An Introduction to Data Validation in Clinical Data Management [Video]

Learn how data validation in clinical data management supports confident decision making and a smooth database lock. We explain what data validation really means in practice, why regulators focus on data integrity, and how strong validation reduces rework, late surprises, and inspection risk.

In this explainer, we show where data validation fits across the study lifecycle, from build and early conduct through to last patient last visit and database lock. You’ll hear how risk-based validation keeps effort proportionate by focusing on critical data such as primary endpoints, dosing, and adverse events, while avoiding unnecessary noise for sites and study teams.

We walk through the core components of effective validation, including the role of a clear data validation plan, purposeful edit checks, efficient query management, and early integration of external data such as central labs, devices, randomisation, and safety systems. We also explain why standardisation of units, identifiers, visit timing, and controlled terminology is essential for reliable reconciliation and downstream SDTM and ADaM creation.

You’ll also learn about common pitfalls that slow studies down, such as vague rules, late onboarding of external feeds, and unclear ownership, and how pragmatic governance, audit trails, and training help teams meet ALCOA++ expectations without creating unnecessary paperwork.

At Quanticate, our biometrics and data management teams support sponsors with inspection-ready data validation approaches that balance quality, efficiency, and regulatory confidence. Request a consultation below today.