead on it as well as other leads.

Particularly when calls need to go into press office by early afternoon to have any chance of getting a comment (this shouldn’t be a problem but it is a reality) and starting to chase people for quotes is best done earlier rather than later.

It is fine if it’s an FOI and you can hold on to the story until the data is tidied and interrogated and probably a pretty visualisation to boot.

But when it’s a statistics release by government, time is of the essence.

Today has been rather characterised by this. I’ve been working on the Welsh Local Government Revenue Expenditure figures, which are about 5,900 lines (not that big, but not that easy to go through quickly)

The best I’ve managed to do with them is a couple of fairly straightforward stories looking at the headline figures, which are basically set out in the statistical release.

It has taken me until now (from 11.30am on and off) to clean up the data and create a nice visualisation that makes them a bit easier to read (the reason I like visualisations of data is it’s easier to scan through data for changes with them).

There are interesting things in there, things that need looking into because possibly there’s a story.

But trying to do it in eight hours just wasn’t possible – it would have taken too long to find the odd numbers and then find out why they were odd (Seriously, today just looking at the headline cuts figures I’ve had three finance departments disputing the figures, I dread to think how long its going to take to get an explanation for things like the massive jump in spending on service strategy in children’s services).

Hopefully, I can start to tease things out over the next few days.

So if time is a problem, how to solve it.

Better resource data journalism in the belief it generates stories readers will be interested in (this seems to be what is happening on the nationals, it’s unlikely to happen on the regionals) or accept data journalism is slow journalism and sometimes you just have to run the obvious stories first and find the buried stories later.

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