Modernizing Data Quality

A large multi-line insurer needed to modernize its data quality management platform to better support its governance and data quality programs. The organization wanted a scalable way to monitor data-related risk, identify anomalies and quality issues, and give information producers and consumers clearer insight into data domain health.

The insurer also needed to expand access to trusted data for business analysts, data scientists, and data engineers while improving visibility into how critical data moved and transformed across the enterprise. To support these objectives, the organization partnered with PremiumIQ to implement a modern data quality and observability platform and strengthen the foundation for enterprise governance.

The Challenge: Modernizing Data Quality at Enterprise Scale

The insurer needed to optimize and streamline its governance and data quality programs while transitioning away from legacy data quality tools. The modernization effort had to improve data oversight without disrupting critical governance requirements or reducing confidence during the migration.

Key objectives included:

Improving management of data-related risks through continuous monitoring of datasets for anomalies and quality issues
Increasing information producers’ and consumers’ understanding of data domain health
Providing trusted data to a broader audience, including business analysts, data scientists, and data engineers
Implementing end-to-end lineage to improve visibility into data movement and transformations
Maintaining governance integrity throughout the transition from legacy capabilities

To achieve these objectives, the organization needed a modern, cloud-based approach that could strengthen governance, improve data quality oversight, and support a controlled migration to a more scalable platform.

The Solution: Implementing a Modern Data Quality Platform

PremiumIQ led the transition from legacy data quality tools to Bigeye’s Data Quality and Observability platform. The engagement combined advisory services, program execution, change management, and communications support to define and implement the client’s modern data quality management solution.

Key components of the engagement included:

Developing a structured transition plan from legacy tools to Bigeye
Conducting extensive parallel testing to validate critical governance and cutover requirements
Implementing data quality capabilities on cloud-based platforms
Executing data quality rules across critical data elements to capture accuracy, completeness, validity, uniqueness, and timeliness results
Integrating data quality rule execution results into the enterprise data catalog
Implementing proactive data governance controls and metrics to detect and prevent governance non-adherence
Enhancing critical governance and compliance reporting through data quality KRIs and KPIs

This approach helped the organization modernize data quality processing while strengthening governance controls, improving visibility, and reducing operational complexity. By combining implementation support with change management, PremiumIQ helped the insurer move toward a more scalable model without losing focus on governance and compliance.

The Results: Stronger Governance and Greater Data Confidence

The modernization initiative established a more scalable foundation for managing data quality and enterprise data risk.

The engagement enabled the organization to:

Continuously monitor data quality and data-related risks
Improve data democratization by expanding access to trusted data
Support better data insights and more informed decision-making
Mature data quality processing by reducing complexity and improving rule execution performance
Operate within a secure cloud environment aligned to modern data quality management

With these capabilities in place, the insurer is better positioned to monitor data risk proactively, increase confidence in critical data, and support future growth in analytics and governance maturity.

Why It Matters

For enterprise insurers, data quality is directly tied to governance, risk management, analytics, and operational confidence. Legacy tools can make it harder to monitor enterprise data health, detect anomalies, and provide teams with trusted information when decisions need to be made.

By modernizing its data quality platform, this insurer strengthened its ability to continuously monitor data risk, improve data accessibility, and support consistent governance across the enterprise. The result is a more scalable foundation for trusted data, better decision-making, and future innovation.

“Data quality is no longer just a technical control. It is a strategic capability that helps insurers manage risk, strengthen governance, and build confidence in the data used across the business.”

— PremiumIQ Engagement Lead