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Community Blog Quick BI V6.1 Enhances Data Modeling and Analysis, Making Analytics More Accessible, Efficient, and Intelligent

Quick BI V6.1 Enhances Data Modeling and Analysis, Making Analytics More Accessible, Efficient, and Intelligent

This article introduces the Quick BI V6.1 upgrade, highlighting its new Intelligent Relational Model and AI capabilities that enable zero-threshold data modeling and intelligent analysis.

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In the era of artificial intelligence (AI), data quality and analytical efficiency are becoming key differentiators in enterprise competition. However, in frontline enterprise practice, the dilemma that “data does not understand the business, and the business does not understand data” still widely exists. Business users still struggle with complex data modeling and SQL writing. Technical teams are still manually moving tables across office tools. Decision-makers repeatedly review reports on their phones, yet still find it difficult to capture the key information.

To address this, Lingyang Quick BI officially releases version 6.1. Centered on strengthening foundational capabilities and improving the customer experience, this release introduces the Intelligent Relational Model, enriches data sources, upgrades the Quick Engine, and enhances visual analytics. It brings intelligent experiences into every stage of analytics, moving data analytics into a new phase of zero-threshold, high-efficiency, and truly intelligent use, fully unlocking the value of data and turning it into a growth engine for enterprises.

I. Newly Introduced Intelligent Relational Model Capability

With the dual capabilities of Intelligent relational model and Data intelligent configuration toolbar, Quick BI fully empowers business personnel to perform zero-threshold data modeling and data processing.

Attribute 1: Newly Introduced Intelligent Relational Model for Worry-Free Data Analysis

Common scenarios:

  1. Zero-threshold data modeling for business users: In the past, data modeling required a professional technical background and a solid understanding of data uniqueness and consistency. Otherwise, improper operations could easily lead to duplicate statistics. Now, the Relational Model automatically completes data processing and adaptation, effectively avoiding data inflation while providing a zero-threshold and user-friendly experience.
  2. Strong dataset reusability to support multiple analysis scenarios across the company: Say goodbye to the need to build a separate wide table for every department and every analysis scenario. The Relational Model includes only the tables related to the current query in computation, while ensuring the correctness of the computation. One dataset can support the needs of multiple analysis scenarios.

Feature overview:

A new logical layer has been added to data modeling. On top of traditional physical modeling, it introduces the Relational Model capability:

  1. Minimal-operation data modeling: You do not need to write complex join statements or understand the underlying logic. You only need to specify the foreign key field to complete data modeling. The Relational Model automatically processes the matching relationships to ensure complete data coverage without omission.
  2. Intelligent prevention of data inflation and errors: When the data granularity of tables such as orders, products, and customers does not match, you no longer need to preprocess the data during modeling. The Relational Model can automatically resolve data inflation issues and ensure the correctness of data computation.
  3. One-time modeling for reuse across multiple analysis scenarios: The Relational Model offers strong reusability. Different departments no longer need to model separately for each individual analysis scenario. This effectively reduces the amount of modeling work and significantly lowers data management and maintenance costs.

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Attribute 2: Intelligent Data Configuration, a Zero-Threshold Data Processing Toolbar

Common scenarios:

Business users generally lack SQL and function knowledge. When they use calculated fields for data cleansing and processing, the process is inefficient and prone to errors, which causes bottlenecks in data cleansing and analysis workflows.

Feature overview:

A shortcut panel toolbar is provided for common data processing, offering business users a zero-threshold operation panel, including:

  1. Time difference calculation
  2. Dimension value alias mapping
  3. Null-value processing
  4. Field splitting and merging

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II. Enriched Data Sources and Upgraded Quick Engine

Quick BI now supports zero-code integration with DingTalk AI Tables and automatic synchronization, while also upgrading the dynamic time placeholder capability of the Quick Engine. This enables high-efficiency, low-cost, real-time analysis across all scenarios, from collaborative online data to massive volumes of historical data.

Attribute 1: DingTalk AI Table Data Source Integration

Common scenarios:

In industries such as the internet sector and retail, teams often use DingTalk or Lark tables for multi-person collaboration, and business data is accumulated in online documents in real time. However, the existing workflow requires users to first export the data from online documents and then manually upload it to Quick BI. This maintenance process consumes dedicated personnel time and is prone to errors due to inconsistent data versions.

Feature overview:

  • Zero-code direct connection for quick integration in minutes: No complex API configuration is required. You can easily connect to DingTalk AI Tables through a guided productized interface.
  • Automatic scheduled synchronization: You can configure periodic synchronization plans and say goodbye to manual handling. Data is automatically updated in Quick BI.
  • Unlock analytical potential: With the powerful and rich visualization capabilities of Quick BI, you can make up for the lack of visualization capabilities in DingTalk AI Tables. At the same time, calculated fields can be created for flexible secondary processing. Multi-dimensional insights can then be derived from raw data, enabling daily collaboration data to support complex business analysis and decision-making.

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Attribute 2: Quick Engine Supports Acceleration Placeholders

Common scenarios:

In scenarios where complex analysis reports customized by finance, operations, and other departments rely on underlying tables containing tens of millions or even hundreds of millions of historical records, the custom SQL of the dataset is prone to timing out during online execution, and extraction acceleration cannot be enabled directly.

Feature overview:

  • Acceleration placeholders for fast execution during the preview phase: Dynamic time window replacement is used. By adding acceleration placeholders to the WHERE conditions in custom SQL, the placeholders uniformly carry the time-filtering logic, and users only need to focus on the core query logic. At the same time, default values can be set for placeholders to ensure fast execution during the preview phase.
  • Flexible configuration for precise control of computation costs: You can complete job configuration by specifying the corresponding placeholder fields in the acceleration settings and setting the time range and delta-update granularity to be extracted. The Quick DPI Engine will automatically parse and replace the placeholders with a specific time range, avoiding the resource waste of full computation and enabling massive data reports to achieve low-cost, high-efficiency real-time updates.

