Data, Monitoring, Evaluation and Reporting
This domain tests whether you can manage data and evidence for DRR decisions and report progress credibly. Expect questions on baselines, indicators, disaggregation, monitoring systems, evaluation criteria and the Sendai monitoring process.
Core rules and concepts
- Data quality depends on relevance, accuracy, timeliness, completeness, comparability and clear definitions. Record metadata, sources and limitations.
- A baseline is the starting value against which change is measured. Indicators must be specific, measurable and linked to an outcome or target. Outputs such as workshops held are weaker evidence than outcomes such as reduced exposure.
- Disaggregate data by sex, age, disability, location and other relevant factors to reveal unequal risk and reach.
- DRR monitoring often uses national disaster loss databases and the Sendai Framework Monitor. Evaluation commonly applies OECD-DAC criteria: relevance, coherence, effectiveness, efficiency, impact and sustainability.
Worked example
A country reports progress on Sendai Target B, reducing affected people. The team defines “affected” using a published methodology, sets a baseline year and disaggregates by hazard, sex and province. It checks for double counting when one person is affected by two events. It reports both the number affected and the population denominator so that a rising absolute number is not misread. The reasoning shows that a target indicator is only credible when definitions, baselines and disaggregation are transparent.
Traps that cost marks
- Reporting change without a baseline.
- Confusing outputs with outcomes.
- Double counting affected people or losses.
- Aggregating data so at-risk groups disappear.
- Treating evaluation as an audit rather than learning.
- Presenting Sendai indicators without methodological notes.