Case Study
Big data engineering
Data | Banks | London
The Background
- The client is a regulated business that operates across multiple jurisdictions
- Many projects have not followed standard data architecture patterns or standard software engineering principles
- The bank urgently required hands-on Engineering Leadership to establish and drive how to leverage big data technologies effectively, consistently and efficiently in order to accelerate solution development, reduce vendor lock-in and reduce costs
The Challenge
- Many of the existing processes relating to data were very manual and therefore would have become increasingly expensive as the business continued to grow
- The organisation had experienced some significant issues with its technology platforms and whilst it had stabilised its systems, it hadn’t invested significantly in new technologies or tools
- The organisation structure was still evolving. Greater clarity was required around the roles and responsibilities within different parts of the business
The Approach
- Established a design authority to define & govern all the architecture patterns being used for upcoming projects, and migrating disparate practices and architectures to the standards
- Built solutions on open source tools as replacements for vendor solutions
- Acceleration of adoption of containerisation technologies like Kubernetes and Docker to build services that are not locked/tied into one another
- The leadership of an initiative to open source the bank’s projects including engagement with Apache Software Foundation and open source projects
The Impact
- Reduced production issues leading to reduction in production support costs
- Open source cost benefits and clearer focus on business value generation rather than spending time on aligning business goals with vendor product roadmaps
- Accelerated solutions development and preparation for cloud migration
- Brand visibility as a technology focused bank, creating crowdsourced contribution opportunities and attracting talent
- Vendor savings of $5m and efficiency gains of 20-30%
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