Taming Frontier Data Part 1: How to efficiently process complex data queries at scale

Life sciences organizations are grappling with the challenge of managing and processing massive and complex datasets, as the healthcare industry generates about 30% of the world's data, growing at an annual rate of 36%. This vast amount of data, including frontier data from sources like genomics, offers significant opportunities for breakthroughs in treatments but also presents daunting complexity. Phenomic AI, focusing on oncology target discovery, exemplifies these challenges as they scaled their single-cell data from 2 million to approximately 30 million cells in a year, which initially slowed their bioinformatics workflows. To handle the increased data processing demands and efficiently manage complex metadata queries, Phenomic AI adopted TileDB, a platform that allowed them to consolidate their data into a unified system supporting single-cell and future multiomics research. This transition enabled Phenomic AI to quickly query large datasets, facilitating faster identification of new drug targets and enhancing their research capabilities. The narrative suggests that new data management approaches can alleviate the difficulties faced by life sciences organizations in mastering unstructured data, with the promise of future insights into secure and effective team collaboration.