Data mining is the process of discovering patterns, relationships, and insights from large datasets using statistical and computational techniques. It goes beyond basic reporting to uncover hidden structures in data.

Data mining techniques include:

• Classification

• Clustering

• Association rule mining

• Regression

• Anomaly detection

Examples of data mining use cases:

• Identifying customer segments

• Detecting fraud patterns

• Finding product affinity (“customers who bought X also bought Y”)

• Discovering churn signals

• Analyzing behavioral trends

In BI contexts, data mining complements dashboards by answering deeper questions. While dashboards show what happened, data mining helps explain why patterns exist.

Technically, data mining often uses:

• SQL for aggregation and filtering

• Statistical models

• Machine learning algorithms

• Large-scale compute platforms

Data mining requires clean, well-structured data. Poor data quality leads to misleading patterns.

A common mistake is confusing data mining with data dredging — looking for patterns without a clear question. Effective data mining starts with a hypothesis or business problem.

Data mining feeds into predictive analytics, personalization, and optimization, making it a key capability in advanced analytics stacks.