Media priorities can change in a flash. It was not long ago that “data journalism” was a big thing globally and began to make its presence in India. Now, in the “post-truth” era, the media can no longer afford to spend its time just reporting facts and thinking up how to present data in a visually attractive and interesting way. It has to deal instead with what are called “alternative facts”, better described as lies, which yet many readers and viewers do not seem to wish to describe them as such. For many reasons, in the West statistics has become a bad word while fake news has a ready audience.

It has not become quite as bad in India but we are getting there. On social media at least, lies seem to receive as much importance as facts. Remember the regular UNESCO awards for the best currency note and the best prime minister in the world?

Post-truth or not, the media cannot give up the fight and abdicate its responsibility to present readers with the facts. does its bit for data journalism by publishing a fair number of articles which present statistics, analyse them and beef up an argument with data. Most of them are drawn from, the initiative which in recent years has taken up the job of data journalism, filling a gap which the big media organisations should have carried out years ago. has published four data-driven pieces in the past fortnight and they present an interesting mix of themes and analysis.

Dalit professors in the IITs

This article on how IITs can be more sensitive to Dalit students in a highly competitive environment looked at one statistic – the numerical presence of Dalit professors in the IITs. More faculty from a particular socio-economic background and it is likely that the environment will be more welcoming to students from the same background. This is a well-known argument and the statistics that the article used are also well-known (and dismal). What was new was the interesting way in which the article pushed the analysis and went beyond presenting the data. The statistics were the bare scaffolding for the analysis.

A second and equally interesting data-based analysis was the one that examined what had happened to the textile industry in Maharashtra post demonetisation. This piece was slightly different from the usual data article. It used only one set of published data but drew a lot from information collected the old fashioned way from the field. Another interesting aspect was that this article did not look at just one activity of the textile industry, but followed cotton from the farm to the retailer to see how demonetisation had affected activities at each stage.

But we can’t assume that all pieces of data journalism must by definition be informative, cogent and with substance. It is a trite observation that you have to use data with care.

As if to prove this point, unfortunately for the readers, we had this piece on the prices of vegetables that farmers received after demonetisation. Soon after demonetisation, the media was full of reports of how the prices of perishables – fruits and vegetables – had crashed and farmers had been driven to distress sales. That was two and a half months ago. What is the picture now? We do not get an idea from at least the piece published.

The article takes information from November 2016, perhaps only published now by the government agency, for four vegetables in seven cities, produces a nice colourful table and tells us prices, well, crashed. So what is new? Why tell us the November story in late January? The November story has been told many times in different forms, why again with similar information?

We also have the article telling us from the data that farmers in Hyderabad, Chennai and other cities suffered price collapses. Farmers in Hyderabad and Chennai? Who can they be in those urban agglomerations? What the article is looking at is prices in the wholesale markets in these cities…but the farmers who grew these vegetables were somewhere else. The prices they received in the local mandis may have fallen by less or more than in the wholesale markets. We do not know from the data that has been presented.

If this is an example of data analysis that has gone wrong, this article about what the Union Budget for 2016-’17 should do to improve school education outcomes is of a different kind. It simply asks the wrong question.

A state subject

Around this time every year, there are any number of articles asking what the Budget will do for this sector, for that programme etc. In many cases, the Budget may have very little to do with the sector. Like in this case. Sometimes the Union Budget does have special initiatives for school education like the Sarva Shiksha Abhiyan. But education is a state subject, so the involvement of the central government is limited. The Union Budget does provide funds for school education but the spending by state governments is manifold this amount. Why then look at the Union Budget? And what can a Budget do about learning outcomes, which have more to do with how children are taught? About the only idea, as the article quotes one researcher, is to make funding partly dependent on outcomes.

Here, then we have a piece of “data analysis” throwing in some numbers on allocation for education, some numbers about spending, speaking to some persons in the field about unrelated issues and coming up with an analysis that has been carried out only because the Union Budget is round the corner.

So here are two articles which used data carefully, asked the right questions and supplemented published statistics with field-level information. Then you have two others which did not read the data properly or ask even the right questions. More such pieces and more readers will turn away from data journalism. Writers and their editors need to be much more careful about data-related articles. Here is publishing articles prepared by another organisation. That does not mean it should not exercise its judgement or ask tough questions before taking a decision to publish such pieces.

The media is in a tricky situation now. Data or statistics – whether from the government or any other “authority” – is no longer trusted. Yet, writers have to use the information, sift through it and tell readers the story such data can tell. Journalists do not help themselves or their readers if they use statistics any which way they want to and only because it is out there.