Which of the following is a core goal of data mining in analytics projects?

Introduction / Context:
Data mining applies algorithms to discover patterns, relationships, and predictive signals in large datasets. A key goal is explaining or predicting events—why churn happened, which factors drive conversion, etc.

Given Data / Assumptions:

• Data mining emphasizes discovery and explanation/prediction.
• It is distinct from data warehousing (storage/integration) and simple confirmation of data presence.
• It explores both expected and unexpected relationships.

Concept / Approach:
Typical data mining tasks include classification, regression, clustering, association rules, and anomaly detection. These tasks help explain outcomes (e.g., what features lead to high risk) and forecast future events (e.g., likelihood of default).

Step-by-Step Solution:

Verification / Alternative check:
CRISP-DM and similar methodologies highlight business understanding and modeling to explain/predict outcomes.

Why Other Options Are Wrong:
They reflect either non-analytical tasks or infrastructure tasks, not data mining objectives.

Common Pitfalls:
Equating mining with reporting; mining goes beyond descriptive summaries to infer hidden structure and drivers.

Final Answer:
To explain some observed event or condition