This case study showcases how Crest Data enabled the business to modernize observability by migrating from Splunk to Dynatrace, an AI-enabled observability platform. Using our proprietary in-house automation tool, the customer was able to migrate thousands of monitoring assets in nearly half the time originally estimated, while preserving logic and improving visibility. The result was more than a platform change, it was a shift toward faster insights, unified observability, and an AI-driven operating model built to scale with the business.

About the Customer

The customer is a globally recognized financial services enterprise operating at massive scale, enabling cross-border and cross-currency money movement across a broad international footprint. With operations spanning hundreds of countries and territories, the organization supports millions of consumer and business transactions through a combination of digital platforms and physical touchpoints.

Its technology ecosystem underpins always-on, high-volume payment processing, where  real-time visibility is critical. Given the scale and complexity of its operations, the customer relies heavily on monitoring and observability capabilities to ensure seamless service delivery, issue detection, and consistent customer experiences across regions and channels.

Customer Challenge

As the customer scaled, their Splunk observability setup became increasingly fragmented and manual. Monitoring data was siloed across teams, dashboards and alerts required constant hands-on effort, and the absence of AI-driven correlation kept operations reactive rather than proactive.

With 3,000+ dashboards and alerts in use, a traditional manual migration to a modern platform was projected to be time-consuming, costly, and prone to error. The key challenge was to modernize observability by moving to an AI-enabled platform while preserving existing logic, and significantly reducing migration effort and timelines.

Proposed Solution

Crest Data led the modernization of the customer’s observability by transitioning from Splunk to Dynatrace, an AI-enabled observability platform designed for automation, and intelligent insights. The solution was centered on reducing manual effort and establishing a unified view across applications, infrastructure, and operations.

To overcome the scale and complexity of migrating 3,000+ dashboards and alerts, we deployed an AI-driven Migration Engine. This led to the conversion of Splunk queries into Dynatrace Query Language (DQL) and reconstructed dashboards using Dynatrace’s JSON definitions. By automating the majority of repetitive and error-prone tasks, the team significantly reduced migration effort while preserving the intent and logic of existing monitoring assets.

The migration followed a 65/35 approach with automation handling the bulk of dashboard and alert conversions, and our expert-led validation focused on complex logic and fine-tuning. This ensured accuracy and performance without disrupting ongoing operations.

Once onboarded to Dynatrace, the customer gained end-to-end observability by AI-driven anomaly detection, automated dependency mapping, and root-cause analysis. The new platform replaced siloed monitoring with a single, integrated view, enabling faster incident resolution, proactive operations, and a scalable observability foundation aligned with the customer’s future growth.

Outcomes & Success Metrics

Our AI-driven migration engine streamlined the migration process, delivering superior accuracy and efficiency without interrupting core business functions.

Migration Efficiency

1. We reduced overall migration effort from an estimated 120 man-days to 64 man-days, achieving a 47% faster time-to-value.
2. Successfully migrated 3,000+ dashboards and alerts from Splunk to Dynatrace at enterprise scale.
3. Automated conversion significantly lowered the risk of human error while preserving existing monitoring logic.

Operational Impact

1. Eliminated heavy manual dependencies involved in dashboard and alert creation, reducing ongoing operational fatigue.
2. It enabled teams to focus on higher-value activities such as optimization and performance tuning instead of repetitive configuration tasks.
3. Improved consistency and standardization across observability assets through automation.

Unified Observability

1. Consolidated previously siloed monitoring data into a single, unified observability platform.
2. Delivered end-to-end visibility across applications and infrastructure through Dynatrace dashboards and notebooks.
3. Improved signal quality and reduced alert noise through centralized, AI-driven correlation.

AI-Driven Outcomes

1. Enabled proactive issue detection using Dynatrace’s AI-enabled anomaly detection and root-cause analysis.
2. Reduced mean time to identify and resolve issues by shifting from reactive troubleshooting to automated insights.
3. Established a scalable observability foundation capable of supporting future growth and additional workloads.

Why Crest Data for Dynatrace Migration

Crest Data brings an AI-first, automation-driven approach to Dynatrace migrations, enabling enterprises to modernize observability faster and with lower risk. For this large enterprise customer, Crest Data successfully migrated 3,000+ dashboards and alerts, reducing effort from 120 man-days to 64 man-days, a 47% faster migration enabled by its proprietary In-House Migration Engine.

As a trusted Dynatrace partner with experience across 5,500+ projects for 150+ global customers, we combine deep observability expertise with intelligent automation to preserve logic, reduce manual effort, and unlock Dynatrace’s full AI-driven services. This helps customers with accelerated time-to-value, unified observability, and a future-ready operating model.