35%
cost saving (platform ownership)
50%
Increase in scalability
56%
less data quality incidents
Background
Our client is an established provider of investment, compliance, and risk services, with over 20,000 employees globally. They came to Keyrus for help with modernizing a legacy performance and risk statistics platform crucial to its high-net worth and ultra-high net worth customers.
Challenge
The client had a high cost of ownership of the mainframe based legacy application, that comprised of a complex legacy code and data transformation with little to no governance. The platform was not scalable enough to meet increasing customer demands or to maintain a competitive position against modern fintech applications. Additionally, the data was not connected to enterprise golden sources and was manually loaded, leading to data quality and consistency issues. There was no data governance framework, low data transparency, and high volume of data quality issues.
Approach
Working with the client, Keyrus set up a data management process incorporating data discovery, data mapping, and data modeling, developing flexible and scalable data integration pipelines. We also designed and developed a data governance and data stewardship operating model, ensuring this was agreed and applied, with measures in place to track progress. Keyrus was asked to go through a tool selection process for metadata management and subsequently implemented the chosen solution. We are now working on the setup of a data quality framework to identify data quality issues at the source.
Key results
Benefits
The migration from legacy to the modern performance management application provided an immediate cost benefit due to high cost of ownership of the legacy application (£multi millions per annum). It also improved customer experience by giving clients the ability to self serve and do a “what if” analysis on their performance statistics. The new scalable and flexible data management application has enabled agility in management and consumption of data for internal users and clients whilst reducing the cost of fixing data quality issues.
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