The financial data asset management platform is an important big data platform product of Yusys Technologies, which handles financial data asset management. Through a systematic development process, it provides financial institutions with comprehensive data asset management and data services, and by using a one-stop data asset management interface and commercial data services, helping financial institution customers to focus on data asset management and data services. By using the data asset management platform, customers can manage the entire life cycle of the data assets of financial institutions, including different types of data and data from different technical platforms, as well as visually manage assets.

The metadata management platform provides a unified metadata storage model based on the CWM standard, and uniformly stores the ever-increasing enterprise data according to the metadata model, laying a solid foundation for users to understand and manage massive amounts of data. This product can store and manage metadata from source systems, BI data models, ETL processes, data marts, and reporting tools, and process all aspects of the data from data source to data target.
The metadata management platform provides a unified metadata model, dynamically collects and manages metadata, and performs version control on metadata, thereby truly realizing management of changeable and dynamic data. The metadata management platform conducts centralized data management, identification, analysis, and mining, to help enterprises achieve the ultimate goal of data utilization. The metadata management platform also provides powerful data analysis functions through influence analysis, ancestry analysis, and table relevance analysis functions combined with data maps to help enterprise users understand the data processing, understand the data, and provide multiple perspectives to analyze the data flow in the life cycle, thereby providing powerful tools for users to better understand data, analyze data, and mine data.

Data standards are normative constraints to guarantee the consistency and accuracy of data for internal and external use and exchange. Standard management is a series of activities that regulate the formulation and implementation of data standards. It is one of the core activities of data asset management. It plays a crucial role for government and enterprises to improve their data quality, clarify the data composition, break data islands, and accelerate data circulation, thereby generating data value.
The data standard determines the distribution of collection items through standard classification and expands the data standard collection items in combination with standard attributes. In the basic data standard, through the current version and the revised version, after the implementation version is released, the revision of the data standard does not affect the use of the current version of the data standard. Data standards and metadata association not only can establish the relationship between the two, but also prepare for data quality management. The life cycle of a data standard records the basic standard life cycle of a series of states such as draft, to be released, released, revised, re-released, and cancelled.

The data quality management platform is a lightweight platform, through integration of data analysis, quality verification, data cleaning, quality monitoring, and other features to ensure the integrity, consistency, accuracy, and uniqueness of data quality. It helps companies to solve data quality related problems encountered in the data integration process and lay a solid foundation for companies to perform efficient and accurate business analysis.
The data quality management platform is used to configure the quality rules and verify data according to the rules. Through the operations of rule library configuration, task configuration, inspection records, etc., it can evaluate data quality, generate problem data, and help users quickly locate and handle problems, and improve data quality. The platform mainly covers the data quality management process, and includes the rule base, inspection report, task management, inspection record, configuration management, and other functions.