From data silos to the next-generation intelligent engine: How Lingyang's data middle platform system Dataphin reshapes the growth drivers of enterprises
I. Introduction: Data Middle Platform — The “Core Engine” for Enterprise Digital Growth
Enterprise digital transformation has entered the “value breakthrough period”. The role of data as a core production factor has become prominent, but the implementation results are insufficient. The Industrial World 2025 report shows that more than 85% of large and medium - sized enterprises globally have incorporated the data middle platform into their core strategies, but only 35% have achieved effective transformation from data to business growth. Data from the China Academy of Information and Communications Technology shows that 82% of surveyed enterprises are restricted by the “inability of data to quickly support decision - making”, and the demands of industries such as manufacturing, retail, and finance are particularly urgent. This situation has given rise to strong demand. By 2025, the global data middle platform market scale will exceed $80 billion, with China accounting for nearly 40%.
The core value of the data middle platform has become an industry consensus. Combining the results of DAMA in 2025, it is defined as “a full - life - cycle carrier that connects data collection and business applications with value realization as the core, and realizes the closed - loop transformation from data to productivity through integration, governance, output, and measurement”. Gartner emphasizes that an excellent data middle platform needs to have four major functions of “breaking barriers, ensuring quality, lowering thresholds, and pursuing results”, rather than being a simple storage and computing tool. This clarifies its hub role - it is not only a technical platform but also the core engine driving business growth.
II. Direct Hit on Industry Pain Points: The “Isolated Island Dilemma” of Traditional Data Management and the Key to Breaking the Deadlock
In the practice of digital transformation, most enterprises face common data management problems: systems such as ERP, CRM, and SCM in each business line operate independently. Different data formats and standards form “information chimneys”. Retrieving cross - departmental data is cumbersome, and it is difficult to form a global perspective. The lack of unified data standards leads to frequent data errors, omissions, redundancies, and caliber conflicts. Insufficient data quality directly affects the credibility of decision - making. The traditional data processing process has a long cycle and low efficiency. A large amount of data stays at the “storage level” and can hardly support core business scenarios in a timely manner.
To solve these dilemmas, a new - generation data middle platform is needed to build a full - link closed - loop of “data integration - governance - service - application”. As a productized output of Alibaba's data middle platform practice, Lingyang Dataphin deeply integrates Data x AI technology. It incorporates Alibaba's more than a decade of internal data construction and governance practices (covering core business scenarios such as e - commerce, finance, and logistics) and service experience in various industries into product design. It can break through isolated islands through multi - source data integration, improve data quality with AI - driven governance tools, accelerate value transformation with low - code development and intelligent analysis, and be compatible with the Alibaba ecosystem and third - party tools at the same time, forming a differentiated advantage of “full - link data closed - loop + ecological collaboration”, precisely matching the enterprise's needs to break the deadlock and laying a foundation for the subsequent reshaping of full - link capabilities.
III. Dissection of Lingyang Dataphin's Core Capabilities: Full - Link Reshaping from “Data Integration” to “Intelligent Driving”
3.1 Foundation Layer: The “Super Connector” for Multi - Source Heterogeneous Data
3.1.1 Full - Scenario Data Integration: Covering Structured and Unstructured Data, Adapting to Multiple Deployment Environments such as Cloud and On - Premises
Lingyang Dataphin supports more than 50 types of data sources, comprehensively covering structured and unstructured data such as relational databases, NoSQL, log files, and API interfaces. It can adapt to various environments such as public cloud, private cloud, hybrid cloud, and on - premises deployment. Whether it is the business system data within the enterprise or the third - party data from external partners, it can be centrally aggregated through a unified entrance, completely breaking the physical barriers and format limitations of data storage.
3.1.2 Efficient Data Synchronization: Supporting Real - Time Incremental Synchronization and Batch Synchronization to Ensure Data Timeliness
In terms of data synchronization capabilities, Dataphin provides various modes such as offline/real - time full - database migration and incremental synchronization. Combined with the speed - limit and fault - tolerance mechanism, it ensures the stability and security of data transmission. The real - time synchronization mode can achieve a second - level response to data delay, meeting the needs of scenarios such as real - time marketing recommendation and real - time risk monitoring. The batch synchronization mode is optimized for the scenario of migrating a large amount of historical data, reducing resource occupation and improving data migration efficiency.
3.1.3 Compatibility and Adaptability Advantage: Seamless Docking with the Alibaba Ecosystem and Third - Party Tools to Reduce Migration Costs
Dataphin is deeply adapted to the lake - warehouse integrated architecture, compatible with more than 10 mainstream computing engines such as MaxCompute, Flink, Hive, and Starrocks, and supports mainstream lake table formats such as Iceberg, Hudi, and Paimon. At the same time, through OpenAPI and open metadata, it realizes seamless docking with products such as Quick BI and Quick Audience in the Alibaba ecosystem and flexible integration with third - party business systems and BI tools. This high - level compatibility allows enterprises to quickly complete the deployment of the data middle platform without reconstructing the existing IT architecture, reducing system migration and integration costs.
