Choosing the right multimodal data platform for life sciences [webinar recap]

If you couldn’t join us live, you can watch the full webinar recording here to catch all the insights from our session.

In a dynamic and data-driven industry like life sciences, managing and leveraging large volumes of multimodal data is crucial for driving discovery and development. In our latest webinar, I led an in-depth exploration of how TileDB’s multimodal data platform is transforming data management in biopharma and research. Here’s a quick recap of the key insights and takeaways from the session.

The growing complexity of multimodal data

I opened the session with a discussion on the increasing importance of multimodal data in drug discovery, where diverse data types (such as genomic, imaging, and single-cell data) need to be integrated effectively. Managing this variety of data is inherently challenging. From data silos to vendor lock-in, traditional data solutions are often not designed for the complexity or scale that scientific discovery demands.

Challenges in current solutions

Through live polling and insights from recent surveys, attendees shared their experiences, revealing that 83% found multimodal data complex and in need of substantial engineering resources. Additionally, two-thirds of attendees mentioned issues with scalability and vendor lock-in, which hinders collaboration and slows down research timelines.

I outlined two common but insufficient approaches:

1. General-purpose platforms (e.g., Databricks, Snowflake): Although flexible, they often lack the depth needed to handle scientific data’s intricacies.
2. Bespoke scientific solutions: These are domain-specific but struggle to scale and support diverse data types, creating barriers to collaboration.

Evaluation criteria for a multimodal data platform

To help organizations select a multimodal data platform that meets both current and future needs, I introduced a four-part evaluation framework::

1. Data centralization and cataloging: Ensuring the platform can centralize data with rich metadata, search functionality, and interoperability.
2. Collaboration: Securely sharing data without moving it, handling federated data environments, and supporting granular access control.
3. Analysis at scale: Enabling fast, scalable analysis with support for notebooks, dashboards, and popular tools.
4. Economical scalability: Ensuring the platform’s scalability is both high-performance and cost-effective, with cloud-native architecture and flexibility.

TileDB’s approach to multimodal data management

TileDB’s platform is specifically built to address these pain points. I described the core building blocks of TileDB’s multimodal data solution:

• Cataloging data: TileDB provides a unified catalog that enables users to store, manage, and annotate all data types in a centralized repository, supporting FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
• Secure collaboration: TileDB enables users across research teams to access, share, and collaborate on federated data, reducing data movement and ensuring compliance with regulatory requirements.
• Efficient analysis: The platform supports programmatic access for data scientists, integrates with popular scientific tools, and offers an interactive analysis environment, making it easy to derive insights across data sets.

Use cases: real-world success stories with TileDB

Multiple small biotechs and large biopharma companies use TileDB for their multimodal data. Phenomic AI, a leader in cancer drug discovery, uses TileDB to handle large-scale single-cell data for machine learning applications. By enabling scalable analysis and data integration, TileDB has empowered Phenomic AI to accelerate its platform’s development.

For Quest Diagnostics, the TileDB platform unified their data infrastructure, delivering a 26% reduction in storage costs and improved efficiency for managing millions of samples per year. The results show how TileDB’s platform can scale with data demands, support compliance, and increase operational efficiency.

Conclusion

Today’s life sciences data environment demands a platform that can seamlessly integrate, catalog, and analyze diverse data at scale. TileDB offers a unified solution built specifically to handle scientific data’s complexities, giving organizations a powerful edge in driving insights and accelerating discovery.

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