Key Challenges in Life Science

The life sciences sector is undergoing rapid technological advancement, yet it faces structural challenges that limit its full potential — especially in the context of multi-omics data and precision health.

• Low Public Participation in Genomic and Multi-Omics Testing

Although the cost of sequencing has dropped dramatically, public engagement remains low. The general population lacks awareness of how genomic and multi-omics insights can benefit their health. Even among those who participate, there is limited understanding or utilization of their data, leading to weak user retention and low data activation rates.

• Fragmented Data and Persistent Privacy Concerns

Multi-omics data — spanning genomics, transcriptomics, proteomics, metabolomics, and beyond — are often fragmented across isolated silos, whether in academic institutions, hospitals, or private companies. This fragmentation severely hampers data integration and research reproducibility. At the same time, users have little transparency or control over how their biological data is stored, shared, or used, further eroding trust.

• Outdated and Unsustainable Business Models

Many current business models in life sciences are either extractive (e.g., selling kits without long-term user value) or overly reliant on centralized platforms and one-off transactions. Such models fail to incentivize ongoing data contribution, collaborative research, or ecosystem growth — making them difficult to scale or sustain in the long run.