This article introduces the Quick BI Smart Q V6.1, detailing its five key capabilities that bridge the gap between AI and business logic to transform data analysis.

Since its launch in August 2025, Lingyang Quick BI Smart Q has proven its value in more than 100 business scenarios across industries such as retail, finance, and manufacturing. It has brought the vision of “an AI data analyst for everyone” closer to reality.

At the same time, real-world adoption has also revealed deeper user expectations:

“Smart Q can answer questions about data, but can it better identify the right table for me?”
“I want to use Smart Q to interpret reports. Can it do that without requiring me to craft prompts?”
“Our business is complex. Can Smart Q truly understand our attribution logic?”

We recognize that there is still a “last-mile” gap between data + AI capabilities and real business needs. To address this, the Quick BI product team has carefully refined Version 6.1, delivering major upgrades across five key areas: Volatility Attribution analysis, reports, data query, interpretation, and the enterprise knowledge base.

These upgrades not only solve the problem of finding the right data, but also enable AI to better understand each enterprise’s unique business logic and the different data consumption needs of different roles. The “AI data analyst for everyone” is evolving into a more capable business partner, helping enterprises build an end-to-end data-to-decision workflow powered by AI.

I.New Volatility Attribution Analysis Capabilities

Smart Q now supports multiple Volatility Attribution Analysis methods, covering a wide range of analytical scenarios and creating a one-stop experience from data query to driver analysis to reporting.

1.1 Multiple analysis methods with flexible orchestration

Common scenarios:

In industries such as e-commerce, marketing, and user growth, high-frequency and complex Volatility Attribution analysis is a common need. In the past, data developers had to master multiple advanced analytical methods to perform driver analysis on complex business processes. Business teams that wanted to understand metric fluctuations often had to submit requests to data teams, leading to cumbersome workflows and long turnaround times.

This disconnect between business needs and analytics often meant that by the time an analysis report was delivered, the window for action had already narrowed, making timely decision-making difficult.

Feature overview: Multiple Attribution methods to cover full-scenario analysis requirements

Supports a rich set of analytical methods, including dimension hierarchy analysis, dimension grouping, metric attribution, and metric correlation analysis.
Delivers the professional depth required for granular attribution analysis, while flexibly adapting to different business logics, enabling one platform for multiple scenarios and deep attribution insights.

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Multi-step driver-analysis orchestration for complex root causes

Multiple analysis nodes can be freely combined to build multi-level, multi-path intelligent driver-analysis workflows
Data analysts can package proven analysis approaches, and business users can reuse them with one click

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1.2 Seamless analysis after data query, with insights directly linked to reports

Common scenarios:

When business users discover fluctuations in core metrics through data querying, Smart Q can complete intelligent Volatility Attribution Analysis in seconds, seamlessly connect with the reporting system, and generate professional insight reports with one click.

Feature overview:

Multiple entry points to trigger insight attribution: Click the Intelligent Insights instruction in the shortcut panel of Smart Q data querying, or click Fluctuation Analysis in the returned results to trigger automatic attribution

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Insight attribution results linked with Smart Q reports: The insight attribution results include an overview of metric changes, attribution conclusions, and detailed analysis across multiple dimensions and metrics. These results also support being added to Smart Q reports

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II. The End-to-end Experience of Smart Q Reports is Upgraded Again

The upgrade in Smart Q Reports Version 6.1 realizes an intelligent closed loop for the full report lifecycle: one-click intelligent generation based on authorized datasets -> asynchronous background processing -> subscription and push across multiple channels -> report archiving. This significantly improves report generation efficiency, traceability, and the team collaboration experience.

2.1 Dataset selection and automatic report generation

Common scenarios:

Enterprises often have a large amount of data that has been specially processed by the IT or data team and transformed into datasets. When business users perform self-service analysis, they hope to generate reports based on this existing dataset data.

Feature overview:

Simple Generation: It supports selecting datasets that the current user is authorized to use and that have already been successfully learned as the data source for one-click report generation. These datasets can also be combined with other uploaded files for analysis.

Flexible Update: A dual-mode update mechanism is provided. When updating report data, users can choose whether to update only the dynamic content or perform a full update of the entire report.

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2.2 Asynchronous generation of reports in the background and instant reach across multiple platforms

Common scenarios:

When a user is waiting for a report to be generated, the user may accidentally close the browser or experience a network interruption. In such cases, the user would need to regenerate the report, which is inefficient.
After periodic reports such as weekly reports, monthly reports, and business analysis reports are generated, users want to retrieve the latest data overview at any time without logging in or manually opening the original reports. This allows each business stakeholder to stay informed of business dynamics, data fluctuations, and abnormal situations in a timely manner.

Feature overview:Asynchronous generation in the background

Smart Q reports support asynchronous background processing. Users do not need to wait for a long time on the current page. Even if they close the browser, they can return at any time to view the report results. If users prioritize other data analysis tasks during the waiting period (such as data processing and report creation) and switch to other feature pages, a prompt will be displayed after the report is generated.

