Answers generated from Webex transcript and chat interactions
Q: Why do executive summaries work on some dashboard sheets but not others in the same dashboard?
A: A few potential reasons:
- Different sheet types: Paginated report sheets don’t support executive summaries
- Data volume requirements: Need sufficient data/visuals to generate meaningful insights
- Single row tables may not have enough data to generate summaries
For specific troubleshooting, posting screenshots on the QuickSight Community is recommended.
Q: What is the licensing model for generative BI features in embedded form (executive summaries/data stories)?
A: The same licensing model applies for both embedded and non-embedded:
- Only available with registered user embedding
- Users need appropriate pro-level licenses (reader pro or author pro) to access generative BI features
- Licensing requirements are the same whether embedded or not
Q: Can we embed analysis into a wiki?
A: It depends on the type of wiki:
- Public-facing wikis require anonymous embedding, which doesn’t support generative BI features
- Internal wikis with authenticated users can use registered user embedding to include analysis capabilities
- For internal wikis, you can embed the console and generative BI features if using registered user embedding
Q: How accurate is the storytelling feature? Does it make up insights or only provide insights from raw data?
A: The storytelling feature:
- Pulls insights directly from dashboard visuals and data
- Doesn’t make up numbers or incorrect information
- Uses LLMs for recommendations based on prompting
- Different components include visual summaries, introductions, conclusions and recommendations
- Accuracy varies by use case and data provided
Q: Is it recommended to use topics for creating data stories or scenarios?
A: No - topics are specifically used to power the Q&A experience. Data stories and scenarios are built directly from dashboards, visuals and provided data.
Q: How do we navigate datasets with non-natural language fields? Are there natural language keywords that are most compatible with generative functions?
A: For non-natural language fields (like abbreviated column names):
- You can configure friendly names and synonyms when setting up topics
- This makes datasets more natural language friendly
- Topics allow you to define how different keywords map to your data fields