understanding the future
As Descriptive Analytics, Predictive analytics operate over historical and recent data. However, their target is not to summarise the existing data to more actionable information. Rather, they attempt to predict data that are not actually available, either because they are missing or because they refer to the future.
To do this, Predictive Analytics use various statistical and machine learning methods to quantify the likelihood of a future outcome based on current trends and patterns.
Types of Predictive Analytics
- Predictive Modelling: What will happen if certain conditions hold?
- Root Cause Analysis: Why this actually happened?
- Data Mining: Identify correlations between data
- Forecasting: What will happen if the existing trends continue?
- Stochastic Simulation: What could happen and how probable is each possibility?
- Pattern Identification & Alerts: When should actions be undertaken to correct a process or sustain an outcome?
The capability of predictive analytics to clearly showcase probable outcomes and identify critical turning points, is the standing ground for the next evolutionary step of analytical processes, where computational methods are actually able to propose solutions for critical situations and optimise processes and approaches for the problems at hand. This concept of advanced analytics is known as Prescriptive Analytics and it is the point where data and advanced computational models lead to truly automatic, highly intelligent decision support.