The need for data sensitivity in newsrooms

While we discuss data journalism and various advanced tools of data analysis, data scraping and visualization, what often gets overlooked is the need for sensitivity towards data by any average journalist.

Two days back, the Indian government released information on revenue generated by major historical monuments in India in the financial year 2013-14 (April 2013 to March 2014). It was a simple list of monuments with the revenue generated by a monument from ticket sales/camera charges etc against it. For some strange reasons, in the press release, the sequence was not in decreasing order of revenue generated by these monuments, as one would expect. Neither was it alphabetical nor was it arranged according to regions (such as North, South, East, West). It was not arranged in any particular order.

But the list itself is fairly simple to understand. Most newspapers presented the list, quite logically, in order of decreasing revenue. The Times of India carried the list as the lead item in today’s newspaper, but giving it a different twist, by choosing to highlight falling visitor numbers in the aftermath of the Delhi rape case and how that has affected visits to monuments—something which is not evident from the data itself.

But while trying to present the list of top revenue generating monuments, the Times of India, missed a few. And there was nothing written to suggest that it was just a representative list and not the top 10. So, some monuments such as Konark, Khajuraho and Elephanta were missing from the list.

Here is the correct list. The figures are all in INR million.

Monuments

This raises questions about how much can we trust the data presented by newspapers? This is surely some data that no journalist would deliberately misrepresent for any vested/ideoological reason. The only plausible reason is the discomfort to make sense of data. And this is such a simple dataset.

While it is important to spread data journalism tools and techniques, it is equally important to sensitize news desks about teh need to understand simple datasets.

 

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