Abstract
Life sciences organizations face a growing challenge: scientific instruments are producing data at petabyte scale, but traditional research workflows were never designed to move, govern, or reuse it at that volume or speed. As AI-driven research raises the bar further, the ability to find, understand, and act on scientific data has become as important as storing it.
This blueprint describes how IBM addresses that challenge through an end-to-end data platform — the life sciences data highway — that connects instrument ingest, shared storage, AI infrastructure, and intelligent data management into a single, unified architecture. It covers the design principles, key components, real-world deployment patterns, and practical guidance organizations need to build an AI-ready scientific data platform on IBM technology.
Authors
Tran Nguyen, Andy Tran, Meghan Grable and Matthew Klos
- Introduction
- Section overview
- Authors
- Feedback
- Executive summary
- Introduction
- Reference architecture overview
- Key use cases and customer benefits
- Guidelines and design considerations
- Conclusion
- Abbreviations
- Notices
- Version History
Introduction
Purpose of this document
The focus is on addressing the full scientific data lifecycle, from high-speed instrument ingest through analysis and long-term retention. The architecture is built on IBM Storage Scale and IBM Fusion HCI, extended by IBM's data management capabilities for cataloging, content awareness, and agent access: IBM Fusion Data Catalog (FDC), Content-Aware Storage (CAS), and the Model Context Protocol (MCP) Server. It also integrates with the NVIDIA AI Data Platform (AIDP), along with partner and open-source technologies for protocol access, data movement, and governance.
Who should read this document
This document is intended for the following audiences:
- Storage and infrastructure architects evaluating or designing shared data platforms for life sciences HPC, AI, and research environments.
- IT leaders and decision-makers at research institutions, hospitals, genomic centers, and pharmaceutical organizations seeking to modernize scientific data infrastructure.
- Data engineers and platform teams responsible for implementing data ingest, tiering, lifecycle management, and AI readiness for scientific workloads.
- Research computing and HPC teams looking to connect instrument ingest, compute clusters, and AI workflows to a unified data platform.
- AI and data science teams requiring governed, discoverable, and content-aware scientific data for model training, RAG pipelines, and agentic AI workflows.
You should have working knowledge of enterprise storage concepts and familiarity with life sciences research workflows. Experience with IBM Storage Scale, Red Hat OpenShift, or NVIDIA GPU infrastructure is helpful but not required.
Section overview
Executive summary
An overview of the life sciences data challenge, IBM's data highway architecture, and the key technologies that enable scientific data to flow from instrument ingest to AI-driven analysis and long-term governance.
Introduction
A detailed examination of the four scientific data challenges (volume, velocity, variety, and veracity), the problems with traditional research workflows, how AI increases data management complexity, the concept of data gravity, and the IBM life sciences data highway architecture.
Reference architecture overview
A component-level walkthrough of the full architecture, covering the compute and AI platform (IBM Fusion HCI), the shared data platform layer (IBM Storage Scale), intelligent data management services (FDC, CAS, MCP Server, AIDP), the scientific data awareness maturity model, and an end-to-end workflow example.
Key use cases and customer benefits
Real-world application patterns across microscopy, genome sequencing, clinical imaging, AI-driven research, and data discovery — illustrated with six anonymized customer architecture examples and a summary of the platform's key benefits.
Guidelines and design considerations
Practical design guidance covering ingest, storage, compute, data management, the awareness layer, high availability, multi-institutional collaboration, data privacy and compliance, and operational considerations.
Conclusion
A summary of IBM's four core architectural capabilities — Abstraction, Acceleration, Access, and Awareness — and how the integrated platform transforms scientific data into a strategic, AI-ready research asset.
Authors
Tran Nguyen is a Technical Product Manager working in storage solutions at IBM, with a focus on developing reference architectures and driving technical initiatives for modern data and AI workloads. Her work combines technical ownership and execution across solution architecture, process development, and technical enablement. She drives cross-functional collaboration to translate technical requirements into practical solutions and guide projects from early concepts through implementation. Her background spans systems engineering, software engineering, hardware and software integration, automation, and technical research. Tran holds a degree in Materials Science & Engineering and Electrical Engineering & Computer Science from the University of California, Berkeley.
Andy Tran is a Storage Client Solutions Engineer, focused on designing reference architectures for storage, hybrid cloud, and AI infrastructure in research and data-intensive environments. He has led architecture discovery with account teams, built and documented validated lab deployments and partner integrations, and developed technical enablement material for field teams and business partners. His background includes software engineering, distributed systems, cloud infrastructure, and AI research. Andy holds a degree in Computer Science from the University of California, Riverside.
Contributors
Matthew Klos is a Senior Solutions Architect working across many industries, including financial services, research organizations, higher education, automotive, healthcare and life sciences, and many more. He has been administering and designing scale and scale system solutions for over 10 years internally and externally. Matt holds a degree in Information Technology from Marist University.
Meghan Grable is a Technical Product Manager on IBM's Client Solutions Engineering team, where she leads strategic customer engagements, first-of-a-kind solution validations, and proof-of-concept initiatives that help shape the future of IBM Storage. With a background in product management, cyber resilience, and data protection, she specializes in translating complex customer challenges into scalable technical solutions. Meghan combines expertise in enterprise design thinking, product strategy, and emerging technologies to help customers and partners accelerate innovation and business outcomes.
Feedback
This document reflects practical experience implementing these technologies in production environments. If you encounter issues not covered here, or if you have suggestions for improving the guidance, your feedback helps make this resource more valuable for the community.