Which of the following is not a capability or typical purpose of a BI reporting system in an enterprise analytics stack? Select the single best answer.

Introduction / Context:
BI reporting systems transform data into dashboards and reports. They summarize, visualize, and distribute information rapidly. Predictive modeling, however, is a separate discipline typically handled by data mining or machine learning components.

Given Data / Assumptions:

• BI reporting focuses on descriptive analytics.
• Predictive analytics requires statistical/ML algorithms and model training.
• Modern platforms may integrate both, but conceptually they are distinct layers.

Concept / Approach:

Reporting systems excel at aggregations, KPIs, and dissemination across devices and portals, including connecting to multiple (disparate) sources via semantic or data virtualization layers. Statistical prediction (e.g., forecasting, classification) belongs to data mining/ML pipelines, whose outputs may then be displayed by BI tools.

Step-by-Step Solution:

Verification / Alternative check:

Architecture diagrams show ETL/ELT → warehouse → reporting for dashboards; separate ML services produce forecasts that reports can display but not train.

Why Other Options Are Wrong:

Create meaningful information: core purpose. Timely delivery: scheduling/alerting features support this. Use disparate sources: supported via connectors/semantic layers.

Common Pitfalls:

Assuming a dashboard tool that displays a forecast also built the model; conflating visualization with modeling.

Final Answer:

use statistical procedures to predict future events