In modern years, there is a growing demand in the field of business analytics. It actually means that what outcome we should get in business from the data to make better decisions. This is often sound like relating a business problem to an operation research problem. However, there is often a question that arises in connecting the business analytics to the operation research problem. In this blog, I will explain to you the meaning of business analytics and how it is related and useful in the operation research methods or decision making including linear programming, inventory management, simulation, and Markov Chains.
Analytics are used to identify (i) what has happened? (ii) What should happen? And (iii) what will happen? In the business. These three forms of question are categorized into Descriptive, Prescriptive and Predictive analytics respectively. Apart from the benefits and uses of business analytics, the main goal of business analytics is to identify which dataset will be useful and how it can be taken forward to solve the business problems and increase the profit, productivity, and efficiency.
Consider a bank that deals with both asset and liability products, and it is obvious that loans taken from the bank play a vital role in the revenue. Hence, the bank executive decided to hire a statistical consultant to find whether they end up in good loans, risky loans, paid-up loans or bad loans. Consider a bank that deals with both asset and liability products, and it is obvious that loans taken from the bank play a vital role in the revenue. Hence, the bank executive decided to hire a consultant to find whether they end up in good loans, risky loans, paid-up loans or bad loans.

In this example, the bad loans and the paid-up loans are the absorbing nodes or the end state in a Markov chain. The absorbing node is that it has no transition probability to any other nodes. So, as a statistical consultant, the first step is to understand the trends in the loan cycle with the previous study. Let’s say; the following Markov chain represents the pattern of loans for the previous year.

From the above transition diagram, it is clear that the bad loans and paid-up loans are the absorbing states; that is, the process end and stays in these states forever. Otherwise, paid-up loans cannot be a bad loan or risky or good and similarly, the bad loans cannot be paid-up or risky or good.