Since India’s Lok Sabha election results, news outlets have been debating why Congress lost so heavily to the Bharatiya Janata Party (BJP) last Friday. Some have suggested Narendra Modi’s focus on the economy was a decisive factor in his success, while others have argued that Modi’s emphasis on infrastructure development was a key reason. However, it is unanimously agreed upon that it was Modi’s combination of technologies – big data analytics and social media (see twitter graphs below) – that separated him from other candidates like Rahul Gandhi and was crucial to his victory. Whereas the latter has garnered considerable media attention, the former has remained somewhat unexplained as a factor in Modi’s success.
What makes Modi’s use of big data so impressive is that it was both relatively new to Indian politics, and wrought with unique challenges. Take, for example, the size of the Indian electorate. With 814 million voters, in comparison to the USA’s 193.6 million and the UK’s 45.5 million, the sheer volume of data of India’s voting population was perhaps the largest obstacle. The second was the variety of data – India’s voter rolls in 12 different languages and 900,000 PDF’s amounting to 25 million pages made for a heterogeneous, non-uniform and deeply diverse information set. Finally, the veracity of the information was often questionable – one report noted that some voters were listed as 19,545 years old, and others a confounding 0 years old. Name overlapping (there are 327,000 women named “Sita” in Bihar alone) only further complicated the process.
Which is why in our interview with Milind Chitgupakar, the Chief Analytics Officer of Modak Analytics (a company that has built India’s first Electoral Data Repository), Chitgupakar stressed the ingenuity necessary to effectively collect and represent Indian data: he and his group of 10 data scientists used everything from heat maps and data visualizations to complicated machine learning algorithms in order to sift through the volume, variety, and veracity of the data before making it available to political parties in India.
Despite these challenges, the rewards – as Modi has clearly demonstrated while employing this data to “drive donations, enroll volunteers, and improve the effectiveness of everything from door knocks…to social media” – are significant. BJP’s website, for example, planted cookies on all computers that visited its site, and then used information about these users’ further internet activity – i.e., the sites they visited after BJP’s – for customised advertisements:
“If you move out of the BJP website and visit a website for bikes followed by a search on jobs, the algorithm will make the inference that you are a young male from a particular constituency, say Delhi, who is currently on a job hunt. What happens next is when you visit a job searching portal like Naukri.com, this system pops up a contextual ad for you like ‘jobs in Delhi’. The BJP banner which is just below the results will tell you ‘There are no Jobs in Delhi. India deserves better’.” – source
Tactics like these — both online and offline analytics and marketing — were the backbone to Modi’s success. He lead the charge with both social media and the analysis of publicly available data. Whereas Indian politicians have been known to rely on “hunches and intuitions to gauge complex demographics of caste, religion, community and localities…,” Modi’s example, like Obama’s in 2008 and 2012, shows that politicking need not be an imprecise affair, even in a landscape as diverse and challenging as India’s.
Furhaad worked as a researcher/writer for The Times of London and is a regular contributor for the Huffington Post. He studied philosophy on a dual programme with the University of York (U.K.) and Columbia University (U.S.) He is a native of London, United Kingdom.
Email: furhaad@dataconomy.com
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