What is it? Why is it important?

Data Quality Assurance (DataQA) includes a set of activities, processes, and implemented measures to ensure that study data is of high-quality. High-quality data grants confidence that obtained study results are reliable and credible


In order to comply with DataQA goals, the SP-INV must implement a risk-based strategy that guarantees that study data is correctly collected, stored, shared, and analysed. As a consequence, applicable data handling processes and guidelines should be in place.

What do I need to do?

As a SP-INV:

  • Identify potential risks that might negatively affect the quality of your study data (e.g. documentation errors, data input errors, incompatible software updates, untrained staff)
  • Evaluate and prioritise risks based on likelihood of occurrence and impact on data quality
  • Based on your risk assessment (RAF) define applicable risk control-measures
  • Train staff on data handling procedures and the implementation of risk control-measures
  • Define a surveillance plan in order to check the efficacy of risk-control measures during study conduct, including the emergence of new risks (e.g. process verification and training)
  • Document your risk assessment including preventive measures in the study Risk Assessment form


*For more information* refer to Quality and Risk and Monitoring in this Trial Guide.


Example of risk control-measures

  • Personalise CDMS setup in order to support ongoing data quality (e.g. variable specification)
  • To perform system validation and functionality testing upon CDMS updates
  • Implement planned monitoring visits during study conduct in order to check if data entered in the study database corresponds to the original data (e.g. raw data)
  • Ensure ongoing system maintenance and security, including ongoing data backup and recovery due to system failure
  • Define data export/import processes that control for unwanted data alterations, data loss, and a risk to data confidentiality

Where can I get help?

Your local CTU can support you with experienced staff regarding this topic


ICH GCP E6(R2) – see in particular guidelines

  • 5.1 for Quality assurance and control
  • 5.5. Trial Management, data handling, and record-keeping

ISO 9001:2015 – Quality Management Systems – Requirements


  • CDMS – Clinical Data Management System
  • CTU – Clinical Trials Unit
  • ICH GCP – International Council for Harmonisation - Good Clinical Practice
  • ISO – International Organization for Standardization
  • RAF – Risk Assessment Form
  • SOP – Standard Operating Procedures
  • SP-INV – Sponsor-Investigator
  • WI – Working Instructions
Development ↦ Data Handling ↦ Data Quality ↦ Quality Assurance

Provides some background knowledge and basic definitions

Basic Monitoring
Basic Drug or Device

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Drug or Device

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Quality and Risk
Set-Up Drug or Device

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Drug or Device

Starts with last study visit completed

Ends after study publication and archiving

Completion Statistics
Completion Drug or Device
Current Path (click to copy): Development ↦ Data Handling ↦ Data Quality ↦ Quality Assurance

Please note: the Easy-GCS tool is currently under construction.