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.
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