3.2 Governance Layer: The “Quality Guardian” of Intelligent Data Governance
3.2.1 Automated Metadata Management: Realizing Traceability of Data Lineage and Inventory of Data Assets
Based on AI - driven metadata management capabilities, Dataphin can automatically identify the attributes, associations, and transfer links of data assets and build a complete data lineage map. Enterprises can use the metadata management function to realize the automated inventory and visual presentation of data assets, clearly trace the full - process transfer of data from the source to the application, quickly locate the root cause of data problems, and at the same time provide support for data permission control and compliance auditing.
3.2.2 Intelligent Data Cleaning and Verification: Based on Rule Engines and AI Algorithms to Improve Data Quality
In terms of data quality control, Dataphin integrates rule engines and AI algorithms, which can automatically identify data quality problems such as missing data, outliers, and duplicate values and provide intelligent cleaning suggestions and automated processing solutions. At the same time, the system supports customizing data quality rules and verification indicators. Combined with the function of automatically classifying and grading sensitive data, it realizes dual protection of data quality and data security, improving data credibility from the source.
3.2.3 Fine - Grained Permission Control: Ensuring Data Security and Compliance, Adapting to the Usage Needs of Multiple Roles
Dataphin has built a full - link data security governance system, supporting field - level access control, multi - dimensional permission division, and operation log tracing, adapting to the usage needs of multiple roles such as data administrators, business analysts, and decision - makers within the enterprise. The system has built - in multiple elliptic curve encryption algorithms and implements encryption protection in all links such as data collection, transmission, storage, and use. At the same time, it meets compliance requirements such as ISO information security management and domestic substitution of information technology, ensuring that data applications are safe and controllable.
3.3 Development Layer: The “Data Productivity Tool” with Low - Code
3.3.1 Visual Data Development Platform: Lowering the SQL Threshold and Improving Development Efficiency
Dataphin provides a visual drag - and - drop development interface, supporting multiple code languages such as SQL and Python. Combined with the COPILOT intelligent assistance function, it provides data developers with capabilities such as automatic code completion, syntax checking, and optimization suggestions. Non - technical personnel can also complete simple data model construction and analysis tasks through template - based configuration, greatly lowering the threshold of data development and achieving a dual improvement in development efficiency and code quality.
3.3.2 Template - Based Data Models: Providing Industry - Wide General Models to Accelerate Business Implementation
Relying on Alibaba's more than a decade of cross - industry data practice experience, Dataphin has precipitated general data models and indicator systems for multiple industries such as retail, manufacturing, and finance. Enterprises can quickly build data models that meet their own business needs based on industry templates, avoiding repeated development from scratch. At the same time, the system supports flexible iteration and customized expansion of models, adapting to the dynamic changes of enterprise business processes.
3.3.3 Automated Task Scheduling: Intelligently Planning Execution Links to Ensure Stable Process Operation
Dataphin has unified scheduling and intelligent operation and maintenance capabilities, supporting flexible scheduling strategy configuration. It can intelligently plan task execution links based on data dependencies. Combined with intelligent monitoring and early - warning and dynamic resource allocation functions, it can promptly discover and handle task execution exceptions. The system provides full - life - cycle operation and maintenance support for data tables/tasks, ensuring the stability and efficiency of the data production process and reducing manual operation and maintenance costs.
3.4 Service Layer: The “Intelligent Data Output Port” Oriented to Business
3.4.1 Diverse Data Service Forms: API, Reports, Data Sets, etc., Adapting to Different Business Scenarios
Dataphin supports various data output forms such as API services, theme data sets, and visual reports, adapting to the data usage needs of different business scenarios. Business systems can quickly call standardized data through API interfaces, realizing in - depth integration of business processes and data services. Business personnel can obtain customized data sets through the self - service data retrieval function or directly view preset reports to meet daily analysis needs.
3.4.2 Intelligent Data Analysis Capability: Integrating AI Insights to Assist Business Decision - Making
Dataphin deeply collaborates with Quick BI in the Alibaba ecosystem and integrates its “Intelligent Xiao Q” function, realizing natural - language - driven asset retrieval, intelligent attribution analysis, and automatic report generation, transforming data analysis from “passive query” to “active insight”.
3.4.3 Business - Oriented Data Portal: Customizing Data Views on Demand to Realize “Everyone is a Data Analyst”
Dataphin has built a data portal centered on business scenarios, supporting customized data views by department, role, and business theme, reorganizing scattered data assets according to business logic. Business personnel can quickly find the required data and analysis tools through the data portal without paying attention to the underlying data storage and technical implementation, truly realizing “using data on demand and using data efficiently” and promoting the full penetration of data applications within the enterprise.
IV. Empirical Evidence: The Value of Dataphin Implementation in Different Industries
4.1 Retail Industry: Connecting Omnichannel Data to Drive Precise Marketing and Inventory Optimization
4.1.1 Case Background: The Problem of Omnichannel Data Fragmentation in Chain Retail Enterprises
Representative enterprises in the retail industry (such as Starbucks) have built a standardized data asset system with the help of Lingyang Dataphin, realizing the unified integration and governance of multi - channel data. Combined with Quick Audience, they have completed the upgrade of user global operation, significantly improving data usage efficiency and business response speed.
