How are organisations adapting evaluation and learning practices amid funding constraints?
Cutting MEAL field visits to save money was the single biggest mistake we made.
When funding ran tight across our climate resilience projects in Bikita, Chiredzi, and Mutare, we initially tried replacing field visits with phone surveys. The data came back thin, and communities felt abandoned because nobody was sitting down to talk with them.
We realised that the real challenge was not merely reducing the cost of evaluation; it was enhancing its value for decision-making and organizational learning.
To fix this, we shifted monitoring directly into the hands of local women and youth; we trained them on offline KoBoToolbox etc and participatory community scorecards using basic Android phones. We instituted a robust feedback uptake mechanism; we realised that instead of waiting for an endline, community monitors shared scorecard data during quarterly reflection sessions right in the wards.
That single move cut routine M&E costs by nearly 30% while transforming data into immediate action. In Chiredzi, for instance, paper reports showed great seed distribution numbers, but community scorecards revealed farmers were losing livestock to dry spells and lacked feed preservation knowledge. Because our feedback loop forced a direct link between field evidence and project adjustments, we immediately pivoted activities toward traditional grain multiplication and fodder preservation.
To keep data objective, our MEAL team combined internal spot-checks with peer evaluation reviews across ward teams, catching errors early while building local ownership.
If I had to do it again, I’d build community-led monitoring and active feedback uptake loops into project design from day one, not wait for a budget squeeze to force the shift.
Lesson learned: Data is only as valuable as the decisions it drives, and it is only as trusted as the people collecting, analysing and reporting it
RE: Evaluation under pressure : Strengthening evidence use, accountability and learning amidst funding constraints (Joint EvalforEarth-ALNAP discussion)
Zimbabwe
Wilbert Marimira
MEAL Expert
Tsorodzo dzeAfrika Advisory
Posted on 23/10/2026
How are organisations adapting evaluation and learning practices amid funding constraints?
Cutting MEAL field visits to save money was the single biggest mistake we made.
When funding ran tight across our climate resilience projects in Bikita, Chiredzi, and Mutare, we initially tried replacing field visits with phone surveys. The data came back thin, and communities felt abandoned because nobody was sitting down to talk with them.
We realised that the real challenge was not merely reducing the cost of evaluation; it was enhancing its value for decision-making and organizational learning.
To fix this, we shifted monitoring directly into the hands of local women and youth; we trained them on offline KoBoToolbox etc and participatory community scorecards using basic Android phones. We instituted a robust feedback uptake mechanism; we realised that instead of waiting for an endline, community monitors shared scorecard data during quarterly reflection sessions right in the wards.
That single move cut routine M&E costs by nearly 30% while transforming data into immediate action. In Chiredzi, for instance, paper reports showed great seed distribution numbers, but community scorecards revealed farmers were losing livestock to dry spells and lacked feed preservation knowledge. Because our feedback loop forced a direct link between field evidence and project adjustments, we immediately pivoted activities toward traditional grain multiplication and fodder preservation.
To keep data objective, our MEAL team combined internal spot-checks with peer evaluation reviews across ward teams, catching errors early while building local ownership.
If I had to do it again, I’d build community-led monitoring and active feedback uptake loops into project design from day one, not wait for a budget squeeze to force the shift.
Lesson learned: Data is only as valuable as the decisions it drives, and it is only as trusted as the people collecting, analysing and reporting it