Accelerating Enterprise Observability with AI-Driven Migration to Dynatrace
Executive Summary
As enterprise environments scale, observability often becomes harder to manage especially when built on legacy, manual-first platforms. For this customer, the challenge was real: a sprawling monitoring environment with 3,000+ dashboards and alerts supporting critical business operations. What once worked well had evolved into a complex setup that demanded high manual effort and drove up operational costs.
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 migration engine, 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, 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 observability capabilities to ensure seamless service delivery, issue detection, and consistent customer experiences across regions and channels.
Customer Challenge
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 focused on reducing manual effort and creating a unified view across applications, infrastructure, and operations.
Using our Neuwave Agentic AI Migrations product and AI-driven Migration Engine, the organization accelerated its transition by automating the translation of complex Splunk queries into Dynatrace Query Language (DQL) and reconstructing thousands of dashboards and alerts using Dynatrace’s JSON definitions. This approach automated the majority of repetitive and error-prone tasks while preserving the intent and logic of existing monitoring assets.
The migration followed a 70/30 automation-to-expert model, where automation handled the bulk of dashboard and alert conversions, and expert-led validation focused on complex logic and fine-tuning. As a result, the migration was completed in less than two man-months, achieving over 60% faster execution compared to manual estimates, without disrupting ongoing operations.
Once onboarded to Dynatrace, the customer achieved end-to-end observability with AI-driven anomaly detection, automated dependency mapping, and faster root-cause analysis. The new platform replaced siloed monitoring with a single, integrated view, enabling proactive operations and a scalable observability foundation across global systems.
Outcomes & Success Metrics
Our AI-driven migration engine streamlined the migration process, delivering superior accuracy and efficiency without interrupting core business functions.
Migration Efficiency
- We reduced overall migration effort from an estimated 120 man-days to 64 man-days, achieving a 47% faster time-to-value.
- Successfully migrated 3,000+ dashboards and alerts from Splunk to Dynatrace at enterprise scale.
- Automated conversion significantly lowered the risk of human error while preserving existing monitoring logic.
Operational Impact
- Eliminated heavy manual dependencies involved in dashboard and alert creation, reducing ongoing operational fatigue.
- It enabled teams to focus on higher-value activities such as optimization and performance tuning instead of repetitive configuration tasks.
- Improved consistency and standardization across observability assets through automation.
Unified Observability
- Consolidated previously siloed monitoring data into a single, unified observability platform.
- Delivered end-to-end visibility across applications and infrastructure through Dynatrace dashboards and notebooks.
- Improved signal quality and reduced alert noise through centralized, AI-driven correlation.
AI-Driven Outcomes
- Enabled proactive issue detection using Dynatrace’s AI-enabled anomaly detection and root-cause analysis.
- Reduced mean time to identify and resolve issues by shifting from reactive troubleshooting to automated insights.
- 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 empowers customers with accelerated time-to-value, unified observability, and a future-ready operating model.
