Predictive analytics have the ability to anticipate risk and identify relationships that may not be apparent with descriptive analytics. Through statistical modeling, data mining, and other advanced techniques, predictive analytics can identify hidden relationships or patterns in huge volumes of data. This is integral to group or segment data into meaningful sets for detecting tends or predicting behavior.*
Organisation that are matured in descriptive analytics move into this level where they look beyond what happened and try to answer the question of “What will happen?”. Prediction essentially is the process of making intelligent/scientific estimates about the future values of some variables like customer demand, interest rates, stock market movements, etc. If what is being predicted is a categorical variable, the act of prediction is called classification otherwise it is called regression. If the predicted variable is time-dependent, then the prediction process is often called time-series forecasting.*
- What is likely to happen?
- What trends are foreseen?
- What are multiple alternatives and scenarios?*
The most common predictive analyzing techniques used are: classification, clustering, regression, association analyzes, graph analyses, and decision tree. These are advanced analytics that provide self-learning models that can be used in real-time applications and forecasting. The use of data, analytics, predictive modeling, machine learning help perform complex correlations on data to gain insights and are the future of organizations.*
- Fraud Detection
- Risk Mitigation
- Effectiveness of Marketing Campaigns
- Operational Enhancement
- Clinical Decision Making *