Data Integrity by Design: Building Systems That Prevent Errors

Data integrity is fundamentally a system design challenge rather than a documentation exercise. Most integrity risks emerge not from intentional falsification, but from processes that rely heavily on manual entries, informal workarounds, and individual memory. In this episode of The GMP Insider, we discuss how the FDA evaluates whether systems are designed to prevent, detect, and correct errors — emphasizing controls over trust. Key topics include:• How system design influences data reliability• Risks created by manual processes and informal practices• Embedding controls directly into workflows• Reducing reliance on individual judgment• Detecting and correcting issues before inspections Strong data integrity systems limit opportunities for manipulation, integrate oversight into routine operations, and ensure corrections remain transparent and traceable. Good data does not result from trust alone — it is the outcome of intentional system design.