In data orchestration platforms, the most dangerous failure isn't always downtime. It also arises as silent degradation: a partial or incomplete report delivered behind a successful response. Serasa Experian faced this challenge with Polaris, its credit data orchestration platform, which composes reports for a growing ecosystem of financial institutions while replacing the legacy systems they depend on.

In this session, Roberto Alvestegui and Paulo Sunairi will share how the Serasa Experian team built Polaris to compose credit reports in near real-time in a complex chain of normalization, routing, calls to internal partners, composition, validation, and delivery. They will explore the challenges of integrating disparate data sources, third parties with variable latency, throttling, and intermittent instability, as well as show how the team ensures the reliability of the delivered data.

You'll also see how they went beyond generic error metrics to detect "degraded success," such as the 200 OK that hides a partial report. To do this, the team mapped the complete query journey, correlated backend signals with the actual user experience through RUM, and applied Data Jobs Monitoring to keep the underlying EMR pipelines, and the credit data they produce, up-to-date and accurate.

This approach took Polaris beyond operational monitoring and connected technical performance directly to business outcomes such as adoption, churn risk, and revenue growth. The team achieved over 80% customer adoption after the migration, reduced end-to-end latency by 30%, and maintained 99.9% availability.

You'll leave with practical insights on how to model end-to-end journeys for critical transactions, structure correlatable instrumentation that scales without cost explosion, hold internal integrations accountable with measurable signals, and build a "no incident without alert" governance cycle that strengthens operational maturity over time.