Why TileDB Carrara, Why Now? The smarter way to unify your scientific data.

By 2030, the pharma industry is expected to increase investment in AI and machine learning by 600%. But even as life sciences organizations pour billions into AI strategy, they cannot afford to overlook their data management infrastructure. This comes at a time when the challenges of multimodal data are already mounting. 

As life sciences firms face a rapidly changing data landscape, TileDB Carrara offers a new vision for a multimodal database designed for discovery. Let’s examine why life sciences data management is at a crucial inflection point, what is causing these changes and how TileDB Carrara can empower organizations to win.

Your data infrastructure is the bottleneck preventing transformative change

You spend days waiting on query requests from IT teams. You wrestle with 400MB Excel spreadsheets where critical metadata lives only in cells with unexplained highlighting colors. You squint at inbox and Slack search results to find OneDrive links from different teams because there's no unified system tracking what data exists or where it's stored. If you’re a bioinformatician, you know these frustrations and the need for a better data infrastructure.

"When you find yourself spending more time attempting to retrieve data from the database and restructure it than actually analyzing the data, that's a red flag," explains Aaron Wolen, TileDB's Single Cell Product Manager. This isn't just inefficiency. It's a creativity killer that robs scientists of the cognitive energy needed for actual discovery.

The data management pain is systemic, and the symptoms are everywhere: Query performance collapsing as datasets grow from thousands to millions of samples. Python and R teams accidentally duplicating data because collaboration requires format conversion. Custom code proliferating to manage endless file formats, creating technical debt that slows research and eats up technology budgets. “With ever growing variant data sets, you have an N+1 problem for all intents and purposes,” says Chad Krilow, Director of Solutions engineering at TileDB. “It’s a constant struggle because each new sample you add requires reprocessing the entire cohort.”

This data management bottleneck is sadly quite common across life sciences firms. 90.5% of life sciences R&D leaders said it’s difficult to store and catalog different data modalities side-by-side. As an AstraZeneca Executive Director describes: “I think we’re really good at understanding what data we have. I don't think we're really good at understanding what data we're missing, to draw the right conclusions from the data sets, especially as we're trying to do larger scale discovery multimodal analysis.” For life sciences organizations to drive transformative change, they must deal with this data management platform problem.

The perfect storm of multimodal data in life sciences

At the same time, three converging forces are creating a perfect storm that makes change a necessity. First, multimodal data has become vital to biotech and pharma research, as 84.5% of life sciences R&D leaders say using multimodal data in life sciences R&D strategy is both important and urgently needed. “In the therapeutic areas where most pharmas are focused, like immunology and oncology, almost 80% of these data are multimodal,” says a Director of Data and Computational Sciences at Sanofi. “You need this multimodal data to find biomarkers and to find your niche of patients compared to your competitors.”

Second, multimodal data types like spatial transcriptomics, proteomics, multi-channel imaging and single-cell data refuse to fit into the rectangular tables of most database solutions. As Krilow describes, "With tabular databases, you’re dealing with a rigid row-based structure that struggles with the high dimensionality and sparse nature of multimodal data like genomics, VCF, single-cell RNA sequencing and spatial and biomedical imagining. This often leads to data being shoved into formats that are inefficient for both storage and analytics. It really hinders the pace of discovery." 

Third, the accelerating use of machine learning has introduced the promise of AI systems that can access and understand multimodal data across systems and organizations. 31.7% of life sciences research leads require their data management platform to have built-in capabilities for data science and machine learning. But as these AI systems emerge, many life sciences organizations lack the data infrastructure to empower such AI agents. 

"Healthcare and life sciences organizations are sitting on goldmines of data, but they can't use it together because it's trapped in incompatible systems,” says Stavros Papadopoulos, founder and CEO of TileDB. Lacking the data infrastructure to bridge these gaps, these orgs can’t “build AI that can see the complete picture of a patient or drug target, just fragments." In short, current AI strategies fail not because their LLMs aren't powerful enough, but because fragmented data infrastructure prevents their AI agents from efficiently accessing the data they need to become truly effective. 

TileDB Carrara: The multimodal database built for discovery

To navigate the perfect storm of multimodal data, TileDB Carrara offers a truly multimodal database that reimagines how life sciences organizations manage and access their data. Built on multi-dimensional arrays that naturally represent the structure of genomic, single-cell and imaging data, Carrara eliminates the need to force complex modalities into rigid schemas. Here’s how Carrara is transforming data management:

Native multimodal architecture: Carrara treats every data type as a distinct "modality" with semantic context, not just a blob. VCF files become queryable three-dimensional arrays. Single-cell data stores as natural sparse matrices. Multi-channel images retain their dimensional structure. As TileDB Senior Solutions Architect Spencer Seale explains, "An array can be architected to store any type of file or data structure, flexing to fit anything from a table to complex imaging resolution sets."

Streamlined collaboration: Carrara offers team spaces and asset views to transform how research groups share and manage data access, essentially establishing trusted research environments for secure collaboration. These capabilities eliminate traditional bottlenecks among global research teams while maintaining high standards of data security and regulatory compliance. Carrara also makes it simple for data science teams using Python and R to collaborate on identical datasets without duplication. "We can store single-cell data once and provide an idiomatic interface for Python users and an idiomatic interface for R users working with the same data," Wolen notes.

Cloud-native performance: Direct querying from object storage eliminates expensive data egress and memory requirements, helping to reduce computing costs for large-scale bioinformatics pipelines. Krilow's team has seen Quest Diagnostics "efficiently integrate and query data from millions of cells to identify novel drug targets" while dramatically reducing computational costs.

AI-ready foundation: Carrara provides the unified data catalog and governance layer that AI agents need. Multimodal data across systems becomes easily searchable with proper metadata and permissions. As Seale describes, "Once you connect your S3 or cloud storage to TileDB, you can traverse that like it’s a local file system using Carrara," removing authentication barriers and enabling seamless AI agent access. This AI vision extends beyond today's needs. As foundational AI models like scGPT and Geneformer emerge to tackle complex problems like Alzheimer's disease, Carrara can provide the seamless access to massive, diverse datasets these models need. 

"The less time you have to spend worrying about infrastructure and data management problems, the more cognitive energy you can focus on actual research," Wolen emphasizes. That's exactly what Carrara delivers—not just better data management, but the omnimodal foundation your AI strategy has been missing. 

The future belongs to organizations that recognize AI success requires data infrastructure transformation. This makes Carrara more than just a database upgrade. It's your competitive advantage in the multimodal future. To learn more, explore TileDB Carrara today.