Design principles

Optimize research computing resources: Research computing in life sciences often requires significant computational power for tasks like molecular modeling and genomic analysis. Organizations should implement practices that balance research throughput with resource efficiency. This involves designing workloads that maximize the use of computing resources during active research while scaling down during quiet periods. When implementing research pipelines, consider scheduling mechanisms that batch similar computations together and optimize the use of specialized hardware accelerators. The goal is to maintain or improve scientific output while reducing the overall resource footprint.

Implement intelligent data analytics and data lifecycle management for research: Life sciences organizations face a double challenge of processing vast amounts of real-world data for insights while also maintaining extensive data archives for regulatory adherence. For real-world evidence analysis, organizations must efficiently process large-scale, diverse datasets from multiple sources including electronic health records, claims data, and patient registries. This requires optimizing analytical workflows and storage patterns to handle high-volume data processing while minimizing resource usage. Simultaneously, organizations must maintain data for extended periods to meet GxP requirements, necessitating efficient long-term storage strategies. Design data architectures that balance these needs by intelligently managing both active analytical workloads and long-term storage. For analytical workloads, implement efficient processing patterns that minimize unnecessary data movement and optimize compute resource usage. For compliance-aligned data storage, create tiered architectures that move data through appropriate storage classes based on access patterns while maintaining regulatory accessibility. This requires careful consideration of data classification, retention policies, and access patterns unique to life sciences workflows.

Maximize laboratory resource efficiency: Modern laboratories generate massive amounts of data from various instruments and systems. Design architectures that optimize the integration between laboratory instruments and cloud resources, minimizing unnecessary data transfer and storage. Consider implementing edge computing where appropriate to process and filter data closer to the source. Laboratory automation systems should be designed with sustainability in mind, improving the efficiencyt of compute resources while maintaining the required level of control and monitoring.

Build sustainable clinical trial infrastructure: Clinical trials involve complex data collection and processing pipelines across multiple sites and systems. Design these systems to efficiently collect and process data, avoiding redundant storage and unnecessary data movement. Implement monitoring systems that provide oversight while minimizing resource usage. Consider how to optimize multi-site trial infrastructure through regional processing and efficient data aggregation patterns, especially for decentralized trials that may generate significant amounts of remote monitoring data.

Healthy people, healthy planet: Supporting sustainability in healthcare with the cloud