Transforming Decisions

The ultimate goal of data is to create value and this is largely in the ability we can perform analysis on it. Most of a good answer, as always, lies in a good question. It is from these questions that the information gathered can provide the answers needed to understand the past, its causes, anticipate the future or even optimize decisions by knowing the impact that will happen under certain circumstances. These variants that help us to understand, foresee and make decisions based on data are actually what we know as Descriptive, Diagnostic, Predictive and Prescriptive Analysis.

Descriptive Analytics: Understanding the Past.

Descriptive analytics focuses on describing what has happened in the past through analysis of historical data. It enables organizations to understand patterns, trends and key metrics to make informed decisions. For example, an e-commerce company can use descriptive analytics to analyze monthly sales, identify the most popular products and understand the buying behavior of its customers.

Diagnostic Analytics: Identifying the causes of the past.

Diagnostic analytics focuses on identifying the underlying causes of observed results. In the technology context, a technical support team might employ diagnostic analytics to identify and resolve recurring problems in a computer system.

Predictive Analytics: Anticipating the Future

Predictive analytics focuses on predicting future events using advanced techniques such as predictive analytics and machine learning. For example, an insurance company can employ predictive analytics to assess a customer’s risk and predict the likelihood of filing claims in the future. This allows them to adjust their strategies and policies to mitigate potential losses.

Prescriptive Analytics: Optimizing Decisions

Prescriptive analytics goes a step further by recommending specific actions to take to optimize future results. It uses sophisticated algorithms to suggest the best way to address a problem or take advantage of an opportunity. For example, a supply chain can use prescriptive analytics to optimize its delivery routes and minimize logistics costs.

Practical Example

Imagine a retail chain in the retail world that wants to improve its profitability. Using descriptive analytics (i.e., the past), it can review historical sales data to identify seasonal patterns and buying trends. Through diagnostic analytics, you can identify that specific characteristics are related to specific buying trends. With predictive analytics, you can forecast (future) demand for certain products based on factors such as weather or special events. Finally, with prescriptive analytics, you can recommend specific strategies to optimize your inventory, pricing and promotions in order to achieve certain results such as maximizing profits or securing inventory.

The effective combination of descriptive, diagnostic, predictive and prescriptive analytics not only provides a holistic view of an organization’s past, present and future, but also drives more informed and strategic decisions.