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III. More Flexible Visual Analytics

The full-scenario visual analytics upgrade of Quick BI makes analysis on mobile devices more accessible, interval comparison more customizable, and isomorphic dataset replacement smoother. It also supports the free combination of multiple charts and precise delivery through long images. From frontline store employees to company executives, it creates a more personalized and flexible data experience in fragmented moments and within limited screen space.

Attribute 1: Comprehensive Upgrade of Legend and Tooltip Features on Mobile Devices

Common scenarios:

The demand for data reading and analysis on mobile devices is growing. With legends and lightweight tooltips that better fit mobile reading habits, key data becomes clear at a glance. The system also supports one-click analysis actions while viewing data, improving analysis efficiency and user experience on mobile devices.

Feature overview:

  • Legend rendering that better fits reading habits: Two new modes—normal legend and legend label—have been added to adapt to different chart types and screen sizes. At the same time, legend layout and text style configurations are available to meet diverse visual needs.
  • A more efficient way to view data details: Support for tooltip field configuration has been added. Users can quickly retrieve the full context through lighter touch interactions. At the same time, content and style settings are available to highlight key information within limited screen space and improve viewing efficiency.
  • A more seamless analytical workflow: The interactive action bar and tooltips are deeply integrated. Users can directly trigger linked analysis operations while viewing data details, achieving a seamless flow from viewing data -> understanding -> analysis and reducing context-switching costs.

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Attribute 2: Query Controls Support Custom Date Shortcut Intervals

Common scenarios:

In high-frequency data monitoring scenarios such as e-commerce, finance, and logistics, business users often need to compare data across different periods. The date shortcut intervals provided by the original system sometimes cannot adapt to industry-specific cycles and dynamic rules. As a result, business users are forced to manually select date ranges, which is time-consuming and error-prone.

Feature overview:

  • The system’s built-in date shortcut intervals have been enriched, and personalized custom shortcut intervals are now supported.
  • A base time can be set, and dynamic date intervals can be configured based on that base time.
  • An interval preview feature has been added, allowing users to check in real time whether the configuration is correct.

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Attribute 3: Conditional Formatting Supports Dataset Field References and Bold/Italic Configuration

Common scenarios:

Business analysis often requires quickly identifying the gap between actual performance and KPI targets or budget values. Existing conditional formatting only supports field comparison within charts, preventing users from directly referencing other key metrics in the dataset—such as target values or time boundary ranges—for comparison. This affects analysis efficiency and information communication. In addition, important data in tables lacks sufficiently prominent styling, making key information easy to miss.

Feature overview:

  1. Conditional formatting can reference dataset fields and select an aggregation method.
  2. Conditional formatting style configuration supports bold and italic text.
  3. The dynamic field dropdown now supports fuzzy search.

Attribute 4: Dashboard Dataset Replacement Supports Automatic Generation of Calculated Fields

Common scenarios:

When an enterprise replaces isomorphic datasets, calculated fields may be missing in the target dataset. As a result, all fields must be manually rebuilt before replacement, leading to low efficiency and a higher risk of errors.

Feature overview:

  1. A split replacement mode has been added for calculated fields, which can now be automatically generated after expression parsing.
  2. When the schemas of the old and new datasets are the same, replacement can be completed seamlessly.

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Attribute 5: Precise Subscription, All Key Information in One Long Image

Common scenarios:

Subscribing to an entire dashboard can easily lead to information overload, making it difficult for users to quickly locate core metrics in complex reports. In specific scenarios, different roles—such as decision-makers and frontline business users—usually only need to focus on a few key data points, such as core KPIs and segmented business progress.

Feature overview

  • Free combination of multiple charts: Users can select multiple chart widgets or sheet pages from complex dashboards or workbooks as needed and freely combine them into personalized subscription content.
  • Precise delivery through long images: The system stitches the selected content into one long image and regularly pushes it to channels such as Email and OA, enabling precise and efficient data delivery and helping users quickly focus on core information.

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IV. Intelligent Experience Everywhere

One-click parsing errors, quickly locate problems

Attribute: Intelligent Error Diagnosis

Common scenarios:

Users often encounter native database error messages during data queries. These messages contain a large number of technical terms and are difficult to understand, making it hard to identify the specific cause of the issue. This leads to high communication costs and a poor user experience.

Feature overview:

Intelligent Diagnosis leverages the capabilities of large language models (LLMs) to perform the following analysis:

  • Automatically parse error messages and provide highly readable explanations
  • Locate the specific cause of the error
  • Recommend executable solutions

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During Monday morning meetings, a precisely delivered long image on users’ phones clearly highlights fluctuations in last week’s core metrics.

On Wednesday afternoon, collaborative data in DingTalk is synchronized within seconds, enabling instant deep drill-down analysis.

Before leaving work on Friday, business users can build a brand-new analytical model with zero threshold.

This is not a vision of the future, but the daily reality enabled by Quick BI V6.1.

Business intelligence with zero threshold, high efficiency, and true intelligence is no longer just a tool for improving analyst productivity—it is becoming an engine that reshapes how every business department works. We are turning delayed guesswork and uncertain fluctuations into real-time insights and more certain growth.

Big Data Developers Data analysis Data Analytics Business Intelligence Data Modeling Quick BI Lingyang Intelligent Relational Model
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