How T. Rowe Price Reduced Time To Resolution 83% with Monte Carlo

Note: This article was written by the T. Rowe Price team and republished here with permission.

As T. Rowe Price continues to modernize its data ecosystem, the need for trusted, reliable, and high-quality data has become essential for powering client experience, sales efforts, marketing activation, reporting, and firmwide analytics.

To address growing challenges with data quality, transparency, and manual reconciliation, the organization introduced Monte Carlo, a proactive data observability platform that continuously monitors the health and accuracy of our data.

The platform alerts us to issues before they impact the business, and gives teams the visibility they need to trust and confidently use data for reporting, client engagement, and decision-making.

Today, Monte Carlo is successfully deployed in production, actively monitoring critical data products, and being used directly by business teams to build custom monitors.

This initiative has materially improved data reliability, reduced data issues, improved data reconciliation efforts, and strengthened business confidence in provided data. 

Why was Monte Carlo needed?

Before Monte Carlo, T. Rowe Price faced a set of recurring challenges that hindered the ability to fully trust and leverage data:

• Frequent data quality breaks across pipelines caused downstream reporting disruptions.
• Manual data reconciliation consumed hours of business and engineering time each week.
• Limited visibility into where and why data issues occurred made root cause analysis slow and inconsistent.
• Business teams lack self-service tools to confirm data accuracy or monitor key data elements (i.e., heavy reliance on technical subject matter experts).
• Data quality issues often surfaced by in-take requests indicating a data quality issue (e.g., data quality was reactive, not proactive).

These challenges created inefficiency, risk, and friction across data- dependent teams.

Success Stories and Early Wins

• Accelerated Issue Resolution: Monte Carlo’s anomaly detection and AI-generated root cause explanations reduced the number of associates required to resolve a data quality issue from 5–6 people to just 2 and shortened resolution time from 6 hours to 1 hour, significantly lowering effort and resource demand.

• Faster Alert Creation: Creating a data quality alert previously required 20+ hours of work. With Monte Carlo, the same task now takes 1.5 hours, enabling rapid response to emerging needs.

• Streamlined Alert Maintenance: Updating existing alerts once required 2–3 resources over 2–3 sprints (4–6 weeks). Now, updates can be completed by one resource in approximately 1 hour, dramatically improving agility.

• AI-Recommended Monitors Increase Coverage: Monte Carlo’s AI-generated monitor recommendations allow teams to quickly add new data quality checks without impacting pipelines. Ultimately, it expands visibility into potential issues and strengthens data reliability.

• Business-Driven Data Quality Dashboards: Dashboards and data quality scoring that previously required a full sprint and a dedicated data engineering resource can now be built directly by business users, increasing self-service and reducing engineering workloads.

• Eliminate Manual Intervention: A previously manual process to identify business parties with similar addresses, previously managed through Excel macros, and prone to false positives has been fully automated through Monte Carlo. This eliminates manual review, improves accuracy, and reduces risk for downstream processes. 

Strategic Fit with Enterprise Data Platform (EDP)

• Establish proactive data quality monitoring across critical data assets with consistency.
• Improve reliability and trust in high-impact data products.
• Enable self-service observability for business teams, reducing dependency on technical teams.
• Automate detection of anomalies and pipeline issues to prevent downstream disruptions.
• Enable business teams to quickly and seamlessly generate data quality dashboards through self-service capabilities, eliminating reliance on development cycles.
• Support a scalable, enterprise-grade approach to data governance and consistency. 

Implementation and Rollout

Phase 1: Evaluation and Platform Selection

The team conducted a thorough evaluation of data observability solutions. Monte Carlo was selected due to its seamless Snowflake integration, automated anomaly detection, lineage capabilities, and usability for both technical and non-technical users.

Phase 2: Pilot and proof of evaluation

A pilot and proof of evaluation was executed to validate Monte Carlo’s capabilities, assess business readiness, and confirm the platform’s potential value.

After completing onboarding, security reviews, and integration with the Enterprise Data Platform (EDP), we demonstrated key capabilities including volume and schema monitoring, anomaly detection, end-to-end data lineage, custom SQL monitors, proactive alerting, and self-service functionality for business teams.

The pilot generated strong positive feedback from both business and technical stakeholders. Business teams recognized the value of proactive data quality monitoring and faster root-cause analysis, while the EDP engineering team confirmed Monte Carlo’s ability to scale and materially improve data reliability.

This collective endorsement established a clear case for moving Monte Carlo into production and expanding its use across the enterprise.

Phase 3: Production Deployment

Monte Carlo was successfully deployed into Production to monitor critical data domains. The platform is now fully operational with the appropriate access and governance controls in place, enabling authorized users to create production-grade monitors and oversee key data assets.

This deployment establishes a resilient, scalable foundation for enterprise-wide data quality monitoring and proactive issue detection. Additional data domains across TRP enterprise are planned in the backlog.

Phase 4: Business Enablement

Monte Carlo held an in-person workshop to train business teams to leverage their user-friendly user interface and to freely build monitors without any technical subject matter expert.

Training sessions empowered business users to build their own monitors directly in Monte Carlo, expanding observability without adding engineering overhead.

Data owners gained the ability to track key data elements, set thresholds, and validate assumptions by improving day-to- day operations and driving stronger stewardship. The successful rollout of Monte Carlo introduced several new enterprise capabilities.

These capabilities significantly strengthen TRP’s data management foundation.

• Proactive monitoring for data accuracy, freshness, schema changes, and pipeline failures.
• Automated alerts that notify teams before issues impact reporting or clients.
• Self-service creation of monitors by business teams without engineering dependency.
• End-to-end data lineage that clarifies where data originates and how it flows into dashboards.
• Improved SLA management for critical data pipelines and data products.
• Consistent data quality processes aligned with the Medallion architecture and governance initiatives.

Business Value and Measurable Outcomes

Operational Efficiency

• Dramatic reduction in manual reconciliation efforts, freeing business teams to focus on insights instead of troubleshooting.
• Faster detection and resolution of data issues—shrinking response times from hours or days to minutes.
• Reduction in recurring data quality issues due to earlier detection and more consistent triage.

Stronger Trust in Data

• Business stakeholders have increased confidence in data as a result of dashboards and data quality scores tagged key data assets used for decision making.

Strategic Advantage

• Supports broader modernization efforts to establish enterprise-wide data reliability and governance.
• Scales easily as new data products and domains are onboarded.

What’s Next for Monte Carlo and T. Rowe?

The implementation of Monte Carlo marks a significant step in T. Rowe Price’s data modernization journey. It is a culmination of Data & Tech strategy execution to drive business value.

By proactively monitoring data quality, enabling business self-service, and reducing operational friction, Monte Carlo has strengthened data trust, improved efficiency, and empowered the organization to make faster, more confident decisions.

This initiative demonstrates how technology, governance, and cross-team collaboration can drive meaningful business outcomes and create a foundation for a more agile and data-driven future.

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