---
schema_version: "1"
id: e576aeff42c19e5281ff117f3b43acd74204bb93aef9375967c86c5a09ea7b31
title: Tips for Improving Performance in Tableau Agent with Data Analysis
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/tua_metric_analytics_qa_bp.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.tua_metric_analytics_qa_bp.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 7246d648daefd0ed00818cdd19ce7d2c663abfd3184aa19cf6b2b51afcf43fdb
description: Multiple factors can affect performance in Tableau Agent in Tableau Next. This topic lists tips for improving query performance when asking questions in Tableau Agent with Data Analysis.
products:
  - Tableau Next
release: "264"
---
# Tips for Improving Performance in Tableau Agent with Data Analysis

Multiple factors can affect performance in Tableau Agent in Tableau Next. This topic lists tips for improving query performance when asking questions in Tableau Agent with Data Analysis.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;"><a href="https://help.salesforce.com/s/articleView?id=analytics.tua_editions.htm&amp;language=en_US" target="_blank">View supported editions.</a></td></tr></tbody></table>

For a complete overview of the phases required to implement Tableau Agent, see, [Plan Your Tableau Agent Implementation for Conversational Analytics](https://help.salesforce.com/s/articleView?id=analytics.tua_ai_concierge_plan.htm&release=264.0.0&type=5 "As an analyst implementing Tableau Agent in Tableau Next, you play an integral role in getting the agent to work well for your organization. Successfully enabling conversational analytics in Tableau Next includes five major phases.").

**Prepare the dataset intentionally**

- Design joins carefully. Avoid many-to-many or non-strict joins (such as BETWEEN) that expand result sets and slow queries. Example: If joining Opportunities to Accounts, ensure join keys are unique (such as AccountID) to prevent result explosion.
- Push complexity into ETL. Heavy calculations or derived fields (such as Forecasted Revenue) should be pre-computed in Data Cloud transforms, not recalculated at runtime.
    
    Example: Instead of computing _forecasted opportunity close rate_ in runtime, compute it in ETL and expose it as a metric.
- Handle large fact tables (>100 million rows) thoughtfully. Pre-compute aggregations (for example, monthly revenue totals) or apply semantic model filters to reduce data scanned at runtime.
    
    Example: For a Sales fact table with 500 million rows, pre-aggregate ACV by quarter to help Concierge answer "Show me ACV by quarter" without scanning the full dataset.

**Follow best practices for semantic model design**

- Include only objects and fields needed for the business.
- Streamlined semantic models ensure faster query generation and improve the accuracy of field mappings.

For in-depth guidelines for designing and refining semantic models in Tableau Next, see [Design Semantic Models for AI Readiness](https://help.salesforce.com/s/articleView?id=analytics.tua_ai_sm_design.htm&release=264.0.0&type=5 "A well-designed semantic model is the key to getting accurate and relevant insights with agentic analytics in Tableau Next. It helps your analytics agents understand the data and confidently answer questions. When designing your semantic model, build on a foundation of AI readiness standards and well-established industry best practices for data modeling and data quality.") and [Build an AI-Ready Semantic Model](https://help.salesforce.com/s/articleView?id=analytics.tua_ai_build_guide.htm&language=en_US).

**Pre-define metrics**

Define metrics in the semantic model that directly support frequently asked utterances.

- Metrics provide context that can help guide analytics agents for understanding where and when specific logic applies.
- Defining metrics improves latency by avoiding expensive ad hoc query paths and ensures consistency across your org.
    
    Example: "What is my ACV?" creates the ACV metric once in the semantic model. Future utterances referencing "ACV" benefit from faster routing and consistent logic.

**Ask targeted questions**

Ask targeted questions rather than open-ended questions. Targeted questions reduce the amount of data that must be analyzed, improve query latency, and prevent timeouts on very large datasets.

- Limit the scope of your questions (utterances) to reduce the query scope.
- Ask questions that clearly specify time ranges. Example: "Show me sales for Q1 2025" vs. "Show me sales"
- Simplify questions to include fewer categories, measures, or aggregations.
- Avoid asking for long lists of records. Ask questions that don't require thousands of records to reduce the query scope. Example: "What are the top 10 opportunities?" vs. "Show me all opportunities?"