China Minsheng Bank

Activate data value and assist Minsheng Bank in building its big data management capabilities

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Customer Background

China Minsheng Bank, established in 1996, is a nationwide joint-stock commercial bank. It was listed on the Shanghai Stock Exchange in 2000 and the Hong Kong Stock Exchange in 2009. As a Fortune Global 500 company, Minsheng Bank has achieved rapid growth despite fierce competition within the industry. This success is closely tied to its top-down commitment to a digital transformation strategy.

The customer's voice
The customer's voice

“Data is one of the most critical assets for banks, serving as the foundation for refined management, differentiated services, business innovation, and enhanced risk analysis capabilities. As the saying goes, "To do a good job, one must first sharpen their tools." To accelerate its digital transformation, China Minsheng Bank, building on its existing data infrastructure, has adopted a business-driven approach and prioritized data application effectiveness to develop a data middle platform that is both practical and user-friendly. With real-time, intelligent analytics at its core, the bank leverages data and models to drive financial product and service innovation, providing rapid, end-to-end business support, empowering scenarios, and directly engaging customers. The successful deployment of DataPipeline’s enterprise-grade real-time data synchronization platform at Minsheng Bank has effectively met our requirements in terms of accurate real-time synchronization of heterogeneous data, system stability, ease of use, and security. It has enabled enterprise-level real-time data collection, integration, and synchronization, laying a solid foundation for the bank’s middle-platform strategy. At the same time, the platform has significantly reduced manual development costs and accelerated the realization of real-time data value. Looking ahead, both parties will explore broader data management scenarios and work together to promote more real-time application deployments.”

— The technology and big data management team of Minsheng Bank
Facing challenges
Facing challenges
Numerous data sources and severe data silos.

The setup of the development environment and the learning of the development language are quite challenging, involving multiple distributed technology components. The process of code development and component optimization is also very difficult.

High production risk

There are numerous configuration files and parameters for associated components, along with a large number of job environment settings and configuration variables. However, the limited availability of validation environments increases the risk of production issues caused by configuration or environment-related errors.

High operational difficulty

Error and exception data are difficult to trace, and business monitoring and task status monitoring for real-time jobs are relatively challenging to implement using the bank's existing traditional monitoring systems.

Solutions
Data Distribution

The real-time data on customer behavior is standardized and completed, and then distributed to various application systems.

Real-time Position Business Support

The real-time account changes and indicator variations of the business system are transmitted to GaussDB as the basis for calculating real-time positions.

Real-time query support for business data

Real-time data is loaded into Redis for use in real-time business queries.

Historical data query support

The master data system data and the data warehouse data are loaded into SequoiaDB for use as historical data for querying.

Solutions
Customer value
Make decisions and drive business innovation based on data!
Experience DEMO
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