Objective:

To highlight the underutilization of data in biotech organizations and its potential to improve sustainability efforts.

Approach:

• Data Fragmentation: Biotech data is often stored in disparate systems, making holistic analysis difficult.
• Metadata Quality Issues: Inconsistent descriptions and missing fields hinder data aggregation and comparison.
• Cultural Barriers: Data is often treated as a by-product rather than a strategic asset, limiting cross-departmental analysis.

Key Findings:

• Biotech generates extensive data but struggles to answer basic environmental performance questions, such as resource consumption and waste generation.
• Data analysis can reveal patterns in resource use, including high plastic consumption in specific assays.
• Operational insights derived from data can inform workflow redesigns aimed at reducing environmental impact.

Interpretation:

Sustainability improvements in biotech depend on more effective data utilization.

Limitations:

• Digital systems are primarily designed for compliance, which limits their effectiveness for optimization.
• There is a lack of incentives for complete data capture and insufficient training in data literacy among scientists, hindering effective data use.

Conclusion:

Linking laboratory data with procurement and sustainability decisions may enhance environmental sustainability in biotech operations.