Predictive analytics refers to the use of statistical algorithms, machine learning and data mining techniques to predict future events based on historical and current data. This analytical process is used in many areas, from marketing and finance to healthcare and production, to make informed decisions, minimise risks and maximise opportunities.
At its core, predictive analytics uses complex mathematical models to identify patterns and trends in large data sets. These models can then be used to predict future outcomes with a certain degree of probability. Although the accuracy of predictions depends on various factors such as the quality of the data used, the complexity of the model and the specific variables included in the analysis, predictive analytics generally provides valuable guidance for decision-makers.
A well-known example of the use of predictive analytics is credit risk assessment. Financial institutions use historical data on payment behaviour and other relevant factors to assess the risk of credit default and evaluate creditworthiness accordingly. In the healthcare sector, predictive models can be used to assess the risk of the outbreak of certain diseases and take preventive measures.
The technology behind predictive analytics is constantly evolving and is becoming increasingly powerful thanks to advances in artificial intelligence and big data. It offers organisations the opportunity to act proactively instead of reactively and thus gain a competitive advantage.