4.1.2 Implementation Plan: Dataphin's Omnichannel Data Integration and Intelligent Operation System Construction
Through the data middle platform built by Dataphin, the enterprise has realized the centralized integration of omnichannel user data, transaction data, and inventory data, and built a unified user label system and global data view. Based on standardized data assets and combined with the Quick Audience intelligent operation tool, it has realized user hierarchical operation and precise marketing push. At the same time, by connecting sales data and inventory data, it has built an intelligent inventory allocation model to realize the dynamic allocation of inventory across channels.
4.1.3 Value and Results: Dual Improvement in Marketing Efficiency and Inventory Turnover
At the user experience level, connecting cross - channel data has upgraded service consistency and effectively solved the problem of experience discontinuity caused by poor user information communication. At the marketing level, precise hierarchical operation has reduced resource waste and significantly improved the accuracy and conversion efficiency of marketing campaigns. At the supply chain level, the dynamic inventory allocation mechanism has significantly reduced the risks of overstocking and stock - outs, making inventory turnover more efficient and realizing dual lean management of marketing and the supply chain.
4.2 Manufacturing Industry: Data Collaboration in R & D, Production, and Sales to Enable Cost Reduction and Efficiency Improvement
4.2.1 Case Background: The Dilemma of Full - Link Data Collaboration in Multinational Manufacturing Enterprises
Minth Group, a top 100 multinational automotive parts enterprise, has business covering multiple links such as R & D, production, supply chain, and sales. The data of each business line is scattered in different systems, resulting in problems such as the disconnection between production plans and market demand, low efficiency of group - level management, and cumbersome monthly settlement processes. The monthly settlement time of a single factory is as long as 72 hours, seriously affecting business decision - making efficiency.
4.2.2 Implementation Plan: Dataphin's Group - Level Data Middle Platform and Full - Link Data Collaboration System
Relying on Dataphin, the enterprise has built a unified master data management platform for the group, connecting full - link data in R & D, production, sales, finance, etc., and establishing a standardized data indicator system and analysis model. Through the data middle platform, it has realized centralized management and real - time monitoring of data from global factories, built a group - level data command center, and supported the optimization of production plans, supply chain collaboration, and automation of financial settlement.
4.2.3 Value and Results: Significant Improvement in Operation Efficiency and Management Level
After the project implementation, the monthly settlement time of a single factory of Minth Group has been shortened from 72 hours to less than 18 hours, with a four - fold increase in monthly settlement efficiency, realizing real - time visual monitoring and efficient collaboration of global business data.
4.3 Financial Industry: Releasing Data Value under the Premise of Compliance to Assist Risk Management and Customer Operation
4.3.1 Case Background: The Dual Demands of Small and Micro Banks for Data Compliance and Business Growth
Taizhou Bank, a benchmark enterprise in the small and micro - finance field, faces problems such as serious data islands, inconsistent indicator calibers, high compliance requirements, and difficulties in customer hierarchical operation. On the one hand, it needs to meet core regulatory standards such as EAST, Jinshu, and 1104. On the other hand, it needs to improve the efficiency of small and micro - finance services and risk management capabilities through precise customer insights.
4.3.2 Implementation Plan: Dataphin's Compliance - Oriented Data Governance and Intelligent Financial Service System
Taking Dataphin and Quick BI as the core, a data middle platform solution has been built, establishing a unified data standard and indicator system for the whole bank, covering 10 major business areas, 14 theme domains, and more than 100 business processes, including core regulatory standards such as EAST and Jinshu. Through a unified data asset management system, it has realized full - link compliance control of data from collection to application. At the same time, based on standardized data assets, customer portraits and risk assessment models have been built to support precise credit granting and refined operation for small and micro customers.
4.3.3 Value and Results: Simultaneous Improvement in Compliance Level and Business Efficiency
Within half a year after the project implementation, Taizhou Bank has formulated more than 1,600 basic - level data standards for the whole bank and completed the construction of more than 2,500 indicator systems for the whole bank, covering all important report indicators, meeting regulatory compliance requirements and at the same time improving the accuracy of credit risk identification and small and micro - customer services.
4.4 Dairy Industry: Connecting Data across the Whole Industry Chain to Drive Digital and Intelligent Transformation
4.4.1 Case Background: The Challenge of Whole - Industry - Chain Data Collaboration in Leading Dairy Enterprises
Yili Group, a leading enterprise in the dairy industry, has an industry chain covering multiple links such as upstream pastures, mid - stream production, and downstream sales. It faces problems such as diverse data sources, inconsistent standards, low supply chain collaboration efficiency, and insufficient insight into consumer demand. With the advancement of digital and intelligent transformation, the enterprise urgently needs to build a whole - industry - chain data collaboration system to support full - link intelligent decision - making from production to consumption.
4.4.2 Implementation Plan: Dataphin's Whole - Industry - Chain Data Middle Platform and Intelligent Application System
Through Dataphin, a multi - cloud integrated data base has been built, integrating upstream pasture data, mid - stream production data, downstream sales data, and consumer