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Subscriptions and Push:

Multi-channel push:Support scheduled push to multiple channels such as Email, DingTalk, WeCom, and Lark, making it more convenient to view reports regularly.
Multi-format support: Content delivered through subscriptions supports multiple forms such as screenshots, links, and data attachments (PDF and Word).

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2.3 Insight questions are traceable, and result comparison is supported

Common scenarios:

When writing insight analysis results into a report, you often need to repeatedly adjust the prompt content through AI conversations. You may also need to compare content generated multiple times in order to optimize the prompts and select the best result for application.

Feature overview:

Insight analysis in Smart Q reports can record and save the five most recent question contents and results for each user, and support multi-version comparison of generated results. Users can freely apply the optimal version of the content.
When historical results are applied, the system automatically validates the consistency of multiple insight analysis objects.

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2.4 Data archiving for worry-free traceability

Common scenarios:

Business analysis often requires regular comparison of historical data, but dynamically updated reports cannot preserve the content at a specific point in time. Report viewers are often unable to trace historical analysis conclusions because of changes in database permissions or modifications to the original reports. Manually taking screenshots for archiving every time is inefficient and cannot support comparison of data content across multiple versions.

Feature overview:

In the Preview state, users can create archived Smart Q reports. Users can also customize the archive storage path.
Archive records can be viewed at any time. Multi-version content comparison is supported to help users understand data development across different periods.

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2.5 Custom roles control report permissions, and collaborative editing is supported

Common scenarios:

To meet enterprises’ needs for data security and fine-grained resource management, and to efficiently support collaborative writing and maintenance of complex reports by multiple users, Smart Q reports provide flexible custom-role permission management capabilities.

Feature overview:

You can configure the usage permissions of Smart Q reports in Custom Roles. Access control can be implemented for specified users by assigning roles.
You can designate members within the organization to collaboratively edit reports, making report content creation more efficient.

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2.6 Automatic saving, chart splicing, and table paging greatly improve editing practicality

Common scenarios:

When editing reports in daily work, unsaved content may be lost because of abnormal situations such as accidentally closing the browser or network interruptions. In addition, when reusing widget content built in dashboards, you may encounter cases where the dashboard style is not compatible with the report document style.

Feature overview: Support automatic saving during report editing

After Automatic Saving is enabled, the system automatically detects content changes every five minutes and saves them. This prevents unsaved report content from being lost because of abnormal situations such as misoperations or network interruptions.

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Support inserting spliced widgets from dashboards into reports

When inserting dashboard widgets, if you select all spliced containers, the original spliced layout style can be retained and inserted into the Smart Q report. If you select only some widgets in the spliced container, they can be automatically downgraded to independent chart widgets.

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Support paging view of dashboard tables in reports

The original paging effects of crosstables and fact tables in dashboards can be preserved after they are inserted into reports. The mobile client also supports paging view of table data.

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III. Smart Q Chat Conversational Interaction Evolves Again

Smart Q Chat has been newly upgraded, further improving the accuracy, convenience, and intelligence of conversational answers.

3.1 Intelligent table selection makes asking questions faster

Common scenarios:

Business users care about business performance, but when they open BI tools, they are often blocked by the question of “where the data is.” The system now supports fully automatic intelligent table selection, truly delivering a “what you ask is what you get” experience.

Feature introduction:Support automatic matching of data sources: Even if users do not know which dataset or table stores the data, the system can automatically match the most relevant data source based on the semantics of the question and directly provide an answer.
Intelligent selection across multiple datasets: When a question can be answered by multiple datasets, the system automatically evaluates relevance, timeliness, and business context, and uses smart routing to direct the question to the dataset that is most likely to match the user’s intent. Users do not need to manually select datasets, avoiding unnecessary decision-making burden.

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3.2 Follow-up questions for clarification make queries more precise

Common scenarios:

When users conduct data query conversations, key elements are often missing, such as missing or vague time conditions, polysemy or ambiguity of metrics, or cases where only time is provided but core metrics are missing. Smart Q data query can now guide users to supplement key elements through follow-up questions.

Feature overview:

When a user asks a question, if key elements such as time or metrics are missing, or if there is ambiguity among multiple metrics, Smart Q proactively asks follow-up questions to clarify intent before providing a precise answer, significantly improving the accuracy of the Q&A.
Through the global configuration of Smart Q data query, you can enable or disable the follow-up question capability at the system level.

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3.3 Dimension value matching optimization for more accurate data query

Common scenarios:

When Smart Q data query learns a dataset, it mainly covers dimension values that appear frequently in that dimension. When a user’s question involves dimension values that have not been learned—such as new brands, less common regions, or custom tags—the system can dynamically choose a matching strategy based on field attributes:

Exact match: Applicable to fields that require high accuracy, such as order IDs or SKU codes
Fuzzy match: Applicable to fields with expression differences, such as “Beijing” vs. “Beijing City”
Intelligent rewriting: Applicable to expressions with similar semantics but different literal forms, such as “Phone” vs. “Smartphone”

Feature introduction:

In automatic mode: The system automatically determines whether the user’s dimension value should be rewritten to a similar learned dimension value when a direct match is not found
In custom mode: Administrators can individually configure whether rewriting is enabled for each dimension

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IV. Smart Q Insights Flexible Configuration Upgrade

Smart Q Insights has been comprehensively upgraded. It now supports flexible configuration of interpretation widgets in dashboards, full coverage of interpretation scope, and AI recommendations of analysis ideas together with quick questioning capabilities. This enables business users to conveniently obtain professional and actionable dynamic insights based on real-time data.

