Plain Facts is Mint’s and India’s only daily data journalism column. Over the last year, from Monday to Friday, with just a team of 7, we have examined a major issue in India using data. Each story is centred around at least three charts and has 650 to 750 words narrative in support. In India’s noisy media environment, data journalism remains a nascent concept and visual data journalism in print is rarer still. Plain Facts has showcased the possibilities of using data in journalism – and especially how critical data is in revealing the truth.
Through our daily data journalism page, we’ve generated fresh, data-driven insights into both enduring and contemporary issues in India. Many of these issues, such as the functioning of Indian courts, had never been examined rigorously using data by the media or even academia. Consequently, our stories have not just informed readers but also influenced practitioners. For instance, our series on the problems in India’s legal system influenced the design of a ‘Data for Justice’ challenge, organized by a legal non-profit, to discover other problems in the system.
Plain Facts has also generated data in places with traditionally scarce data. Our series on cities, for instance, brought together different datasets to objectively assess India’s biggest cities on a range of metrics. Moreover, our data-based stories have been able to powerfully hold the government to account. For instance, ahead of the 2019 Indian elections, our report card series examined the performance of the ruling Bharatiya Janata Party (BJP) in different sectors (e.g. power, water and roads). Within this instance, one story on sanitation revealed the extent to which the BJP was exaggerating its achievements about improving India’s sanitation situation and triggered a response from the government. Like this, the series debunked many claims made by leaders across the political spectrum and added rigour to an otherwise frenzied approach to elections.
In generating our daily column, we use all the standard tools of data journalism: excel, R, python, illustrator and the Google suite (Google Docs for editing and Google Groups for internal communication). In addition to technical tools, we rely on expertise. With almost every story, we speak to an expert, someone who has worked with the story’s data extensively, to ask them how we should analyze and interpret the data.
What was the hardest part of this project?
Data in India suffers from major issues — both in terms of quality and quantity. Datasets (such as regularly updated employment numbers) that are considered commonplace elsewhere are nonexistent in India. And what data that does exist often lacks credibility and quality. Operating a daily data page in this data-scarce environment, with just a team of 7, has been our biggest challenge. This has meant some articles are less data-intense than others. For instance, a few stories simply describe a newly-released dataset. Yet we believe that this is still worth doing. The Indian media is yet to acquire an appetite for data-based stories so even our simpler stories add value to the news cycle.
What can others learn from this project?
In operating a daily page in a data-scarce environment, we have shown what can be done with limited data. We’ve been forced to be innovative in reusing and combining existing data from different sources. For instance, ahead of the elections, we compiled a member of parliament (MP) performance index that used data from three different sources to assess MP performance. In another story, we used rally location data and elections data to estimate the effect of a political rally by a certain leader on vote swing. We’ve also used proxy data to track major issues. For example, we’ve used Google Trends to examine important cultural trends in India (including the rise in porn consumption). Taken together, we believe we’ve shown other newsrooms what are the possibilities for data journalism in India.