Nine Key Evaluation Criteria for a Multimodal Data Platform

Multimodal data, including high dimensional multiomics data, represents a cutting-edge frontier in biopharma, where it is leveraged to enhance treatment capabilities and improve patient outcomes through early disease detection and diagnosis. However, the complexity and volume of this data pose challenges in deriving value, leading biotech and pharma companies to often rely on expensive and resource-intensive DIY solutions based on open-source tools supplemented with additional infrastructure to meet enterprise needs. This approach demands significant engineering resources, time, and operational costs, as multiple teams within organizations, such as research, data, AI, and informatics teams, work collaboratively to accelerate insights and streamline processes. Investing in a dedicated multimodal data platform can be transformative, with key criteria for these platforms including scientific record-keeping, a unified data model, vector search capabilities, adherence to FAIR data principles, support for emerging data types, scalable computational power, facilitation of unsupervised learning, regulatory compliance, and strong governance controls.