Case study

Master Data Management

Master Data Management - SG Analytics


One of the leading North American Custodian Banks that has close to $38 trillion assets under Custody and $2 trillion assets under management.


The client wanted a single source of truth for its data-driven digital transformation, providing trusted, accurate, complete data to improve its internal operations which would also help to create a better data governance framework. They wanted an authoritative view of your business-critical data from disparate, duplicate, and conflicting information sources and also identify the relationships between products and customers and more.

The Client also required the SGA team to maintain certain set of attribute values, rule inventory and Data Quality for the in-scope datasets so that it can be consumed by various BAU operations teams of the Bank.

Business benefits

  • The bank’s billing teams leveraged the Universal Data Master to plug the Revenue leakage to the extent of USD 150 Mn.
  • Data quality score was improved by 12% through effective incident and exceptions management.
  • The executive leadership got a Bird’s eye view of the data governance program through interactive and insightful dashboards.


SG Analytics team deployed a team of data experts to cleanse the Entity Data and established relationships between the entities and corresponding accounts. The team conducted secondary research on various attributes for entities and accounts and created a database which resulted in cleansed/accurate data within the MDM tool. SG Analytics team addressed data quality issues based on a pre-defined set of business/technical validation rules. Further, the team liaised with various data owners, operations teams and consumers to identify the gaps across various attributes bridged accuracy and data integrity issues.

As a next step, SGA team helped the client in the creation of a data operating model to enable better data management and governance framework. Simultaneously, the team is also designed and developed metrics and dashboards/visualization in order to generate actionable insights for the team. The team used a variety of tools and technologies including SQL, GitLab, Data modeling tools, Integration tools, JIRA, HiPAM, Splunk and other bespoke Applications to deliver value on this program.

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