National Statistics Day: We live in a world of data analytics; here's what you should know in India

We are living through a data revolution. It is changing our world. Our choices as individuals are increasingly informed by more and more complex sources of data. Businesses are making decisions drawing on diverse and sophisticated information systems. Governments are making laws and improving public services with an ever-widening evidence base at their disposal. The Finance Industry is not indifferent to the use of Statistics – in fact, it deeply leverages Statistics and the science of it for very critical decision making. In jobs too, ranging from journalism to government policymaking, the demand for training in statistical literacy is increasing.

Statistics – facts generated from data or pieces of data- are hence quite vital in every field of human society. It is also a methodology of collecting, organizing, interpreting, and presenting data for conducting an Analysis -- an analysis which can help in making certain decisions or predictions about future based on a few tested assumptions and past data.

According to Kalpana Pandey MD & CEO CRIF Highmark, each of us uses some type of insurance – be it a life cover or health insurance or vehicle insurance. The premium of these insurance are calculated based on Statistics. Traders use Statistics and forecasting models to invest and make money in financial markets. Banks use statistics to assess how much is withdrawal is expected every day so that they can identify how much to lend.

It adds, “The analysis of economic, budgetary and financial developments prepared for each fiscal and monetary policy meeting influence the interest rates people and businesses pay. So statistics can indirectly affect the lives of many.

Here's a list of factors as per CRIF Highmark, one should know about data analytics.

Statistics for informed decisions

Statistics - discovering new trends and making predictions – are very important for any business. More so for information businesses such as Credit Bureaus as they help banks, NBFCs, and other companies in making objective decisions. Much of their work, be it related to Credit Score function, Analytics, or other Information solutions, depends on Statistics and Statistical Analysis – a necessity for informed decisions. Sound decisions require high-quality statistical analysis. A Good quality analysis will depend on relevant data and robust methodology.

A Credit Report provides information about your past a behavior on the loans and credit cards you had taken. However, as Peter Drucker said “you cannot manage, what you cannot measure”, Credit Information industry also evolved a statistic called “Credit Score” to enable measurement of creditworthiness through the credit history. Credit Score has now become a very critical tool in bank’s loan decision process.

Importance of methodology

The choice of parameters and which statistical measure – mean, moving average, median – to use comes with a deeper understanding of statistical science, the decision which this analysis is supposed to support and the data at hand. Two indicators of average - mean or median - give different interpretations from the same data.  The Methodical and scientific approach provides for trustworthy statistics, supporting the credibility of the outcomes.

Credit Score is indeed developed using complex statistical algorithms and a globally proven methodology to be able to predict whether you as a potential borrower are likely to repay on your loan once approved and disbursed by the bank. Adherence to strict guidelines and best practices are ensured for credit score to be accurate, consistent and unbiased.

The challenges of finding Relevant data

Collecting relevant and granular data is pre-requisite to any statistical analysis. Gathering detailed information can be quite an uphill task as all of what is desired may not be available and even if available, it may be challenging to merge such diverse data sets.

Identifying what is relevant and how to use it is also key to understanding the outcome of the statistical analysis. A credit score traditionally gets calculated on account and customer level data collected from various banks and lenders. This data is verified and controlled by the sharing institution. New age credit scoring is exploring the use of alternative data sources such as the use of mobile, payments, activities on social media, psychometry etc. to establish the creditworthiness of an individual. How one behaves on Facebook, how many friends do one has, what kind of pictures one posts, what kinds of posts one likes, what kind of pages and topic one follows, what information have I revealed on my social page – all are data points around one’s social activities.

Creating some meaningful information from a vast amount of data is what Statistics is all about.

Original Source: ZeeBiz

(Reprinted)