June 4, 2025

Raina Ho

11th Grade

Lakeland Senior High School

Introduction

In countless movies and TV shows, there’s the infamous encounter of some sort of business meeting: an isolated room with a handful of people, looking at a screen of line graphs and pie charts. But what are they talking about, really? More often than not, business analytics tends to be the main point of discussion. It is described as the process of transforming data into insights in order to improve business decisions. Although it seems like a broad topic, the world of business analytics can be broken down into four key parts that make up the concept: descriptive, diagnostic, prescriptive, and predictive analytics.

Descriptive Analytics

For example, suppose that you look at a report that shows a sales income of $100k. Without context, this could either be a good or bad thing; this $100k could be an exponential increase from two months ago, or a dramatic drop in sales from a couple of days prior. In these types of situations, descriptive analytics will be the first one used to gather more information about the new statistic. 

Used as a means to put marketing and financial statistics in a greater context, descriptive analytics proves to be quite useful in the business world, providing data that investors and managers will use to pinpoint specific weaknesses and strengths in a given company. Without this form of analytics and its utility of comparing and contrasting, it would practically be a mystery as to how well or poorly a business is really doing. 

Diagnostic Analytics

As descriptive analytics shows how a certain event occurred, diagnostic analytics provides reasoning as to why a certain event occurred. 

For instance, if a company is losing customers, diagnostic analytics collects data from these individuals that broke off from the business and uses it to rationalize the occurrence. Whether it was due to dissatisfaction with a product, poor customer service, or some other external cause, diagnostic analytics will use these personal experiences and statements to give a “why” behind certain data trends.

Prescriptive Analytics

Giving insights as to what should happen next, prescriptive analytics is logically used after diagnostic analytics. Taking in that information as to why something occurred and employing it in order to plan the next step for a business. 

If diagnostic analysis shows that product dissatisfaction is the driving force behind customer loss, then the most logical thing to do is to rebrand the product—this means improving it to maintain your customer base. Making these decisions is exactly what prescriptive analysis is—a means to allow for smarter and more effective business positions. It is a fundamental statistical tool for the further development and success of any business. 

Predictive Analytics

Used to predict possible future trends, predictive analytics is used to help forecast certain events for businesses through the use of historical data. One tool that proves to be especially helpful in predictive analytics is regression analysis, which establishes relationships between certain variables in a business and how they affect one another. These predictions can either be long-term, when predicting future cash flow for a company in a year, or short-term, when predicting a number of sales in the next week. 

For instance, in a restaurant, a manager can predict when their rush hours are and how many customers they tend to get during this time by utilizing previously observed data from the establishment. Understanding this context, and the relationship between customer service, supply, and customers, the manager can then make the decision to staff more people and order extra supplies to satisfy this rush hour and possibly bring in more customers through the use of predictive and regression analysis. 

Conclusion

These business statistics are what help different organizations thrive; by translating data into easy-to-understand sets of information, the higher-ups of businesses can create feasible solutions to improve themselves.