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LSSUS03-BP01 Optimize data management for sustainability in life sciences - Life Sciences Lens
Implementation guidanceResources

LSSUS03-BP01 Optimize data management for sustainability in life sciences

Implement data management practices that reduce redundant storage, optimize processing efficiency, and minimize data movement while maintaining regulatory requirements. Establish centralized data catalogs and automated lifecycle policies to optimize your storage tier utilization based on access patterns. Design data architectures that balance accessibility requirements with energy efficiency goals.

Desired outcome: Achieve significant reduction in storage footprint and energy consumption through optimized data lifecycle management, deduplication strategies, and intelligent storage tiering while maintaining adherence to life sciences regulatory requirements.

Common anti-patterns:

  • You store data in the same storage tier regardless of access patterns or lifecycle requirements.

  • You don't implement deduplication strategies for redundant research datasets.

  • You move large datasets frequently between regions or storage systems without considering energy impact.

  • You don't establish data retention policies aligned with regulatory requirements and business needs.

  • You maintain multiple copies of reference datasets across different research projects.

  • You don't use compression techniques for archival and infrequently accessed data.

  • You don't monitor storage utilization and costs to identify optimization opportunities.

Benefits of establishing this best practice:

  • Reduce storage costs through intelligent lifecycle management and tiering.

  • Lower energy consumption of storage systems by using optimal storage classes.

  • Improve data accessibility through centralized cataloging and metadata management.

  • Improve regulatory adherence through automated retention and archival policies.

  • Reduce data transfer costs and energy consumption through strategic data placement.

  • Enable better research collaboration through shared, deduplicated reference datasets.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Life sciences organizations generate and consume vast amounts of data across research, clinical, and manufacturing operations. Effective data management for sustainability requires understanding data access patterns, regulatory retention requirements, and the energy implications of different storage approaches. The key is implementing automated policies that transition data through appropriate storage tiers while maintaining accessibility for research and regulatory needs.

Centralized data catalogs play a crucial role in reducing redundancy and improving data discoverability. By implementing services like AWS Lake Formation for research data or AWS HealthLake for healthcare data, organizations can remove duplicate datasets, improve data governance, and reduce overall storage requirements. This approach is particularly important for reference datasets, genomic databases, and clinical trial data that may be accessed across multiple research projects.

Implementation steps

  1. Establish centralized data catalogs and governance:

    • Centralize the management of data by implementing services such as AWS Lake Formation for research data cataloging and governance.

    • Use data lakes such as AWS HealthLake for healthcare and clinical data management.

    • Create standardized metadata schemas for different data types.

    • Establish data ownership and access control policies.

  2. Implement intelligent storage lifecycle management:

    • Configure lifecycle policies to automatically transition data between storage classes.

    • Use Amazon S3 Standard for active research data requiring frequent access.

    • Implement dynamic tiering for data with changing or unknown access patterns.

    • Archive audit and reference data to durable, infrequent access storage such as Amazon Glacier or S3 Glacier Deep Archive.

  3. Deploy deduplication and compression strategies:

    • Implement data deduplication for reference datasets and genomic databases.

    • Use compression algorithms appropriate for different data types (genomic, imaging, clinical).

    • Use service compression features for automated optimization.

    • Create shared reference datasets to remove redundant storage across projects.

  4. Optimize data placement and movement:

    • Analyze data access patterns.

    • Implement regional data placement strategies to minimize transfer costs.

    • Use transfer services such as AWS DataSync for efficient data transfer and synchronization.

    • Consider AWS Storage Gateway for hybrid storage optimization.

  5. Monitor and continuously optimize storage efficiency:

    • Use AWS Cost Explorer to track storage costs and utilization patterns.

    • Implement Amazon CloudWatch metrics for storage efficiency monitoring.

    • Set up automated alerts for storage anomalies and optimization opportunities.

    • Conduct regular reviews of data lifecycle policies and storage class effectiveness.

Resources

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