Data transformation and decision making

Mining is one of the most significant economic activities in India. It significantly contributes to the GDP share produced by the industrial sector of the country. With a large workforce involved in mining, the Government of India provides special care while laying down the policies for mining. In recent years, special attention has been provided to make mining sustainable and in compliance with environmental standards.

It is quite out in the open today that mining results in a great deal of environmental pollution. However, mining is the only way to extract precious metals, minerals, and ores, which play an important role in the manufacturing of consumer products. This is why restricting mining activity is difficult, and it might hamper the growth of the economy of a developing country like India.


Data transformation refers to the modification of the data so that various analytical tools can analyse them and generate inferential results, which can be used by the management for decision making. There are different types of transformation of data practiced by the banking systems. In this article, we shall discuss a few of them.


1. Data weightage attribution

When any banking system starts collecting the data of customers, it is done in bulk. This data has a lot of parameters. Some of these parameters hold more relevance to the banking system compared to others. Now, it is the responsibility of the banking system to differentiate between more and less important data. While putting this differentiated data into calculation, the banking system can assign attributes to them in the form of weights. It means that more important data will have more weight, and less important data will have less weight. So, the resultant inferences will be more accurate.

2. AI-driven data analytics

It is a type of data transformation done with the help of the epitome of current technology- artificial intelligence. Banking systems employ AI tools to detect patterns in customer data, which are used to predict results. This methodology has been proven to be quite useful to predict the behavioural attitudes of customers by analysing the pattern of their past transactions. The banking system can predict the perfect banking service, which would interest the customer.

3. Data visualization

This is probably the most useful type of data transformation for the management of core banking services. It refers to the manifestation of black and white banking data into visually intelligible graphs and charts. Such representation of the customer and transactional data drives management meetings in the right way and plays a pivotal role in decision making.


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