An RSI Understanding Risk method
Data analysis and mining
Broadly
Data analysis and mining refers to the systematic examination of large or complex datasets to uncover patterns, relationships, or anomalies relevant to risk understanding. Techniques range from descriptive statistics to advanced machine learning and network analysis. It is used across public health, environmental monitoring, and risk surveillance. The power of the method lies in its ability to detect insights not visible to manual review; its risks include overfitting, spurious correlation, and interpretability challenges.
More specifically
RSI uses data analysis and mining to support evidence synthesis, pattern detection, and trend forecasting in risk contexts. Whether analyzing incident databases, health surveillance data, or regulatory submissions, RSI applies statistically sound methods tailored to data characteristics and decision needs. RSI’s emphasis on interpretability ensures that findings are not only technically valid but also decision-useful—bridging analytics and policy in a coherent risk understanding approach.