Case Study: Assurance achieves data trust at scale with Monte Carlo

How Assurance Achieves Data Trust at Scale for Financial Services with Data Observability

Assurance, a financial services company acquired by Prudential Financial, faced significant challenges in maintaining data trust and meeting regulatory requirements as it scaled rapidly. With data use exploding across the organization, its teams struggled with a lack of visibility into data lineage, dependencies, and quality. This made manual data audits difficult and hindered the reliability of analytics and operations. To address this, Assurance partnered with the vendor Monte Carlo to implement its data observability solution.

Monte Carlo provided Assurance with end-to-end field-level lineage and automatic anomaly detection, integrating seamlessly with their existing data stack including dbt, Airflow, Trino, and Starburst. The solution delivered immediate visibility, enabling proactive data quality monitoring and faster root cause analysis via Slack integrations. This resulted in a measurable reduction in time to detection and resolution for data incidents, with one identified fix potentially saving a six-figure sum. Monte Carlo's platform was also crucial for scaling reliable machine learning operations and was instrumental in shifting the company's culture from data skepticism to data trust.

Assurance

Mitchell Posluns

Analytics Manager