Currently, manual methods are used to import source data files, and lower versions of SAS tools are used to store and process data, with high personnel input, and long data implementation and update cycles.
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There are data barriers between various business systems, and data inconsistencies, making it difficult to effectively integrate and share data, increasing the complexity and difficulty of data processing.
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With the growth of data volume, the requirements for data processing efficiency and accuracy have increased, but the existing tools and methods cannot meet the computational needs of business scenarios and data volume.
Build a data mart: Establish a full-process risk data mart to achieve unified collection and management of risk data.
Design a graphical process automation monitoring interface: Use graphical menus to implement ETL configuration and management, achieving automated and timely data processing.
Improve the response time of data set applications: Design ETL process scheduling based on operational efficiency and scalability.
Establish a unified indicator permission management: Configure differential permissions to prevent information leakage and other risks.
Form a supporting data asset catalog: Support applications such as data queries, calculation logic, and historical change tracking.
Create a unified data platform connecting 10+ systems, streamlining data processes in the risk department.
Automate processes to reduce manual input and accelerate data updates.
Protect against leaks and errors with centralized permission management and fault warnings.
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