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Enhancing Insights with Customer 360 & Big Data Solution for an APAC-Based Retail Bank

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Overview.

An APAC-based retail bank aimed to improve business performance insights, data quality, and governance. Coforge implemented a Customer 360 and Big Data solution using an Azure-based data lake, cloud data warehouse, and interactive dashboards. This solution enabled the bank to identify market opportunities, enhance business performance clarity, and ensure robust data governance.

Challenges.

The client faced significant challenges in their data management and analytics processes:

  • Inadequate Data Platform: No sufficient data and analytics platform to meet growing information requirements.
  • Lack of Business Performance Insights: Difficulty in understanding business performance, trends, and opportunities.
  • Poor Data Quality and Governance: Inconsistent and unreliable data quality and lack of proper data governance.

Solution.

Coforge implemented a comprehensive Customer 360 and Big Data solution:

  • DLXpress Powered Data Lake: Developed an end-to-end Azure-based data lake.
  • Data Model and Dictionary: Created a detailed data model and dictionary for retail banking.
  • Cloud Data Warehouse: Set up for direct and multidimensional analysis.
  • Metadata-Driven Pipeline: Automated data quality and remediation processes.
  • Interactive Dashboards: Used Power BI to create interactive dashboards for data visualization.
  • Data Science Use Cases: Developed data science use cases for advanced analytics.
  • Agile Delivery Methodology: Ensured iterative and efficient delivery of solutions.

Key Highlights:

Coforge's solution delivered significant value to the client's operations:

  • Market Opportunity Identification: Helped the bank identify potential market opportunities.
  • Self-Service BI: Enabled business users to develop their own insights through self-service BI.
  • Performance Clarity: Provided a clear picture of branch, functions, and department performance.
  • Customer Risk Profiling: Created risk profiles of customers using a credit risk analysis model.
  • Robust Data Governance: Ensured robust data governance and high data quality.
  • Near Real-Time Information: Enabled users to access near real-time information.

 

The impact.

Self-Service BI Empowered business users
Customer Risk Profiling Improved with credit risk analysis
Data Governance Robust and reliable

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