Modern demands on software quality and development have surpassed the ceiling of the document-based approach to CSV.  The limitations of the traditional static document have made it increasingly difficult to keep CSV on pace with the ever-increasing adoption of Agile and DevOps, but as with most things in the business world, a large enough need will drive change.  From that need, innovation has come in the way of moving away from the document-driven approach and shifted to a more dynamic, data-driven validation methodology. 

Removing the documents from the CSV equation has opened the door to a more streamlined and dynamic approach to CSV, allowing Life Sciences organizations to fully utilize solutions and practices widely adopted across the enterprise business landscape.  A data-driven CSV solution enables the adoption of a wider variety of tools and increased automation capabilities, while wrapping it all in controlled, repeatable, compliant workflow. 

Over recent years, many large pharma, biotech and medical device companies have begun to adopt a solution set to utilize this methodology after recognizing and embracing its many benefits……. 

• Real-time insight and access: Gone are the days of “reactive” discovery and analysis to identify compliance issues.  Real-time access allows users to monitor and take corrective action before simple compliance concerns become significant compliance breakdowns. 

• Repeatable process:  Quality control is paramount in all life sciences business processes.  Using configurable workflows, data-driven CSV helps to provide repeatability and efficiency, greatly reducing the risk of human error while enforcing the correct process. 

• Test automation:  Test automation can easily be incorporated into an ALM tool and leveraged along with manual testing.  These test elements and their associated execution results can be sent through a formal review and approval process, while being recorded in the audit history.  In addition, historical information such as number of executions, execution times and defect trends can be leveraged as future releases of the application are being considered. 

• Comprehensive reporting:  The data-driven approach truly provides a panoramic view of the entire CSV landscape – and captures all necessary information and data for rigorous, comprehensive reporting.  Real-time reports can show exactly where you are in a validation project, including what reviews/approvals might be stuck and how you can re-assign elements to keep things moving forward. 

• Analytics and analytical reporting:  Life Science companies are intense collectors and users of data; however, they have historically been unable to extract intelligence from the underlying validation data.  A data-driven approach provides the ability to capture relevant and meaningful data at each critical step of the process.  This means users can analyze the data along the entire Application Life Management (ALM) cycle.  For example, analytics can help create “what-if” scenarios or highlight productivity (or lack thereof) with different out-sourced teams. 

• Traceability:  The data-driven approach enables users to trace data across the full lifecycle – regardless of the tools that are used (requirements tool, agile tool, testing tool, or ITSM). 

• Improved efficiency:  It streamlines processes to help reduce processing time, while ensuring accurate tracking. 

• Reduced risk:  A systematic, iterative approach, applied throughout a computer systems lifecycle, helps drive better decision-making, ensures product quality, and minimizes supply chain disruptions.  Factors that can reduce risk include repeatability, electronic signature security, visibility and audit trail. 

With all these improvements over the traditional document-based approach to validation, it’s clear why so many teams are viewing the data-driven methodology as the best of breed in computer systems validation.  The ability to control compliance and reduce risk while enabling faster, higher quality dev and test delivery has truly allowed Life Sciences organizations to make their CSV practices flow with organizational goals instead of hindering them.   

However, a methodology is only as strong as the solutions that support it...... 

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