Types of data transformation

Banking systems across the globe are witnessing tremendous re-development. The methods and frameworks of these developments might be different and varied, but the objective of each of them is the same. All these efforts undertaken by the banking institutions are directed towards better and more personal customer service. The primary vehicle to reach this objective is data generated by customers themselves.

Today, the banking institutions have realized that they can serve the customers only after they understand their requirements and work to fulfil them. And what better way than customer data analysis to understand their requirements. It is the reason why banking institutions lay so much emphasis on the accumulation of customer data, both the characteristic data of the customers and the transactional data. Now, the mere collection of data would not serve any purpose unless made suitable for analysis.

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.