Technology applied to the business sector is growing, accelerating in speed and complexity, but if there's a hot topic in the analytics scene, it is undoubtedly predictive analytics, so we are going to review what it is and what its practical applications are.
Before BI (Business Intelligence), data analysis was known as decision support. It consisted of a descriptive analysis of the situation based on historical data normally stored in a structured way in monolithic systems using OLAP (Online Analytical Processing) techniques. In this first stage, we can traditionally answer manually what has happened. By condensing structured data to be understood by people through visualizations, we can refer to any past event that we have recorded.
The next step on the path to truly becoming a data-driven company is through predictive and cognitive analytics. It is the difference between having a reactive attitude, in which it is often too late to act, and moving to a proactive way of working, anticipating events, trends, and the market.
Predictive analysis is an umbrella term to refer to the set of processes that involve applying different computational techniques in order to make predictions about the future based on past data. The variety of techniques used include data mining, modeling, pattern recognition, graph analytics, to mention some of them.
Predictive models apply known results in order to train the model to predict values, with different or completely new data, in a repetitive process. The modeling provides the results in the form of predictions represented by the degree of probability of the target variable based on the significance estimated from a set of input variables. The target variable can be sales, a person's face, the coordinates of an oil field, or anything else we can think of.
There really is no limitation in the uses of applying predictive analytics, they will depend on what we want to obtain. They are widely applied in almost any sector, not only in business, whether to detect business opportunities, detect and reduce fraud, customer retention, predict system failures but also in other fields in which we all benefit, such as detecting cancer in patients, the evolution of epidemics, cost savings in public organizations, speech recognition, the list is endless. We can, however, classify the types of predictive analysis and the most appropriate techniques to achieve the objectives.
