top of page
  • Writer's pictureChesapeake Group

Elections – The big winner is Big Data!

The Indian National elections – the largest elections held in the world with over 800 million voters – just concluded today with a landslide victory for the incumbent Prime Minister Narendra Modi and his party – the Bharatiya Janata Party. These are also probably the most diverse elections in the world held across length and breadth of a country which has over 2,000 ethnic groups and 22 major languages. Catering to the needs, wants and aspirations of this large and diverse electorate and getting the message right is the key challenge that any political group is looking to solve – and it is primarily a Big Data problem!

In this era, where companies such as Google, Facebook and Amazon are increasingly using the power of data to deliver the right products to the right people, political parties have also caught on to using technology to understand and communicate with their voter base.

Role of analytics – In recent history, the first industrial level use of big data was probably done in the 2012 US Presidential Elections. Political parties not only try and mine publicly available census data and consumer databases, but use social media – like Facebook and Twitter – to spread their message and identify voter trends and perceptions. Like in any other industry, social media marketing can also prove far more cost effective than the traditional channels. Over the years (Cambridge Analytica scandal aside) it has been proved the use of data analytics as key source for political decision making when it comes to elections.

Big data analytics start-ups rise – As political parties bank heavily on data-driven approach and less on instinct, several big data and social media analytics companies have gained momentum. As per NASSCOM, India is currently among the top big data analytics markets in the world.

Modak Analytics, a data analytics start-up, announced in 2018 that it has built India’s first electoral data repository. To deal with complexity of Indian demographics, it leveraged 64 Node Hadoop, PostgreSQL and servers that could store eight terabytes of data. Machine learning algorithms allow it to perform unsupervised learning on voters data to identify key voting patterns. The company developed a Rapid Extraction, Transformation and Loading (RapidETL) tool which uses fuzzy logic techniques to match data based on Indian names and address. Additionally, app based analytics platforms like Silver Push have played a pivotal role in understanding voter sentiments.

With emergence of blockchain based technology and c.460m Indian users joining internet in 2019, the space is filled with unparalleled opportunities for innovative analytic firms to drive next level of analytics based campaigning,


Os comentários foram desativados.
bottom of page