Modern computing and analytics platforms must deliver scalability to support diverse user communities, from regulatory submissions and patient safety in GxP environments to non-GxP use cases like exploratory analysis, all built on an adaptive, scalable infrastructure.This case study explores the journey of creating a validated, multi-lingual analytics ecosystem on a shared storage platform, leveraging insights from non-regulatory use cases. It highlights key architectural decisions, governance and validation strategies, and operational lessons, providing practical guidance for designing scalable, compliant analytics platforms in regulated environments.

Research organizations invest heavily in ELN and data infrastructure yet consistently struggle to make scientific data truly reusable. This talk draws on hands-on experience delivering production ELN systems and CRO data ingestion frameworks at a large pharma company to examine what separates implementations that get used from those that don't. The core insight: adoption is not a change management problem. It is whether the scientific workflow is repeatable enough to systematize, whether the data can be processed into something analytically usable, and whether anyone downstream actually needs it. A diagnostic framework for assessing all three—before committing resources.

NCI's Cancer Research Data Commons is a data ecosystem built to administer and manage the data generated by the various NCI funded programs for FAIR data sharing across the research community. NCI has been a pioneer in establishing a democratized ecosystem co-locating data and compute, building secure and scalable data commons and cloud analytical platforms for the diverse users from cancer community.

In this session, we will discuss a federated data architecture and operating model to shift ownership of data to domain teams and treat datasets as discoverable, secure products—enabling data producers to publish well-documented, versioned data products, data consumers to self-serve high-quality assets for analytics and ML, and platform teams to provide the plumbing, tooling, and federated governance that makes it all scalable. By combining product thinking, domain ownership, an organization-wide data catalog, and platform-provided capabilities (cataloging, lineage, observability, automated quality checks, and data contracts), we have made pipelines resilient to sudden schema changes, elevated accountability for data quality, and removed the central-team bottleneck that traditionally limited access. The result: faster time-to-insight, more reliable data for data science and analytics, and a governed, interoperable ecosystem where standards and policy are enforced without stifling domain innovation, demonstrating how a pragmatic federated implementation can deliver both autonomy and enterprise-grade control.

MATRIX (Montai AtTRIbute eXpander) system is a cloud-native platform for large-scale molecular property generation with full data traceability. It produces 50+ attributes for 500M compounds in under two hours, addressing throughput and reproducibility challenges in biotech R&D while enabling faster, more reliable discovery workflows.This scalable, reproducible workflow modernizes cheminformatics and provides a blueprint for rapid, traceable, research-grade bioinformatics discovery.

As demand for analytics and AI accelerates, many organizations face a hidden bottleneck: they don’t know what data they have, where it lives, or how quickly it can be made usable. While FAIR principles define what good data should look like, they don’t solve the operational challenge of discovering, curating, and serving real-world datasets across fragmented storage environments. This session explores the emerging concept of a data facility—an evolution beyond file systems, catalogs, or data lakes. Examine how organizations can architect platforms that actively surface valuable datasets, connect distributed storage, apply readiness workflows, and deliver data that is truly “compute-ready” for downstream research.  Attendees will learn practical approaches for automating data discovery, reducing reliance on human intermediaries, and building infrastructure that turns legacy and siloed data into reusable research assets.

• How are knowledge graphs supporting reliable scientific intelligence
• Lessons from early deployments
• Remaining challenges and barriers to scale​

• Learn how leading global pharma companies are applying quantum principles to real-world research. 
• Discover the quantum companies transforming traditional processes around drug development and drug discovery.
• Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.​

• Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
• What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
• What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?​

• Where are the highest-value collaboration opportunities between academia/TTOs and industry (biopharma, startups, AI/data, investors), and what makes them actually work?
• What types of partnerships are most effective in practice (licensing, co-development, startup creation, sponsored research), and how do both sides evaluate quality and fit quickly?
• What specific formats, access points, or structures at the Bio-IT event would actually drive follow-up, partnerships, and deal-making?

• How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
• What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
• How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?
  

• How real-world data is being used alongside clinical and experimental data
• Challenges in standardization, access, and regulatory acceptance
• Opportunities to improve outcomes, access, and long-term patient insights

• Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work 
• How structured experimental memory makes past decisions, failures, and context reusable across teams
• How organizations can make scientific knowledge reusable for both humans and AI

• How to think strategically about IP early in biotech and AI-driven research
• Common pitfalls in patent and trademark applications and how to avoid them
• Navigating ownership, protection, and commercialization of innovations
  

• Why do current workflow systems fail most scientists in practice? 
• Can no-code and AI-driven workflows meet standards for reproducibility, validation, and clinical readiness? 
• What would a “production-ready” workflow ecosystem actually look like across teams and organizations?

• The gap between AI ambition and the reality of data foundations, platforms, and operating models
• What “AI‑ready data” actually means in practice—beyond pilots, dashboards, and proofs of concept 
• Where AI initiatives most often break down: data integration, data quality, metadata, and governance at scale​

Metaphore Bio is building a function-first generative drug-design platform where data infrastructure is a first-class product. Cloud-orchestrated autonomous labs stream high-throughput functional and multiomic data into a governed cloud platform that powers AI/ML and protein design at scale. I will share how our storage and compute architecture balances speed, performance, cost, and compliance while accelerating functional biologics.

Lilly is developing an integrated digital platform to transform large molecule discovery in the age of AI. From assay data capture and NGS workflows to MLOps and DMTA, the solutions in this platform are unified in both data and UI by design. This talk will highlight the roadmap, progress, impact, challenges, and learnings from this transformational platform.

Structure Designer is an internally built, customizable application for AbbVie's Discovery medicinal chemists, centralizing compound design workflows and data access, and replacing fragmented, vendor-dependent solutions with a flexible, web-based platform. Its modern architecture integrates rapid calculations, seamless session management, and secure user controls, significantly improving speed, usability, and accessibility for both internal staff and external collaborators, all at a lower cost than commercial alternatives.