The client, one of the world’s leading global clinical research organizations (CRO), provides clinical development capabilities and integrated consulting expertise to streamline trials and ensure regulatory compliance for their customer sponsors in the life sciences and pharmaceutical industry.
The Challenge
The client was challenged by the lack of quick scalability which was impacting business growth. They wanted to provide unified data and reporting view for the team and accelerate research outcomes and analysis by
- Establishing a common data standardization and removing the data silos across multiple research departments in the company
- Generating reports for dissemination to different stakeholders (Company executives, Physicians, Patients and collaborators)
- Creating an in-house data lake/database with strong data management, security and access permissions
- Enahancing research capability for future assay designs
The Solution
Persistent developed a genomic big data multi-tenant platform, supporting Peta byte scale storage processing. The team worked very closely with multiple research teams and created a data lake for the standardization of ingestion of data across multiple public and private data sources.
Persistent’s end-to-end offerings helped the client define the data strategy roadmap, implement a modern cloud data stack, put together the governance and security frameworks, and manage the data quality using modern data mesh and operations principles. The team completely automated the workflow from sample to report for diagnostic labs for current as well as future assays.
Persistent supported the client’s data management requirements by creating a data lake architecture for Clinical Trial Management System data to promote automated transformation lookups. We also helped the client modernize their core clinical management systems to stay ahead of the competition, increase scalability, and improve system security.
Benefits
- Reduction in variant interpretation from weeks to days
- 70 % faster processing time with parallel processing
- Reduced cost per sample to less than $1
Technology Used
- AWS Cloud
- Bigdata
- Python
- Microservices Architecture
