Building an effective computing environment early on helps ensure positive research outcomes later. 

Now more than ever, Life Science research and development is driven by technological innovation.

That doesn’t mean human ingenuity has become any less important. It simply depends on accurate, well-structured data more than ever before. The way Life Science researchers capture, store, and communicate that data is crucial to their success.

This is one of the reasons why Life Science professionals are leaving major pharmaceutical firms and starting their own new ventures. Startups have the unique opportunity to optimize their infrastructure from the very start and avoid being hamstrung by technology and governance limitations the way many large enterprises often are.

Optimized tech and data management models offer a significant competitive advantage in the Life Science and biopharma industries. For example, implementing AI-based or other predictive software and lean workflows makes it easier for scientists to synthesize data and track toward positive results or, equally as important, quickly see the need to pivot their strategy to pursue a more viable solution.  The net effect is a reduction in the time and cost of discovery, which not only gives R&D teams a competitive upper hand but improves outcomes for patients.

Time is Money in R&D

Life Science research and development is a time-consuming, resource-intensive process that does not always yield the results scientists or stakeholders would like. But startup executives who optimize discovery processes using state-of-the-art technology early on can mitigate two significant risks:

• Fixing Broken Environments. Building and deploying optimized computing infrastructure is far easier and less expensive than repairing a less-than-ideal computing environment once you hit an obstacle. 

• Losing Research Data Value. Suboptimal infrastructure makes it difficult to fully leverage data to achieve research goals. This means spending more time manually handling data and less time performing critical analysis. In a worst-case scenario, a bad infrastructure can lead to even good data being lost or mismanaged, rendering it useless. 

Startups that get the experience and expertise they need early on can address these risks and deploy a solid compute model that will generate long-lasting value.  

5 Areas of Your Computing Model to Optimize for Maximum Success

There are five foundational areas startup researchers should focus on when considering and developing their compute model:

1. Technology

Research teams need to consider how different technologies interact with one another and what kinds of integrations they support. They should identify the skillset each technology demands of its users and, if necessary,  seek objective guidance from a third-party consultant when choosing between technology vendors. 

2. Operating Systems

Embedded systems require dependable operating systems, especially in Life Sciences. Not only must operating systems support every tool in the researchers’ tech stack; but individual researchers must also be well-acquainted with the way those systems work. Researchers need resource management solutions that share information between stakeholders easily and securely.

3. Applications and Software

Most Life Science organizations use a variety of on-prem, Cloud-enabled, open-source, and even home-grown applications procured on short-term contracts. This offers flexibility, but organizations cannot easily coordinate between software and applications with different implementation and support requirements. Since these tools come from different sources and have varying levels of post-sale documentation and support, scientists often have to take up the heavy burden of harmonizing their tech stack on their own.

4. Workflows

Researchers have access to more scientific instruments than ever before. Manufacturers routinely provide assistance and support in implementing these systems, but that is not always enough. Startups need expert guidance in establishing workflows that utilize technological and human resources optimally. 

But building and optimizing scientific workflows is not a one-size-fits-all endeavor; teams with multiple research goals may need separate workflows optimized differently to accommodate each specific research goal.

5. Best Practices

Optimizing a set of research processes to deliver predictable results is not possible without a stable compute environment in place. For Life Science research organizations to develop a robust set of best practices, they must first implement the scientific computing model that makes those practices possible. This takes expert guidance and implementation from professionals who specialize in IT considerations unique to a research and development environment that, many times, lean startups simply don’t have on the team.

Maximize the Impact of Research Initiatives 

Emerging Life Science and biotech research companies have to empower their scientific teams to make the most of the tools now available. But architecting, hinging, and implementing a robust and effective compute model requires experience and expertise in the very specific area of IT unique to research and discovery. If the team lacks such a resource, scientists will often jump into the role of solving IT problems, pulling them away from the core value of their expertise.

The right bio-IT partner can be instrumental in helping organizations design, develop, and implement their computing environment, enabling scientists to remain focused on science and helping to position the organization for long-term success.   

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.