Power BI's Smart Narrative feature is primarily designed to generate summaries and insights based on visualizations, not the entire dataset. However, it can be used to summarize large datasets indirectly by creating visuals that represent aggregated data and then leveraging Smart Narrative to generate insights based on those visuals. Here's how it works in various scenarios:
1. Summarizing Specific Visualizations (Charts and Tables)
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Visual-Based Insights: Smart Narrative is tightly integrated with Power BI visuals (such as bar charts, line graphs, tables, etc.). It analyzes the data within the visual and generates text-based insights that summarize key patterns, trends, and figures. It works well for summarizing specific charts, helping users understand the main takeaways at a glance.
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Limitations: It will only summarize what is represented in the visual. If the visual is filtered or aggregated to show only a subset of the data, Smart Narrative will reflect those changes and summarize the data within that visual context.
2. Summarizing Larger Datasets Using Aggregated Visuals
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Aggregating Data: For larger datasets, you can create aggregated visuals such as summaries, averages, totals, or key metrics (e.g., total sales, average revenue, etc.). Smart Narrative can then summarize these aggregated figures in a meaningful way. For example, a bar chart representing sales by region can be accompanied by a Smart Narrative that highlights the top-performing regions and overall trends.
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Performance Considerations: When summarizing large datasets through aggregated visuals, Smart Narrative will focus on the key metrics presented in those visuals, helping to avoid performance issues associated with analyzing raw data directly.
3. Summarizing Multiple Visuals with a Dashboard
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Combining Multiple Insights: While Smart Narrative does not summarize the entire dataset directly, you can combine multiple visuals in a Power BI dashboard and have Smart Narrative generate insights based on the combined data. Each visual will contribute to the overall insights provided by the narrative, allowing you to cover a broader range of data points without overwhelming the user.
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Dynamic Updates: As users interact with slicers or filters, Smart Narrative will update to reflect the changes in the visuals. For instance, when a user selects a specific time period or region through a slicer, the narrative will automatically adjust to summarize the data for that filtered subset, helping users explore large datasets more easily.
4. Using DAX Measures for Custom Summaries
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Custom Measures: To ensure Smart Narrative provides relevant insights across large datasets, you can create custom DAX measures that aggregate or analyze data in specific ways (e.g., total sales, percentage growth, or performance against a target). These measures can be used in conjunction with visuals and Smart Narrative to provide high-level summaries that are dynamically updated based on user filters.
5. Limitations for Entire Dataset Summarization
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Dataset-Level Summarization: Smart Narrative is not designed to summarize raw data at the dataset level (i.e., without visuals). If you want to summarize an entire dataset, you will need to first create visuals that represent key aspects of that data and then use Smart Narrative to provide insights based on those visuals. Large datasets can be summarized effectively in this way, but the narrative will always be based on the aggregated data displayed in visuals, rather than the raw data itself.
