The intelligent data insight analysis assistant helps users intelligently build and modify dashboards, as well as interpret dashboards. This article introduces how to use the analysis assistant's intelligent dashboard feature to interpret dashboards.

Intelligent dashboard interpretation leverages large language model (LLM) capabilities to automatically perform in-depth analysis of global dashboard data, enabling users to accurately grasp data value and efficiently support decision-making.

• Quickly summarize data in charts and extract key information
• Enable instant attribution linkage and data source verification
• Supports custom interpretation templates, providing both template-based and free-form interpretation configuration options

1. Before using intelligent dashboard generation, the corresponding dataset must have the analysis assistant capability enabled. On the "Dataset" details page, click the "Enable DataWind Analysis Assistant" button.
   

2. Enable interpretation. When users open a dashboard, the "Dashboard Interpretation" entry can be found in the upper right corner of the chart.
   

3. Interpretation configuration. Both template-based and free-form interpretation methods are supported. Click "Go to Configure" or the "Interpretation Configuration" icon in the upper right corner to open the interpretation configuration window below, where users can select any interpretation configuration method.

   warning

   Users need to have edit permissions for the dashboard. For details on how to apply for permissions, refer to Detailed Explanation of Permission Features.

   

   Template interpretation	Free interpretation
   The user enters the template text, then selects the intelligent paragraph sections, and configures the prompt for each intelligent paragraph. The large model generates the processing Python code for each paragraph based on the prompt, executes the Python code, and produces the interpretation results.	The large model generates interpretation results by obtaining dashboard chart data based on the interpretation idea prompt entered by the user.
   Compared with free interpretation, template interpretation has the following characteristics: More focused debugging: Debugging is performed for a single paragraph, making it more focused More stable results: The LLM is only called during debugging. The generated Python code is fixed, and only the code is executed each time thereafter, resulting in more stable results Faster computation: As above, since only the code is executed each time and the LLM is not called, the response speed is faster

4. Refresh interpretation. To regenerate the interpretation result if dissatisfied, click "Refresh" to generate a new interpretation result.
   

Intelligent dashboard interpretation allows users to set interpretation templates according to business scenarios, providing both template-based and free-form interpretation configuration options, which users can choose as needed.

Template-based interpretation #

The template interpretation feature applies a cloze test-like process to the template text entered by the user, helping users configure and debug prompts specifically for the flexible parts of the interpretation template.

1. Switch to the "Template Interpretation" page and enter the template content on the left side.
   

2. Select the parts that require AI-assisted generation and convert them into intelligent paragraphs.
   

3. Configure the prompt for each intelligent paragraph. Please use natural language to describe the data extraction logic and style for each intelligent paragraph. You can use "@" to insert objects such as datasets or charts, and you can also specify rules such as "red for increase, green for decrease" or "add a special symbol at the end if the value is greater than 10 percent".
   
   The "Prompt Optimization" feature is provided. Click the "Prompt Optimization" icon to view the optimized result. To use the optimized result, click "Confirm"; otherwise, click "Cancel" to exit the local prompt optimization page.
   

4. Local preview result for each intelligent paragraph. Click "Preview" to view the interpretation result of the paragraph.

   warning

   The preview effect of intelligent paragraphs is based on the interpretation results obtained from the initial dashboard data and is not affected by changes to filter options.

   
   The local preview result is as follows. You can switch to view the result preview, generation logic, and thought process.

   Result preview	Generation logic	Thinking process

   If you are not satisfied with the preview result of the intelligent paragraph interpretation, click "Edit" or "Adjust Based on Generated Python".
   

   • Edit: Return to the intelligent paragraph prompt input step to adjust the prompt content.
     
   • Adjust Based on Generated Python: Open the generated Python code and adjust the code directly as needed.
     

5. Global preview of the interpretation result. After configuring the prompts for all intelligent paragraphs, click "Preview" to view the overall interpretation result.
   
   The overall interpretation preview result is as follows. Click "Save" to activate the template.
   

6. Click "Dashboard Interpretation" again or refresh the interpretation content to use the configured template for interpretation.
   

