Outcreating Data Quality Assurance

Our Client

The client is a major American Fortune 500 holding company, offering solutions across life insurance, annuities, retirement planning, and employee benefits. With a long-standing market presence and a broad customer base, the company is focused on helping individuals and organizations achieve financial security and long-term confidence.

Challenges

The client’s annuity data was spread across multiple source systems, creating complexity in consolidation, validation, and reporting. Manual validation of large datasets placed heavy pressure on data, QA, and compliance teams, slowing release cycles and increasing the risk of data quality issues that could impact downstream reporting and regulatory submissions.

To build a scalable and resilient reporting ecosystem, the organization needed to:

• Consolidate annuity data onto a unified cloud platform
• Strengthen data quality assurance across complex ETL workflows
• Reduce manual effort in validation and regression testing
• Accelerate releases while improving reporting accuracy and compliance confidence

LTM's Solution

LTM designed and implemented a customized AI-led Data Quality Accessor framework on AWS to support the client’s cloud reporting modernization journey. The solution automated validation across key stages of the data pipeline, improving scalability and operational efficiency.

The framework enabled automated validation across multiple processing and storage layers, ensuring data integrity and transformation accuracy throughout the pipeline.

Designed to align with the client’s phased integration of seven source systems, the solution enabled repeatable regression testing as new sources were onboarded. By shifting validation earlier in the delivery cycle and reducing manual effort, LTM helped improve release readiness.  It also accelerated issue detection and strengthened confidence in reporting and analytics on AWS.

Business Benefits

The transformation delivered measurable business impact while modernizing the data quality assurance foundation:

• Achieved up to 60% savings in regression testing efforts through end-to-end automation
• Accelerated time-to-market by 2x with a shift-left automation approach that significantly reduced release cycles
• Boosted overall test coverage and maximized efficiency for every release across the data pipeline by more than 95% accuracy
• Identified and resolved data issues earlier in the testing lifecycle, minimizing downstream risk
• Enabled a scalable, resilient data infrastructure on AWS supporting business growth and regulatory compliance

Conclusion

The successful migration and consolidation of multi-source annuity data onto AWS marked a major modernization milestone for the organization. By implementing a customized AI-led Data Quality Automation framework, the client automated ETL validation and regression testing end-to-end. As a result, quality improved, costs were reduced, and release cycles were accelerated. With a modern cloud data platform in place, the client is now better positioned to scale reporting, reduce risk, and deliver trusted insights with greater agility.