Insights are meaningful interpretations of data that explain what is happening, why it is happening, and what action should be taken. Unlike raw metrics or charts, insights connect data to decision-making. A dashboard may show numbers, but an insight tells a story and drives action.

For example:

• “Revenue dropped 12% last week” is a metric

• “Revenue dropped 12% last week due to lower mobile conversion after the checkout update” is an insight

In BI and analytics, insights typically emerge from:

• Trend analysis

• Comparisons over time

• Segmentation

• Correlation analysis

• Anomaly detection

• Experiment results

Insights are often framed around business questions such as:

• Why did churn increase?

• Which segment is driving growth?

• What changed after a release or campaign?

• Where are we underperforming against targets?

From a technical perspective, insights depend on reliable data models, governed metrics, and sufficient historical context. Poor data quality or inconsistent definitions lead to misleading insights.

Modern BI tools increasingly automate insight generation using AI. These systems surface notable changes, explain drivers, and sometimes recommend actions. However, human judgment remains essential to validate relevance and business impact.

A common mistake is confusing insights with visualizations. A chart without interpretation does not guarantee insight. Effective analytics teams focus on interpretation and communication, not just reporting.

Ultimately, insights are the output that matters most. They are the bridge between analytics and action, turning data into better decisions.