Designing MEL systems that funders trust

Start from the results framework

Indicators, definitions, disaggregation requirements and reporting frequencies should drive the data model. When forms are designed first, programmes discover late that they cannot calculate the indicators they promised.

Build quality in at capture

Validation rules, required fields, controlled lists and GPS or timestamp metadata prevent errors that are expensive to fix later. Supervisor review queues and automated flags for outliers maintain quality as programmes scale.

Quality should be monitored against the dimensions funders use in data-quality assessments, such as validity, reliability, precision, integrity and timeliness.

Make figures traceable

Every number in a donor report should link back to the records that produced it. This audit trail shortens verification visits, supports third-party monitoring and builds confidence in the programme.

Use open standards and plan for handover

Platforms such as DHIS2 and KoboToolbox, and publishing standards such as IATI, reduce lock-in and make it easier for national partners to own the system after the programme ends. Sustainability and exit planning should be written into the proposal, not added in the final year.