In predictive analytics, what is primarily used to determine the likelihood of future events?

In predictive analytics, the primary focus is on using historical data combined with business rules to forecast the likelihood of future events. Historical data provides a rich foundation of previous occurrences that can be analyzed for patterns, trends, and correlations. This data becomes instrumental in creating models that can project future outcomes based on what has happened in the past.

The integration of business rules further refines these models by incorporating organizational parameters and expert knowledge, which helps tailor the predictions to the specific context of the business. Together, historical data and business rules enable analysts to build robust predictive models that enhance decision-making processes.

Other options, while relevant in their own domains, do not primarily drive predictive analytics in the context of forecasting future events. Current market trends and qualitative data might provide valuable insights, but they do not serve the same foundational purpose as historical data in modeling. External audits and inspections are more focused on compliance and governance rather than prediction. Employee performance reviews, while indicative of individual contributions, are not structured to foresee broader future events. Thus, they do not have the same predictive power as the combination of historical data and business rules.