7. In addition, the "Template Interpretation" area also provides the following features:

   • Insert intelligent paragraph: After insertion, you can configure the generation logic on the right. The AI will obtain data and generate paragraph content according to the instructions.
     
   • Auto display toggle: When enabled, interpretation content is automatically displayed to viewers accessing the dashboard; when disabled, viewers must manually turn on the interpretation toggle to view the content.
     
   • Data limit settings: You can enable the data limit toggle for individual or multiple charts. If not enabled, the data limit for chart retrieval is 1,000 records; when enabled, the limit increases to 10,000 records.
     
   • Supports redo, undo, set the text format of smart paragraphs, delete individual smart paragraphs, clear all smart paragraphs, and more.
     

Free interpretation #

The large model generates interpretation results by obtaining dashboard chart data based on the interpretation prompt entered by the user.

1. Switch to the "Free Interpretation" page and enter the content of the free interpretation template below.
   
2. Click "Preview" to view the interpretation results.
   
3. If you are not satisfied with the new interpretation results, click "Return to Edit".
   
4. You can adjust the content of the free interpretation template or click "Restore Default" to revert to the default interpretation approach.
   
5. After completing the settings, click "Save". By enabling the "Dashboard Interpretation" feature again or refreshing the interpretation content, you can use the customized interpretation analysis approach.
   
6. The dashboard's free interpretation feature also provides a "Knowledge Base" function, allowing you to configure the free interpretation template content in conjunction with knowledge base content.
   
7. In addition, free interpretation also supports undo, redo, and data limit adjustment features.
   

The intelligent dashboard supports interpretation of all chart data. Click to enable "AI Interpretation"; an "Interpreting" prompt will appear at the top of the dashboard. Once interpretation is complete, users can view the interpretation content.

• It automatically extracts and summarizes key chart points such as outliers, trends, rankings, and more.
• In addition to automatically extracting key chart information, two advanced features are provided: real-time attribution linkage and data source verification.

Real-time attribution linkage #

The real-time attribution linkage feature is an important component supporting the intelligent dashboard interpretation function in achieving in-depth data insights. This feature is based on preset attribution analysis logic, automatically selecting the metrics in the chart that require change analysis, intelligently breaking down metrics and dimensions, and quickly identifying the contributing factors behind the changes.

• The system can perform attribution analysis from both the dimension and metric levels, accurately pinpointing the key factors driving data changes, and prominently presenting the top three positive contributing factors (which drive positive data changes) and negative contributing factors (which lead to negative data changes).
• In-depth impact analysis can be conducted for each dimension and metric, with interactive visualizations displaying key data such as contribution values and contribution rates for dimensions like merchant segmentation and industry classification. Users can click on related chart elements to drill down from macro data to micro details, quickly identifying the root cause of data changes and providing a reliable basis for business decisions.

Data source validation #

Intelligent dashboard interpretation supports validating data mapping relationships and closely linking the core conclusions generated by the interpretation with the data dashboard.

• When key data in the conclusion is clicked, the corresponding original data trend chart automatically pops up, enabling users to drill through from textual conclusions to visualized data. This allows users to quickly trace data details from the conclusion, assisting in validating the logic of the conclusion and facilitating the exploration of deeper data correlations, thereby improving analysis and decision-making efficiency.

Copy interpretation content #

You can copy all interpretation content.

Refresh interpretation content #

If the interpretation result is unsatisfactory, you can adjust the template and click the refresh button to regenerate the interpretation result.

Subscribe to interpretation results #

After enabling the intelligent dashboard feature, click "Create subscription". In the subscription summary or interpretation settings, you will see the "AI-powered intelligent interpretation" switch. Once enabled, the subscription notifications will include dashboard intelligent interpretation content. For detailed steps on how to configure subscriptions, please refer to Data Subscription.

