Clinical trials are growing in complexity, due to higher data volumes, more diverse data types, greater international collaboration, and shifting regulatory demands. These developments place increasing pressure on data analysis teams who must integrate and analyze large, often fragmented datasets to derive actionable insights, all while maintaining international standards for data security and traceability. In response to these and other challenges, clinical trial data workflows are increasingly moving to cloud-based platforms1. This transition is not simply a method for overcoming the challenge outlined above, but provides tangible benefits for researchers that surpass the capabilities of traditional methods. This blog will focus on five core reasons why workflows are moving to the cloud and how this benefits researchers. We’ll also talk about the role of solutions like Aspire™ in enabling more efficient and compliant clinical trial data management in an increasingly complex environment.
1. Enhanced Scalability and Flexibility
The growing volumes of data in clinical trials, such as genomic sequencing datasets, place demands on organizations’ data storage and processing capabilities2. Cloud-based platforms scale easily to meet the growing needs of clinical research. This helps teams avoid both overinvestment in infrastructure and shortages that can occur during sudden increases in data volume. This flexibility provides organizations with solutions that adapt to their specific needs. This enables them to better allocate their resources. Pre-validated cloud systems allow data teams to start working on large datasets immediately. This will enable teams to go without the need for lengthy on-premise setups and hardware procurement. Researchers can add software as needed, giving them the flexibility to tackle more complex workflows and adapt to emerging technologies ahead of the competition.
Aspire from Instem is a cloud-based, next-gen Statistical Computing Environment (SCE) that provides configurable modules to enable teams to scale resources based on project needs. The platform supports SAS, R, and Python coding languages to ensure seamless onboarding and buy-in. Another important aspect of a clinical analytics system is automation.
2. Automation Reduces Manual Effort and Improves Accuracy
Automation is becoming a crucial component of modern clinical data management and analysis. Large, complex, and fragmented datasets are challenging to manage and track using manual methods. Manual approaches leave room for errors in data collection and analysis and can have far-reaching consequences for trial success and patient outcomes. These tasks take up a significant amount of a biostatistician’s time, preventing them from applying their expertise to extract meaningful insights from the data. They also introduce compliance risks, as tracking data versions, user activity, and access permissions becomes increasingly complex with dispersed, multi-location teams.
Cloud-based systems like Aspire provide automated processes for audit trails, archiving, scheduling, and batch execution which reduce manual data handling and free statisticians’ time for data analysis rather than tedious documentation tasks. Automated code execution also enables large teams to maintain consistency by clearly defining procedures for managing various tasks. Though automation is important, one of the most critical aspects of a modern clinical analytics system is its compliance measures and features.
3. Simplified Compliance and Traceability
The regulatory landscape is constantly evolving, and regulatory bodies continue to place a strong emphasis on data security3. Security concerns are increasing as data migrates to cloud-based systems, making it more vulnerable to theft4. Regulatory bodies, such as the FDA, EMA, and Health Canada, require robust traceability for how patient data is handled and processed3,5,6. Maintaining strict documentation trails and limiting access to specific users is essential for maintaining compliance. Meeting regulatory requirements is challenging without dedicated, automated systems to maintain audit-ready documentation and track user access across large collaborative teams.
Cloud-based platforms offer built-in modules to ensure compliance throughout all stages of clinical trial data workflows. For instance, the Aspire Audit module records all activities within the SCE, while the Aspire Trace module will track inputs, code, and outputs, providing an automated solution for ensuring compliance.
4. Seamless Collaboration and Sharing
Traditional approaches to clinical trial data analysis and storage limit teams’ ability to collaborate effectively and work on projects simultaneously. On-site data silos complicate data sharing, while poor accessibility makes duplicated efforts more common, as researchers may not have a record of who is working on a project at a given time. Duplicating worksheets and datasets for sharing increases security risks, while transferring large datasets by email can be challenging, if not impossible. Incompatible software used by different team members further complicates real-time collaboration. Centralized cloud-based systems offer researchers secure access to a unified workspace, eliminating duplication and enhancing data security. These systems enable real-time communication and feedback, improving efficiency and facilitating faster resolution of bottlenecks and technical issues.
5. Faster Time-to-Market
Finally, speed of delivery is a vital aspect of an efficient clinical analytics system. Cloud-based clinical trial data workflows mitigate inefficiencies and risks in clinical trial data management and analysis, resulting in a faster time-to-market. Flexibility enables researchers to adopt newer practices more readily, while automation eliminates the inefficiencies and risks associated with manual data handling. Cloud-based systems simplify security and compliance requirements by providing built-in audit trails, role-based access controls, encrypted storage, and data version tracking. These platforms free researchers from tedious work, allowing them to apply their expertise to overcome challenges that require expert human input. Simplified collaboration through a centralized system allows teams to work together on shared documents in real time, increasing efficiency and accelerating analysis and reporting. By combining these advantages, cloud-based SCEs eliminate risks and barriers to clinical trial progression, resulting in a faster time-to-market and quicker access to life-changing therapies for patients.
Conclusion
The growing complexity of clinical trials is driving a shift towards cloud-based SCEs. These platforms not only enable researchers to handle growing complexity but also provide significant advantages over traditional methods, particularly in areas like scalability, automation, compliance, collaboration, and efficiency. When combined within dedicated modular platforms, such as Instem’s Aspire SCE, these advantages mitigate risk and allow researchers to achieve a faster time-to-market.
Contact a member of our team to learn how shifting your clinical trial data workflows to Aspire can help you achieve more efficient and compliant clinical trial processes for deeper insights and better outcomes.
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References
1. Cloud Computing In Pharmaceutical Market Size & Trends 2033. Accessed June 26, 2025. https://www.globalgrowthinsights.com/market-reports/cloud-computing-in-pharmaceutical-market-103388
2. Mallappallil M, Sabu J, Gruessner A, Salifu M. A review of big data and medical research. SAGE Open Med. 2020;8:2050312120934839. doi:10.1177/2050312120934839
3. European Medicines Agency. Guideline on computerised systems and electronic data in clinical trials. Published online March 9, 2023. Accessed June 26, 2025. https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf
4. Dawood M, Tu S, Xiao C, Alasmary H, Waqas M, Rehman SU. Cyberattacks and Security of Cloud Computing: A Complete Guideline. Symmetry. 2023;15(11):1981. doi:10.3390/sym15111981
5. Research C for DE and. Electronic Source Data in Clinical Investigations. April 29, 2020. Accessed June 26, 2025. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electronic-source-data-clinical-investigations 6. Canada H. Guidance Document: Part C, Division 5 of the Food and Drug Regulations “Drugs for Clinical Trials Involving Human Subjects” (GUI-0100). September 29, 2022. Accessed June 26, 2025. https://www.canada.ca/en/health-canada/services/drugs-health-products/compliance-enforcement/good-clinical-practices/guidance-documents/guidance-drugs-clinical-trials-human-subjects-gui-0100/document.html