4.1 Support adding Smart Q Insight widgets in dashboards

Common scenarios: In the past, data interpretation often depended on static explanations or analysis reports prepared by analysts. This was costly and slow to update, making it difficult to respond to dynamic data changes. The Smart Q Insight widget allows developers to configure analysis prompts and automatically combine them with the latest data to generate intelligent interpretations in real time when the dashboard is accessed. This enables business users to instantly obtain professional and actionable insights.

Feature overview:

Support dashboard editors in adding Smart Q Insight widgets

Dashboard editors can configure interpretation logic and analysis ideas for dashboard data. The interpretation results can be added to dashboards as widgets.
When visitors view reports, they can generate new interpretation results based on the latest dashboard data to quickly gain insights.
Developers can also flexibly configure the position, size, and style of interpretation widgets.

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The interpretation scope is more flexible: Interpretation widgets support insertion into tabs and single-chart interpretation

Tab-level widget interpretation: Interpretation results can be directly inserted into tab containers. Users can select widget interpretation within a tab to interpret multiple charts inside a specific tab container.
Single-chart-level interpretation: Support interpretation of a single chart to meet fine-grained insight needs.

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4.2 Artificial Intelligence Recommendation of analysis ideas

Common scenarios:

When business personnel face Dashboards data interpretation, business personnel want to perform analysis on the current Dashboards data, but business personnel often do not know how to write appropriate Prompts. Business personnel also lack clear interpretation ideas, making it difficult to efficiently mine the Business Insight behind the data.

Feature overview:

AI recommendation: Smart Q intelligently recommends analysis ideas based on dashboard data.
Quick Question panel: A quick question panel is added so that users can quickly launch Smart Q Interpretation with one click.

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V. Enterprise Knowledge Base Vertical Business Understanding Engine

5.1 Enterprise knowledge base vertical business understanding engine

Common scenarios:

When enterprises in different industries conduct daily data analysis, they often rely on their own exclusive business logic and analysis approaches. This accumulated content gradually forms an enterprise-specific knowledge system. However, general-purpose large language models often rely on generic semantic understanding when analyzing requirements, and therefore tend to generate broadly applicable analysis results rather than results tailored to the enterprise’s own business context and analysis habits.

Feature overview:

Connect to the enterprise knowledge base: You can connect to accumulated enterprise knowledge by adding knowledge documents (such as local files and Lark documents), or by directly connecting to third-party knowledge bases (such as Alibaba Cloud Model Studio, Tongyi Qianwen, and Dify)
AI learning and extraction: Based on the connected enterprise knowledge base, the system can intelligently learn and extract different knowledge types, such as the enterprise’s unique business logic, analysis approaches, and content templates, and store them accordingly
Retrieve knowledge and generate enterprise-specific analysis results: When you use Smart Q capabilities in the frontend, such as Smart Q Chat and Smart Q Report, the system can invoke relevant knowledge to generate analysis results tailored to the enterprise

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From Volatility Attribution Analysis to the report closed loop, from precise data query to real-time interpretation, and to the deep integration of enterprise knowledge, Quick BI Smart Q V6.1 achieves a key leap for the super data analyst—from being merely usable to being truly easy to use—through the systematic evolution of five major capabilities.

This is not only a feature upgrade, but also an innovation in the data analytics paradigm: when AI truly understands business context, and when complex analysis becomes as natural as conversation, the barrier to data-driven decision-making is fundamentally lowered. Technology recedes behind the experience, and value becomes readily accessible.

Community

Quick BI Smart Q V6.1: Making the AI Data Analyst Easier to Use

I.New Volatility Attribution Analysis Capabilities

1.1 Multiple analysis methods with flexible orchestration

1.2 Seamless analysis after data query, with insights directly linked to reports

II. The End-to-end Experience of Smart Q Reports is Upgraded Again

2.1 Dataset selection and automatic report generation

2.2 Asynchronous generation of reports in the background and instant reach across multiple platforms

2.3 Insight questions are traceable, and result comparison is supported

2.4 Data archiving for worry-free traceability

2.5 Custom roles control report permissions, and collaborative editing is supported

2.6 Automatic saving, chart splicing, and table paging greatly improve editing practicality

III. Smart Q Chat Conversational Interaction Evolves Again

3.1 Intelligent table selection makes asking questions faster

3.2 Follow-up questions for clarification make queries more precise

3.3 Dimension value matching optimization for more accurate data query

IV. Smart Q Insights Flexible Configuration Upgrade

4.1 Support adding Smart Q Insight widgets in dashboards

4.2 Artificial Intelligence Recommendation of analysis ideas

V. Enterprise Knowledge Base Vertical Business Understanding Engine

5.1 Enterprise knowledge base vertical business understanding engine

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