Below are a set of development principles designed to make your biotech data team more effective by allowing them to not just support their bench scientist colleagues, but to get the support they need in turn, to drive your research pipeline. Each principle links to a Scaling Biotech Newsletter post with more details.
- Your highest priority is to drive progress towards your organization’s scientific objectives.
- Design projects around deliberate scientific objectives coordinated with the overall organization.
- The primary measure of progress is data-driven scientific discovery.
- The simplest technical solution that will reliably meet scientific objectives should be chosen over complex or novel approaches with marginal improvements.
- The most effective form of communication across functions and specialties is direct communications between individuals in each team.
- Collaboration across teams is more important than technical excellence within any one team.
- The key to successful collaboration is empathy for team members with different perspectives, priorities and responsibilities
- Delegating decisions and accountability as far down as you can is the only way to continuously adapt to an unknown and changing environment
- Information should be captured in a FAIR system as early as possible and anything derived from it should remain in a FAIR system.
- Technical tools can only be effective when deployed in the context of good processes and communication patterns.
- Evolve processes and tools incrementally, in parallel, based on continuous feedback, rather than introducing major changes all at once.
- Development timelines should be deliberately coordinated with experiments to maximize opportunities for feedback.