Recognizing the need for modernization, the organization sought a solution that could migrate its highly customized monitoring setup without disrupting critical workflows or introducing operational risk.

This case study demonstrates how Crest Data uses its proprietary migration automation tool with expert-led validation to streamline the migration to Dynatrace. The result was a faster, safer transition, preserving the integrity of existing logic, unlocking AI-enabled insights, and establishing a scalable foundation for future observability and operational efficiency.

About the Customer

The customer is a well-established insurance organization operating in a highly regulated market, serving a large share of households and businesses through a comprehensive portfolio of personal, commercial, and pension products. Built on a cooperative model, the organization prioritizes long-term customer trust, sustainability, and shared value, while maintaining a strong local presence complemented by modern digital channels. Alongside its heritage, the insurer continues to invest in digital transformation and modern IT platforms to improve efficiency, resilience, and customer experience, supported by a strong financial foundation and a forward-looking innovation strategy.

Customer Challenge

The organization aimed to modernize its monitoring strategy by moving away from fragmented, manually managed systems to a unified, AI-driven observability platform. However, the existing environment had grown significantly over time, with close to 900 dashboards and nearly 500 alerts embedded with complex logic. Migrating this volume manually would have required months of sustained engineering effort and introduced a high risk of errors while rewriting alert logic from SPL to DQL, potentially creating monitoring gaps and blind spots. With an estimated 58 man-days of manual work, the migration timeline threatened to delay access to Dynatrace’s advanced AI capabilities, making it critical to find a faster, more reliable approach without compromising accuracy or operational continuity.

Proposed Solution

To tackle the scale and complexity of the migration, Crest Data implemented an automation-first, hybrid approach. This strategy leveraged proprietary tools to handle the bulk of repetitive conversion tasks while allowing engineers to focus on high-value, complex logic. The result was a faster, more reliable migration that balanced speed with precision.

• Automation-first Migration Strategy: Implemented a hybrid approach that combined proprietary automation with expert-led execution, allowing large-scale migration to proceed quickly without compromising accuracy.
• Dashboard Conversion at Scale: Used an in-house automation migration tool to automatically convert approximately 70% of legacy dashboards into Dynatrace Dashboards and Notebooks, significantly reducing manual effort.
• Alert Logic Transformation: Automated the conversion of 75% of security and performance alerts, translating complex SPL logic into Dynatrace DQL and deploying them directly into the target environment.
• Targeted Manual Refinement: Addressed the remaining 30% of dashboards and 25% of alerts through focused manual migration and validation to handle custom logic and edge cases.
• Human-in-the-Loop Validation: Ensured 100% accuracy and functional parity by combining automation with expert review, delivering a reliable and production-ready observability environment.

Outcomes & Success Metrics

The automation-first migration delivered a high-velocity, precise, and reliable transition to Dynatrace, achieving measurable impact across dashboards, alerts, and overall effort:

• Dashboard Panels: 867 migrated with 70% automation success, preserving structure and functionality.
  
• Alerts and Triggers: 492 converted with 75% automated, ensuring critical monitoring logic remained intact.

Efficiency Gains:

• Projected manual effort: 58 man-days
• Actual effort with automation: 28 man-days
• Total time saved: ~50% reduction
  

Key Takeaways:

• Efficiency Through Innovation: Automating the bulk of migration cut go-live time in half compared to traditional manual approaches.
  
• Superior Accuracy: Automated SPL-to-DQL conversion maintained consistency and reliability across all critical alerts.
  
• Hybrid Excellence: A ‘Human-in-the-Loop’ strategy combined automation with targeted manual validation to ensure 100% parity.
• Unified Observability: The organization successfully transitioned from a fragmented SIEM to a single AI-driven platform, future-proofing monitoring and security operations.

About Crest Data

Crest Data is a data and AI-first technology firm that helps enterprises accelerate Dynatrace migrations and observability modernization. By combining proprietary automation tools with deep engineering expertise, Crest Data delivers faster, more accurate, and low-risk transitions from legacy monitoring systems to AI-enabled platforms.

Trusted by 150+ global customers and backed by 5,500+ successful projects, Crest Data collaborates with leading partners including Dynatrace, AWS, Datadog, Google, ServiceNow and more to help organizations unify monitoring, streamline operations, and unlock the full value of AI-driven observability, ensuring business continuity and measurable efficiency gains.