100% increase
In Data transparency via Data Cataloguing and Data lineage analysis
56% decrease
In Data quality incidents via profiling and management of DQ at source
24% increase
In Data Trust scores by measurement of data quality and implementation of DQ rules
Background
Our client, an established provider of asset servicing, asset management and wealth management solutions, with over 25,000 employees globally and $6bn revenues; came to Keyrus for help with modernising a legacy Performance and Risk statistics platform crucial to it's HNW and UHNW customers.
Technology partners
Alteryx
Alteryx automates analytics, data science, and processes in one unified platform to accelerate digital transformation. Organizations rely on Alteryx to rapidly upskill their workforces and deliver strategic, high-impact, business outcomes. First Alteryx Global Premier Partner
2019
North American partner of the year
120+
expert consultants
6+
years of partnership
Challenge
The challenge was to migrate the legacy system to a modern, cloud based architecture solution to lower cost of ownership and to provide clients a better experience, better quality of data and enable customised, self service for users.
Approach
Keyrus conducted a data maturity assessment to identify current state and define target state capabilities. We identified data governance personas and target state use case design across : Data quality, metadata management and master/reference data management. Tool selection process via questionnaires and comparative analysis. Led work streams for Data quality, metadata management and master/reference data management to deliver MVP release. Additionally, the Keyrus team of Data Analysts / Data Engineers led a data lineage analysis for the legacy application to identify data elements from upstream sources.
Key results
Benefits
Keyrus was able to improve data governance and data quality, allowing the client to respond to regulatory reporting commitments in a faster and more accurate way. The process has enabled increases in operational efficiencies through establishing standardises definitions and data quality rules.
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