The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure.Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis.
The next phase is predictive analytics. Predictive analytics answers the question what is likely to happen. This is when historical data is combined with rules, algorithms, and external data to determine the probable future outcome of an event or the likelihood of a situation occurring.
The final phase is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Prescriptive analytics can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option.
Uses data aggregation and data mining to provide insight into the past and answer: “What has happened?”
Uses statistical models and forecasts techniques to understand the future and answer: “What may happen?”
Uses machine learning and simulation algorithms to advise on possible outcomes and answer: “What should we do?” to influence “What may happen?”
In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
See the section on Machine and Deep Learning for more information.
Applications of advanced analytics across different functional areas are numerous, some are depicted in the image below.