Effects of subscription notifications after enabling "Large Model Intelligent Interpretation":

Quickly locate charts #

• When searching for charts, supports locating the chart on the canvas
  
• Clicking an inserted chart also allows you to quickly locate the chart
  

Knowledge base integration #

The "Free Interpretation" module of dashboard intelligent interpretation provides integration with the knowledge base. By leveraging existing industry patterns, historical data conclusions, business metric definitions, and other content in the knowledge base, the "Free Interpretation" module can automatically associate information and generate evidence-based interpretation reports, eliminating the need for manually referring to materials repeatedly or manually organizing analysis logic. For knowledge base-related information, see Knowledge Base Overview.
Specific applications:

• Generate analysis dimensions based on the knowledge base
• Embed knowledge base explanations in interpretation reports
  

Interpretation writing recommendations #

When using data interpretation, crafting high-quality prompts is key to obtaining ideal output. A clear, specific, and well-organized prompt can significantly improve the response quality and efficiency of data interpretation. The following provides basic conceptual approaches, writing suggestions, and reference examples to help users make better use of data interpretation.

Configuration interpretation example #

Template interpretation example #

Free interpretation example #

The final output consists of two parts of data:
## Overview
Summarize the content of the dashboard without presenting specific data
## Key Findings
Analyze the data in the dashboard and summarize some key findings


## Interpret Method Reference
1. Verified GMV is the core metric we focus on.
2. Attention should be paid to abnormal increases or decreases in GMV (fluctuations exceeding 3 pp), or continuous increases or decreases for 3 days or more.
3. When there is abnormal fluctuation in the verified GMV, it is possible to drill down through dimensions such as industry, merchant segmentation, and region to analyze the reasons for the fluctuation. Analysis can also be conducted from the perspective of relevant metrics
4. The calculation logic between relevant metrics is as follows:
- verified GMV = pay GMV - refund GMV
- verified rate = verified GMV/pay GMV（verified rate>80% is considered as meeting the standard，<80% is considered below the standard）

## The data format of metrics
1. GMV, display numerical values with 2 decimal places. When the value is large, unit conversion can be performed for "ten thousand", "one million", and "ten million".
2. Verified rate, the percentage is displayed in the percentage form, and the decimal places are retained to 1 decimal place.

Key Findings

Abnormal fluctuations in verified GMV:
In data from last 10 days, there are various abnormal fluctuations of verified GMV. For example:
-8.30%（2025-08-18）。
3.45%（2025-08-19）。
-9.64%（2025-08-20）。
These fluctuations indicate significant instability in the verified GMV and require further analysis of the reasons.

Verified rate compliance status:
The verified rate has not met the standard for 5 out of the past 10 days(below 80%). For example:
78.87%（2025-08-18）。
79.83%（2025-08-21）。
The low compliance rate indicates that business efficiency needs to be improved.

Continuous increase trend:
On August 23, 2025, there was a continuous three-day increase trend in verified GMV, which may be related to promotional activities or other factors.

The impact of refund GMV:
The change in refund GMV has a direct impact on verified GMV and verified rate. For example, the refund GMV on 2025-08-26 was 149,100, resulting in a decrease in verified rate to 79.54%.

The relation of pay GMV and verified GMV:
The growth of pay GMV does not always lead to synchronous growth of verified GMV. For example, on 2025-08-22, the pay GMV was 76,3400, but the verified GMV only increased by 1.80% month-on-month.

Next step suggestion
Drill-down analysis:
For the abnormal fluctuations in verified GMV, we can drill down from dimensions such as industry, merchant segmentation, and region to identify specific reasons.
Analyze dates with high refund GMV and identify merchants or industries with high refund rates.

Optimize verified rate:
Check for systemic issues or merchant performance problems with dates where verified rate does not meet the standard.

Monitor continuous trend:
Pay attention to the continuous increase or decrease trend of verified GMV and adjust operational strategies in a timely manner.

(1) All content output by this service is generated by artificial intelligence models.
(2) Applicable users for this feature: limited to customers' internal, proprietary business, and self-use only. Customers are not permitted to provide generative artificial intelligence services to the public via the analysis assistant.
(3) This feature is a value-added module and supports both SaaS and on-premises deployment versions. For purchase or trial, please contact sales personnel for consultation.
(4) Currently, the AI capabilities of Intelligent Data Insight DataWind support the following types of datasets:

• Supports extracted datasets and supports datasets uploaded as Excel/CSV files.
• Supports direct connection datasets, currently including only: ClickHouse / ByteHouse, Doris / selectdb.