When data journalism gets it wrong…

It only recently occurred to me that data journalism isn’t as new as I’ve come to believe over the last few months. I was watching the news when suddenly it was like a cloud was lifted, and then it hit me – THE WEATHER!

Just how do we get to know what to wear tomorrow? Whether we should wrap up warm or get the miniskirts out? The weather forecast! And how do weather reporters tell us about the weather for the following day and week? – From DATA!

You only have to watch a weather report to see just how much data visualisation goes into making a report that is easy to watch instead of looking at a bunch of random numbers and trying to make sense of it.

So it puts a whole spin on when data journalism became popular. I was led to believe this happened over the last decade. But  it turns out data driven journalism in theory has been going since the 1950’s when numerical data was first used to predict the weather. The BBC showed its first TV weather forecast in 1954.

But like most things, it’s never perfect, and only through trial and error you end up with better results. The atmosphere is complex, but none the less, weather gatherers do a decent job of predicting the weather. Analysing huge amounts of data and complex calculations to predict the weather requires the use of the most powerful supercomputers in the world. Even as technology develops, the computers still produce incorrect data from time to time.

Image courtesy of SWNS

Image courtesy of SWNS

October 15th and 16th 1987 is known in UK history as the great storm. It was the worst storm to hit the UK since 1703 and was responsible for 18 deaths in the UK, and 4 more in France.

Michael Fish who was giving the weather forecast the night before told viewers there would be no such hurricane, even though a member of the public rang the BBC insisting his own research suggested otherwise.

The youtube videos demonstrate this.

The original broadcast of Michael Fish’s report has been made famous by those words,
“Earlier on today, apparently, a woman rang the BBC and said she heard there was a hurricane on the way; well, if you’re watching, don’t worry, there isn’t, but having said that, actually, the weather will become very windy,”

So just who should be blamed for this? The Met office came under serious scrutiny as a result. A geography student from Manchester University was able to predict such an outcome but an organisation such as the Met Office and the BBC couldn’t. Had more care been taken, this could have saved 22 lives.
It can be argued that at the time, the computers used for predicting the weather were the most sophisticated measuring tools, and to not trust its data would have been foolish.

But what about news that gives the wrong information, not because of a computer error, but because of a human error? And not a human error which was a one off mistake, but a complete lack of understanding of data itself? Hands up to the Independent for this one. On Tuesday 25th May 2010, the Independent’s FRONT PAGE showed a “mountain” of Britain’s debt or deficit (they didn’t seem to know which one was which). They had confused the UK’s deficit and debt! By doing this, they made out that our debt was about 8 times higher than the actual figure

In an interview with Simon Rogers, who edits the DataBlog at the Guardian, I asked him if data driven journalism skills should be taught by news organisations or whether it should be up to the journalist themselves to acquire the neccessary expertise. He replied: “It should be actively encouraged by news organisations – as should a basic statistical literacy.”

This entry was posted in Examples of where data journalism is used, How is data journalism used?, Visualization Experiment and tagged , , , , , , , , , . Bookmark the permalink.

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