An RSI Understanding Risk method
Data quality assessment
Data quality assessment is one of the methods used by Risk Sciences International to better understand risk.
Broadly
Data quality assessment evaluates the completeness, accuracy, consistency, and relevance of datasets used in risk analysis. It ensures that conclusions drawn from data are credible and that uncertainty is understood and documented. Techniques range from simple audits to structured scoring systems such as Klimisch scores or GRADE. While vital for defensibility, challenges include subjectivity in evaluation criteria and variations in data origin or reporting standards.
More specifically
RSI conducts formal data quality assessments as part of literature reviews, regulatory dossier preparation, and internal model development. Its approach includes predefined criteria, transparent scoring, and expert review to contextualize quality within the intended use of the data. RSI ensures that decisions made using imperfect data are clearly framed with appropriate caveats and confidence statements, reinforcing trust and rigor in risk outputs.