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Power BI insights help you discover important patterns in your data automatically. Instead of manually exploring every chart and number, you can ask Power BI to analyze your data and highlight interesting findings. Think of insights as your data assistant, pointing out trends, unusual values, and patterns you might have missed.
You can get insights from:
- Dashboard tiles
- Report visuals
- Report pages
For step-by-step instructions, see View data insights on dashboard tiles with Power BI.
What insights reveal
Power BI examines your data and applies advanced algorithms to find meaningful patterns. When you request insights, Power BI creates new visuals that highlight discoveries like unusual spikes, hidden correlations, and seasonal patterns.
The insights you see depend on your data. For dashboard tiles, you might see up to 10 different types of insights. For reports, Power BI automatically analyzes trends, unusual changes, and key performance indicators.
Common terms
As you explore insights, you might encounter these terms:
- Measure: Numbers you analyze, like sales totals, average scores, or counts. Measures answer questions like "how many?" and "how much?"
- Dimension: Categories that organize your measures, like product names, regions, or time periods. Dimensions answer "what type?" and "where?"
- Correlation: When two things change together in similar or opposite ways. For example, if ice cream sales increase when temperatures rise, they're positively correlated.
- Time series: Data points shown over time, like daily sales, monthly website visits, or yearly revenue.
Types of insights Power BI finds
Here are the insights Power BI can discover in your data. Each one helps you understand your data from a different angle.
Top and bottom performers
What it finds: Categories that stand out from the rest with much higher or lower values.
Why it matters: You can quickly identify your best and worst performers without manually comparing every category.
Example: If you're looking at sales by product, this insight might show that Product A sells 10 times more than any other product.
Significant changes over time
What it finds: Points in time when your data shifted direction or changed dramatically.
Why it matters: Helps you spot when something important happened that affected your metrics.
Example: Customer complaints dropped sharply in March, coinciding with a new product release.
Correlated patterns
What it finds: Multiple metrics that move together in similar or opposite directions.
Why it matters: Reveals relationships between different aspects of your business.
Example: Marketing spend and website traffic both increase during the same months.
Consistent values
What it finds: When values are remarkably similar across different categories.
Why it matters: Shows when performance is evenly distributed, which might indicate stability or lack of differentiation.
Example: All five regions have nearly identical customer satisfaction scores.
Dominant contributors
What it find: One category that makes up most of the total value.
Why it matters: It highlights concentration risk or your biggest driver.
Example: 80% of your revenue comes from one customer segment.
Unusual values
What it find: Individual data points that don't fit the expected pattern.
Why it matters: It flags anomalies that might need investigation or represent special circumstances.
Example: One store's inventory levels are three times higher than all other stores.
Upward or downward trends
What it find: Steady increases or decreases over time.
Why it matters: It shows the overall direction your metrics are moving.
Example: Monthly active users have been steadily increasing for six months.
Seasonal patterns
What it finds: Recurring patterns that repeat at regular intervals like weekly, monthly, or yearly.
Why it matters: Helps you anticipate cyclical changes and plan accordingly.
Example: Sales spike every December and dip every February.
Stable proportions
What it finds: When a category maintains the same percentage of the total over time, even as the total changes.
Why it matters: Shows consistent market share or stable distribution.
Example: The East region consistently represents 25% of total sales, whether monthly sales are $100,000 or $200,000.
Unusual dates or times
What it finds: Specific dates or times with values that differ dramatically from other periods.
Why it matters: Identifies exceptional days that might represent opportunities or problems.
Example: Website crashes on Black Friday caused a traffic spike 500% higher than normal.
Related content
Have more questions? Ask the Power BI Community.