The advancement in technology and the introduction of new technological processes have made the human’s life much simpler than ever. From inventing the computer to complex uses of Artificial Intelligence, we have transformed in a desirable sense. Today, the development of Artificial Intelligence has led the different sectors of any economy to utilise big data analytics for making various inferences about the probable happenings in the future and base their policies accordingly.

In simple words, Big Data can be understood as a large amount of information which is to be analysed to arrive at some useful conclusion. Big Data Analytics is referred to as a complex process of examining the big data to reveal some hidden correlations within the data. Among the different type of Big Data Analytics, Predictive Analytics is the most commonly used technique by various artificial intelligence experts. It is a forward-looking analytics technique, which helps in forecasting the future activities based on data set available at present.

Big data is used by companies, law enforcement organisations, and even the political parties for varied purposes. Predictive analytics plays a major role in the examination of big data from different organisations. Some of the applications of Predictive Analytics are mentioned below:

  1. Forecasting sales to optimise the product mix: Nowadays, predictive analytics is performed by different companies to observe the behaviour of their potential customers, thus predicting a fall or rise in their sales. These firms can reframe their pricing and output policies based on the inferences drawn from the big data available.
  2. Predictive Policing to bring down crime rate: Law Enforcement bodies and security services make use of predictive analytics either to observe the activities of suspects in a crime or to find the criminals by applying artificial intelligence to large sets of data collected. More often, OSINT (Open Source Intelligence) is used for this purpose, where data is obtained from various sources such as media, internet, social media, government reports, etc. and then inferences are drawn by connecting the various dots.
  3. Predicting the optimum campaigning strategy for political parties: Various political parties also make use of big data analytics to predict their performance by analysing various households in different constituencies and form their campaigning strategies accordingly.
  4. Recruiting top talent: Different firms are constantly in competition with each other to recruit the best talent. They often analyse the performance of their competitors and their employee-related policies based on data collected through their competitors’ websites, official records, and other sources.

It is an indisputable fact that the current Lok Sabha elections involved extensive use of Big Data by different political parties to understand the demographical variations in various constituencies. This data is arrived at by a study of the behaviour of different groups of people on social media.
The parties, along with various research agencies also conducted pre-polls and exit polls to predict the election results in advance. The predictive analytics approach followed for these poll predictions, therefore, use both the information drawn from open sources as well as by personal interaction (OSINT as well as HUMINT). Some of the parameters on which the algorithms and models of this AI are based are as follows:

  1. Post interactions: If a person is more involved in the social media posts promoting the propaganda of a particular party lately, this indicates the person might be a follower of that party. It includes the comment wars the person engages in during political debates, and the political stuff the person posts through his/her account.
  2. Pages followed on social media: The kind of pages followed by a person on social media handles are also indicative of the political group the person supports. For example, a person following Narendra Modi fan pages is more likely to be a supporter of BJP.
  3. Mutual friends: The social network of a person may influence his voting decisions. A study of the political views of the social circle of people may depict correlations.
  4. Residence/ location of the person: If a particular area has a majority of a particular community, a random person picked from that community is more likely to be a supporter of the party which the community supports.

From defense to law enforcement, from retailers to enterprises, Big Data Analytics is utilised across numerous domains. The above example of Elections is indicative of the fact that we might not even realise that we are making use of Big Data Analytics in our day to day lives.

(Written by Nischal Upadhyay for The Connectere)

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