The monitoring phase is where the implementation of the chosen alternative is systematically followed and evaluated over time.
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Objectives of the phase
- Track whether the implementation is progressing as planned.
- Measure actual outcomes against expected impacts.
- Detect deviations, emerging issues and unintended consequences.
- Provide feedback for corrective actions and future decision-making
What to expect in this phase
This phase typically involves:
- Defining indicators and metrics for tracking performance.
- Collecting and analysing data on implementation and outcomes.
- Comparing observed results with expected results from the analysis.
- Continuously reporting progress to relevant stakeholders.
- Identifying the need for adjustments or corrective measures.
In practice, monitoring can be automated, for example, with technical monitoring systems or it can be carried out through participatory assessment involving experts and/or stakeholders. In the latter case, the approaches vary from data-driven reporting systems to interactive participatory monitoring for example, through interviews
Regular and systematic information on implementation progress and evidence on achieved impacts and performance.
Follow up Questions for the AI
Ask the following questions to get more information from the AI.
- What are the key principles of effective monitoring in decision processes?
- How should indicators for monitoring be selected and defined?
- What is the difference between monitoring and evaluation?
- How can deviations from expected outcomes be interpreted?
- What types of data collection methods are appropriate for monitoring?
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Benefits and Risks of Using AI
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Benefits (++)
- Automating data collection, processing and reporting.
- Detecting patterns, trends and anomalies in monitoring data.
- Supporting real-time dashboards and visualisation tools for tracking performance against defined indicators.
- Identifying early warning signals of implementation problems
Benefit Scoring: ++ Medium
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Risks (!!)
- Misinterpretation of monitoring data without domain-specific understanding.
- Over reliance on automated analysis without human validation.
- Lack of transparency in AI-driven monitoring tools.
- False alarms or missed signals due to limitations of the model
Risk Scoring: !! Medium
Questions to Better Understand the Benefits and Risks of AI
Questions to make use of the benefits
Prerequisite for each of these questions is that the results of the analysis as well as sensitivity analysis are available.
- Can you suggest indicators for monitoring the success of the chosen alternative?
- Can you analyse the monitoring data and identify key trends or deviations and visualise the progress of implementation using appropriate charts?
- Can you compare the actual impacts of the actions with the expected results from the analysis?
Note on all the above questions: The stakeholders and/or facilitator should always validate the results, and AI can make misinterpretations of the provided information
Questions to avoid the risks
- What are the risks of relying on AI for monitoring and tracking performance? Understanding of the risks is essential in terms of successfully carrying out the DA process.
- How can incorrect interpretations of monitoring data affect decisions
Questions NOT to ask
Create a fully automated plan for monitoring and evaluation?
Monitoring requires human validation and contextual understanding.