What is predictive analytics?

Predictive analytics is a branch of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.

Here are the steps to understand predictive analytics:

1. Data Collection: The process begins by collecting data from various sources. This could be customer data, business data, demographic data, etc.

2. Data Analysis: The collected data is then analyzed to identify patterns and trends. This is done using various statistical tools and techniques.

3. Statistical Analysis: This involves the use of statistical algorithms and machine learning techniques to identify trends, patterns, and relationships within the data. This could involve regression analysis, clustering, classification, etc.

4. Model Creation: Based on the results of the statistical analysis, predictive models are created. These models attempt to represent the underlying structure of the data and can be used to predict future outcomes.

5. Deployment: The predictive model is then deployed into the operational systems. This could involve integrating the model with the existing IT infrastructure.

6. Monitoring: Once the model is deployed, it is regularly monitored to ensure it is providing the desired results. If not, adjustments are made to improve the model's performance.

Predictive analytics is used in various fields like marketing, finance, healthcare, etc., to predict future trends and make informed decisions.