Data Architecture Planning for a Financial Institution _Typical Cases

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Data Architecture Planning for a Financial Institution

Project Background

The institution launched its EDP system in 2012 to centralize data sharing and enhance decision-making. However, amid big data and cloud finance trends, evolving market competition, customer demands, and regulatory requirements necessitated redesigning the data architecture to maximize data value and establish a trustworthy big data foundation.

Business Challenges

• Data inaccuracy and duplication

• Ambiguous ownership leading to dispute

• Slow response to data requests

• Unclear data hierarchy

• Poor model management

Solution

Based on a thorough understanding of the bank’s overall architecture—including business, IT, and technology architectures—and drawing on best practices from the industry, the next-generation data platform delivers seven core capabilities:

· Integrated Data Integration Capability

· Big Data Processing Capability

· Real-Time and Near Real-Time Data Processing Capability

· Unified Data Exchange Capability

· Historical Data Management Capability

· Comprehensive Data Service Capability

· Data Asset Management Capability

Achievements

• Structured hierarchy with defined layer functions.

• Unified management across data flows: collection, scheduling, control, storage, and ETL standardization.

• Value realization: Enhanced deep data processing and unified API services to strengthen the data middle platform.

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Data Architecture Planning for a Financial Institution

中电金信鲸Bot RPA是一款面向金融行业客户的机器人流程自动化的开发平台。