When a risk practitioner is building a key risk indicator (KRI) from aggregated data, it is CRITICAL that the data is derived from:

While business process owners can provide context and input, the raw data itself needs to be representative, not just sourced from individuals.

For a Key Risk Indicator (KRI) to be meaningful and accurate, the underlying data must be representative of the actual conditions and events it aims to measure. If the data is biased, incomplete, or not truly reflective of the operational environment, the KRI will provide a skewed or misleading view of the organization's risk, leading to poor decision-making.

Industry benchmark data is useful for comparison but cannot serve as the primary source for an organization's internal KRIs, which must reflect its unique risk profile.

Data automation systems are a means of collecting data, but the criticality lies in the quality and representativeness of the data collected, regardless of the collection method.