Data Management as a Service (DMaaS)

A global asset management firm operating across a complex ecosystem of multiple data vendors and downstream systems faced significant challenges in establishing a consistent and reliable view of its entities and associated data. Variations in source data, duplicate records, and inconsistent identifiers limited the firms ability to create a unified high-quality dataset for operational and analytical use.

To improve the current process, the firm implemented an Enterprise Data Management (EDM) platform to centralize and standardize data processes. However, it became evident that realizing the full value of the platform required a dedicated operational capability to manage data ingestion, validation, and entity consolidation, ensuring accurate and business-ready data across the organization.

• Lack of a Dedicated Data Operations Setup
• Limited Process Standardization and Documentation
• Production and Audit Issues Due to Inadequate Testing
• Unified Entity View Across Vendor Data Sources
• Inefficient Exception Handling Mechanisms
• Unstructured Change Management Approach

• DMaaS operations and enablement: New resources were onboarded and trained to ensure a standardized operating model across the team. This reinforced operational consistency, scalability, and high performance.
• SOP and knowledge governance: Multiple Standard Operating Procedure (SOP) documents were prepared, reviewed, and approved by the client. A structured knowledge repository was also established to support audit readiness and compliance requirements. Best practices were formalized and consistently applied in resolving data-related issues as well.
• UAT Assurance and Pre-Production Validation:Performed end-to-end functional testing across workflows to ensure accuracy, completeness, and expected system behavior, with all identified issues thoroughly resolved prior to deployment to ensure a stable production environment. This was supported by a comprehensive validation framework for assessing enhancements and data changes prior to production release.
• Advanced entity matching and data resolution: We delivered a white-glove service, executing over 7000 entity matches and proactively resolving customer-challenged records through deep analytical reviews and root-cause remediations, significantly improving match accuracy and overall data reliability.
• Exception management excellence: Managed 30,000+ Kensho entity-matching exceptions and 200,000+ vendor data discrepancies as a part of the service level agreement (SLA), transforming exception processing into a streamlined operations via SQL-driven bulk resolution.
• Operations-initiated change control: Operational change requests were initiated, reviewed, and executed to enhance the client environment, and ensure smooth day-to-day operations. Also, rigorous user acceptance testing and close collaboration with Product and Implementation teams ensured seamless deployment.