Benchmarking Validation: Why Standardized Data Would Accelerate Industry Progress

Given resource constraints at a time of increasing workload, quality organizations need a clear and complete understanding of the health of their validation.

However, digital validation processes — which often replicate paper processes on glass — remain manual and disconnected from wider quality. As a result, data captured across the lifecycle is often buried within documents and spreadsheets managed at the site or process levels, making it difficult to extract for analysis or use.

Data captured across commissioning, qualification, and validation activities is not easily accessible, so company efforts in tracking and comparing performance are limited. This is despite considerable operational benefits. Greater process standardization would allow ongoing benchmarking and reporting, enabling validation teams to improve efficiency. It would also uncover insights to reduce bottlenecks, mitigate issues early, and introduce remediation actions to reduce risk — all of which support safer treatments for patients.

The status quo of siloed data

Benchmarking remains a rare practice in validation. Validation still typically operates in a silo: each site developed its own validation processes and, later, adopted distinct digital solutions while relying heavily on manual steps in the process. Manual updates create bottlenecks and are often the “broken link” in the value chain, preventing validation managers from optimizing processes. As validation managers lack visibility, they may be unable to confirm or demonstrate to auditors that the procedure is being followed correctly.

The result is that data is either missing or collected from multiple workstreams and solutions that do not use the same format. Processes aren’t standardized across sites so data isn’t consistent even when available, and analysis and site-by-site comparisons are difficult to decipher.

The effect of siloed systems is that companies cannot compare performance across sites. To pinpoint bottlenecks, teams are forced to aggregate and interpret data manually, which becomes even more challenging when multiple sources and solutions are used. Data is often outdated or unavailable at scale, making timely updates difficult. Lacking time and resources to focus on manual data analysis, validation managers are consumed by routine validation tasks rather than strategic improvements.

Continuous improvement as a default

Companies with access to validation metrics encompassing quality gain a more complete view of performance. As validation managers can see data from start to finish of the lifecycle, they find it easier to identify gaps and risks that would prevent validation from operating smoothly. This ensures projects stay on track and improves accountability.

Benchmarking key validation data would lead to quicker release by improving process efficiency, compliance accuracy, and insight-driven decisions. This is because organizations can identify patterns once they start measuring data systematically:

• Project status. Easier to understand validation project status and due dates to drive proactive insights
• Discrepancy management. Quicker to classify and trend discrepancies (ranging from minor typos to scripting errors to system failures) and decide whether or not to investigate them jointly with quality
• Efficiency and consistency. Tracking KPIs across sites reveals which approaches lead to quicker results or differences in resourcing

Companies that benefit from detailed analytics and reporting are on track to speed up cycle times. Resilience’s former Director of Quality Applications, Tamara Redondo, explains: “In our previous solution, we were unfortunately unable to run any reports as it was difficult to extract data, so we were always looking for a way to find bottlenecks in our workflows. With Veeva, we will be able to easily identify where we can improve our process and validation approach for quicker releases and approvals of systems and equipment.”

Operational excellence within sight

Generating insights from validation data across sites would contribute to continuous improvement. If each site could consistently track end-to-end cycle times, as well as cycle times within phases (such as test development/execution/approval), resources could be allocated more efficiently based on actual performance. It would become easier to assess discrepancies (for instance, by type, vendor, or if re-testing was required) so any problems uncovered are quickly addressed by learning from better-performing sites. Reviewing a site’s periodic assessments could help identify patterns in requalification activities, for instance.

One area to monitor for continuous improvement is the volume of automated versus manual tests. As validation industry practices evolve, there is an opportunity to adopt new risk-based validation methods, such as computer software assurance (CSA) or validation by exception. As this will force a new way of working, companies need measurable baseline data to support the business case. Teams could measure the return on investment of changing their processes using quantifiable data: for example, by comparing the number of discrepancies per 100 test scripts within the old and new processes, time spent on documentation, or overall cycle times.

If all sites leverage the same digital validation system, like-for-like benchmarking on site performance becomes feasible. Over time, benchmarkable data should improve validation decision-making and improve predictability. For example, if one site consistently achieves faster batch releases due to fewer validation discrepancies or deviations, this could help other sites optimize their processes to achieve similar efficiency. Tracking operator errors during manufacturing (such as incorrect handling or procedural deviations) would lead to fewer batch inconsistencies and delays.

By providing the validation and quality data foundation for automation and predictive analytics, it becomes possible to identify other process improvements that will result in faster release and approval of systems and equipment.

Beginning a virtuous cycle

A systematic approach to validation benchmarking and reporting would support more timely decision-making, helping teams manage periodic reviews, plan validation projects, and optimize the validation lifecycle. With access to insights, your organization will be able to benchmark KPIs across sites and throughout the lifecycle, feeding continuous optimization.

Validation teams will be able to closely monitor critical activities requiring revalidation, such as requirement approvals, deviation statuses, trend analysis, and IT or GxP change controls, strengthening compliance.

Standardization across sites and data retention for all validation activities, along with their associated quality event context, will establish a solid foundation for continuous validation, automation, and future AI use cases.

Learn how Veeva’s Validation Management solution can streamline your quality and validation teams’ workflows.