Automated Monitoring and Performance Optimization for Life Sciences Platforms
The Challenge
The customer, a global biopharma, was managing a large enterprise data platform that supported their commercial, operational and R&D activities. As adoption grew across teams, new data environments were being added. Each environment had its own workflows, usage patterns and monitoring needs. With the increase in activity, there was a requirement to maintain consistent performance and reliability across all the connected systems.
- Growing Platform Use
More users and new projects were onboarded over time. The number of daily requests also increased, for access, performance checks and incident reviews which needed structured handling. - Performance Oversight
Daily monitoring of running jobs, storage utilization and overall platform health became a critical task. It was important to identify any issues early before they affected ongoing operations. - Proactive Monitoring Need
The customer wanted a system that could provide real time alerts and early warnings whenever there were performance drops or pipeline delays. - Incident Visibility
Requests and incidents raised by users had to be tracked properly till closure, ensuring nothing stayed pending or unnoticed.
Our Solution
Agilisium worked with the customer to build an administrative support framework that could bring consistency to how the data platform was monitored and maintained. The approach combined engineering expertise with automation to ensure faster detection, reporting and resolution of issues.
Platform Administration and Monitoring
The team performed daily health checks across the data environments. Monitoring included job runs, pipeline status and system usage. The goal was to keep the platform stable and ready for new workloads.
Performance Optimization
Routine tuning and maintenance activities were done to improve the speed and reliability of processes. This ensured that critical jobs continued to run smoothly during high workloads.
AI and ML Powered Alerts
To complement manual checks, an AI and ML based alert mechanism was introduced. It extracted key information from logs and notified the support team of potential performance risks before they turned into incidents.
Structured Support and Ticket Handling
All incoming incidents were reviewed, categorized and resolved based on their impact. This made it easier to prioritize tasks and respond quickly to user requests.
Standardized Operations
The monitoring and support process was aligned across multiple environments, creating a common approach that simplified how platform activities were managed.
The Outcomes
Reliable and Scalable Data Platform Support
Through Agilisium’s support framework, the customer maintained high platform availability and stable performance even as new projects were added.
Early Warning and Monitoring
With AI and ML alerting in place, the customer could detect and address performance issues faster.
Consistent Operations
Uniform monitoring methods ensured that each environment followed the same standard of maintenance and response, keeping the overall system aligned and efficient.
Continuous Engagement
The support continues to run as an ongoing engagement, ensuring the platform remains optimized for performance and operational reliability.
