Objective

Supported a top-5 global bank in implementing a single, enterprise data lake to overhaul their existing data supply chains and modernize their infrastructure to support enterprise-wide data and analytics use cases.

Challenges
 

• Legacy architecture insufficiently scalable for increasing data volumes.
  
• Infrastructure blockers to undertaking next gen analytics use cases.
  

Approach
 

• Defined enterprise data reference architecture and capability frameworks and design patterns across the data value chain.
  
• Established a framework to identify enterprise data use cases.
  
• Defined design patterns across the data value chain.
  
• Developed an agile product development methodology.
  
• Developed the logical architecture for end-to-end data management, privacy, and security.
  

Impact
 

• Optimized technology spend on legacy architecture by rationalizing systems and applications.
  
• Implemented scalable infrastructure capable of handling future increases in data volumes.
  

Questions

Looking for high-end research and risk services? Reach out to us at: