Digital transformation of laboratory operations is a significant undertaking. The different approaches that can be applied, transition of legacy systems, data migration, and the incorporation of new technologies create a web of considerations in determining your strategy. But, successful digital transformation is not rooted in technology.

You can have the best tech in the world, but without well-designed processes with a specific technology to support efficient process design and data management, all the effort that went into transforming your laboratory may not yield the outcome that you were looking for. There is no single best practice for digital lab transformation, but it is crucial to find a “playbook” that works within the organization or company’s structure and culture. The “playbook” is a recipe for approaching the R&D process implementation and upgrades.

Starting with the end goal in mind will lead you down the most successful path to digital transformation. At the end of the day, you want to have actually usable data.  So any digital transformation must be centered around producing, collecting, and using high-quality, reproducible data.

“Think about how you are going to use your data before you architect your data infrastructure. If you can’t use your data, what’s the point of collecting it?” – Dr. Timothy Gardner, CEO, Riffyn

Connecting People, Processes, and Workflows

A common error many R&D organizations make when planning their digital transformation is to focus on integrating disconnected systems and eliminating manual processes along the way. This approach suffers from the lack of purposeful integration.

Successful digital transformation is built around connecting people, processes, and workflows. This type of connectivity ensures that your R&D teams will always be on the same page, processes and workflows can be easily tracked and are reproducible. The data you collect are usable and inform iterative process design decisions.

The data management technology you choose will be critical for successfully connecting people, processes, and workflows. Most data tools are not designed to support the complete R&D process and the agility of modern R&D organizations. Additionally, they fail to uphold FAIR data practices.

The four foundational principles of FAIR data¹:

• Findability
• Accessibility
• Interoperability
• Reusability

As R&D organizations seek data management solutions, they increasingly turn toward SaaS (Software as a Service) and IaaS (Infrastructure as a Service) as solutions. Advances in informatics has laid the foundation for the digitally transformed enterprise. Expectation is now shifting to Results as a Service – business results, not test results. To accomplish this, a practical and results-driven data architecture is required.

The Riffyn Nexus™, a SaaS platform, is the world’s first Process Data System, with a process-centric data architecture. By placing R&D processes at the center of everything, Riffyn Nexus solves the disconnected workflow, collaboration, and scientific data analysis problems inherent in traditional informatics technologies.

Data Accessibility is… Everything

Riffyn Nexus captures experiment data directly into the context of well-documented scientific experiments and processes, which form the foundation of conducting and communicating science.

It traces the flow of materials through the entire R&D lifecycle, both within a single experiment and across months of experiments. The data is then delivered in a clean, standardized, vendor-agnostic format that can be consumed and analyzed by any person, software system, or programmatic interface.

The primary goal of science is to gain knowledge and understanding through investigation to explain the cause and effect relationships between variables. Riffyn Nexus makes the understanding of these relationships accessible and scalable, helping your R&D organization bring product to market faster by accelerating process improvements with insight gains from usable data.

Conclusion

Digital transformations can be a daunting and complex undertaking for any scientific organization. Successful digital transformation focuses on outcomes; going truly end to end across the organization and systems. Riffyn Nexus facilitates all aspects of digital transformation, connecting people, processes and workflows to unlock the collaborative power of the team.

Why it Matters for You

To accomplish a full digital transformation, a practical and result-driven data architecture is required. Factors that support digital transformation include:

• Implementing Riffyn Nexus facilitates all aspects of digital transformation, connecting people, processes and workflows to unlock the collaborative power of the team. It collects data and information from people, instruments and databases, and puts them in the context of processes.
• Riffyn Nexus is built upon FAIR data principles. Ensuring your data are FAIR will enable you to gain insights into cause and effect, uncover complex relationships within your data, and inform scientific decisions.
• Using Riffyn Nexus supports a researcher’s entire experimentation and learning cycle by synthesizing scientific processes and data capture for data that is always ready for machine learning. As a result, critical details are not lost, and effort not wasted.

References

¹Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18. Accessed May 12, 2021.