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Home / Case / Banking Industry

Banking Industry

Customer Background

Many clients have built a data warehouse, built a big data platform, and completed data governance, but the data lacks unified management, there is too much manual intervention.

and the degree of digital automation is not high.


Business Challenge
• Execution procedures are not standardized: The data processing process and links are still highly dependent on the execution project team, lack of autonomy, and there are potential safety hazards in production quality.
• Manual processing accounts for a large proportion: In the process of project execution, offline and manual processing methods as still the main methods, occupying a log of human resources, long delivery cycles, and low execution efficiency.
• Insufficient asset precipitation: The resources in the execution process are not shared among the project teams, and some project processes need to be repeated, resulting in a waste of manpower and time costs.
Solution
• Integrated Data Management Platform: Implement an integrated data management platform that brings together data warehouses, big data platforms, and data governance frameworks. This platform should provide a unified view of data, streamline data workflows, and enforce standardized execution procedures.
• Process Automation: Introduce automation capabilities to reduce manual intervention and improve efficiency. Implement workflow automation tools to automate repetitive tasks and streamline data processing procedures. This will minimize human errors, save time, and enhance overall productivity.
• Data Governance Enhancement: Enhance the existing data governance framework to ensure consistent and standardized data management practices. Establish clear data ownership, access controls, and data quality monitoring mechanisms. Implement data governance tools that provide visibility into data lineage, data profiling, and data quality metrics.
Project Achievements

By implementing this comprehensive solution, the banking industry can achieve standardized execution procedures, reduce manual processing, improve data management efficiency, and unlock the full potential of their data assets.


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