‘Analytics is the way of extracting and communicating meaningful patterns in the data.’ In simple words, it’s converting raw data into actionable information. Along with Big Data, Analytics is now powering businesses across all verticals by improving operation efficiency, thus increasing the profit. As experts say, not all analytics are the same. To efficiently deploy analytics into the business, one must be aware of the types of Analytics and which one to use based on the requirements.
Analytics can be broadly classified into the following 3 types.
This type of analytics describes the data, in other words, it analyses and summarizes the complex raw data into a form that is understandable by humans. Though it is the most basic type of analytics, it is inevitable and around 90% of the organizations which use analytics rely on this technique. It is critical for the organizations to learn from their past performances and descriptive analytics comes in handy for the same.
Use case: The spendings from various accounts of a customer over a period of time are analyzed to provide a spending pattern. This tells the customer exactly where he is spending most of his income, which will be helpful in future financial planning.
It’s a step further to the traditional descriptive analytics. Here, the data is not only summarized but the analyzed patterns are used to predict the future course of the data. The outcome of the predictive modeling cannot be definite as they are probabilistic. In simple words, it provides only the probability of the occurrence of an event based on the provided historical data.
Use case: After spend pattern analysis is obtained from historic data the predictive analytics can be used to predict the future spend of the user. Say, the user has continuously gone on vacation for 3 consecutive years; predictive analytics algorithm will highlight the high probability of the user taking the vacation in the current year also.
It’s the most advance form of analytics. It uses emerging technologies such as AI and Machine Learning along with Predictive Analytics. Unlike predictive analytics, it not only provides the future course of the data but also the optimized path to achieve the best outcomes. This helps to identify uncertainties and helps to make better business decisions.
Use case: Prescriptive analytics can help in suggesting investment opportunities that are apt for the user based on their income, spend pattern and risk profile. It can also proactively help in financial planning and strategic decision making for the business.
Analytics has helped organizations in swift decision making, reducing cost and increasing profitability for different industries. It helps in mining useful information from tons of unutilized data. This would be an indispensable tool for all industries and will help them provide their customers with personalized products and services that are in sync with their needs and goals. A true revolution, yet to attain its peak!
About Market Simplified: Market Simplified is a thought leader in revolutionizing and digitizing products for financial institutions by continuously innovating and simplifying finance. We empower our customers with cutting edge digital experience that is highly personalized and enhanced for the end users with our ‘Experience Engineering’ platform driven by Analytics, AI, Machine Learning and Blockchain technologies. Our clientele includes industry leaders like OptionsXpress (Charles Schwab), Currenex (State Street), MB Trading, Maybank Kim Eng, Kotak Mahindra Bank, National Stock Exchange of India and many others across the globe.
About The Author: Gokoulane Ravi is a foodie, technology enthusiast, and a developer turned marketer with more than 5 years of experience in the space of mobility. When he is not working, he likes to read, write, run and cycle.