When you think about life-saving technology, does a statistical computing environment come to mind? Statistical computing environments (SCE) are critical in accelerating scientific discoveries by enabling researchers to manage, process and analyze data efficiently and compliantly, maintaining the utmost regulatory integrity.
As life sciences research generates increasingly large and diverse datasets, powerful statistical computing environments are essential for extracting meaningful insights and advancing our understanding of biological systems. By providing the necessary computational tools and infrastructure, SCEs empower researchers to uncover clinically relevant correlations, identify potential therapeutic targets and drive the data driven development of new diagnostics and treatments.
But with so many SCEs in the market, how do organizations know they’re working on the most effective, productive platforms? How can they trust the outputs and analytics while needing to produce tangible therapeutic developments that improve patients’ lives worldwide?
Just like the diversity of data sets we see when analyzing molecules and therapeutics, a million different endpoints could answer this question. But to get the answers with the least amount of severe adverse events (see what we did there?), here’s five things that leaders need to think about when choosing the statistical computing environment that they’ll trust their data to:
1. First things first? It’s the about data security
Data privacy and security should be a top priority when choosing a statistical computing environment for life sciences. Life sciences data often contains sensitive information that must be protected in compliance with guidelines and legal regulations (e.g., HIPAA and GDPR [General Data Protection Regulation]). This is not “new” news, but it’s a reminder we all need. And it’s the bedrock of a sound, usable, viable and auditable SCE.
Ensure that the statistical computing environment you choose employs role-based access control, user-traceability, and secure data storage and transfer protocols. Regular security audits and adherence to industry-standard security certifications can also help maintain high data protection. If an organization offers a validated SCE, lean in and learn the difference between validated and unvalidated environments and make your decisions based not just on your organization's current landscape but on your products' future roadmap. As the age-old adage goes, build it correctly and securely for the first time.
2. It’s secure, but is it available? Openness is key.
Here’s where the balance comes in – are we choosing security or the ability to integrate openly? For far too long, statistical computing environments have had the “either/or” option, which has left organizations vulnerable to security challenges while limiting the ability to integrate with other platforms. And when it comes to accelerating life sciences, that’s just unacceptable.
To ensure rock-solid analysis for the long term, select a statistical computing environment that can seamlessly integrate with your existing operational systems and workflows to avoid potential disruptions to your research. This may include compatibility with programming languages like SAS, R and Python and support for relevant libraries, packages and API calls to various related systems. Sharing data easily between different tools and platforms is essential for efficient collaboration and reproducibility in life sciences research.
And if this key point has won you over already, don’t be worried – SAS offers an SCE that already checks the boxes.
3. It worked! But reproducing it – and explaining it – is a challenge
You’ve hit your analysis targets and found the answers to the world’s biggest challenges, but you got a different output when you ran the analysis for the second time. How is this happening? Unfortunately, that’s the narrative for many organizations without a strong, reliable statistical computing environment.
Reproducibility and transparency are vital for maintaining scientific rigor and credibility in life sciences research. And unfortunately for some analytics programs, your results might vary over time as new packages are released to analyze your data. Without guaranteed, proven backward compatibility, your SCE could produce inaccurate results.
Strong SCEs contribute to reproducibility and transparency goals by providing an environment where researchers can document their data processing and analysis workflows, share code, and ensure consistency across different studies. By promoting standardized tools, methods and data formats, SCEs facilitate the replication and validation of research findings, strengthening the evidence base for life sciences development.
With a strong, interoperable, secure statistical computing environment, you can trust that the outcomes you’re seeing are explainable, transparent and fail-proof on every run.
4. It’s great, but no one can use it
When we read articles about democratizing access to analytics and data science, we often forget how broad that idea really is. And with statistical computing environments, the industry has seen more than a few almost impossible options to work within.
By lowering the barriers to entry for researchers with limited computational expertise, SCEs encourage a more diverse range of scientists to engage in data-driven research. This inclusivity can help drive innovation in life sciences by incorporating broader perspectives and ideas. Moreover, cloud-based SCEs can provide researchers with powerful computational resources in resource-limited settings, further promoting global collaboration. While statisticians will remain the primary experts and users of an SCE, the ability to provide explainable insights to additional resources and ensure scientific alignment is key to advancing the speed at which progress is made.
5. Pick your statistical computing environment for now and for the future
The future of life sciences is laser-focused on artificial intelligence, advanced analytics and machine learning. To stay ahead of the curve – and ensure your research stays relevant – your statistical computing environment needs to be built with the future in mind.
Many SCEs provide a platform for researchers to develop, train, and deploy AI and ML models, harnessing their potential to uncover hidden patterns and make predictions from complex biological data. By offering the necessary tools and resources for AI and ML research, SCEs facilitate the development of innovative computational approaches that can revolutionize life sciences and drive the discovery of new therapeutics and diagnostics.
