Why this matters
Donors and managers make decisions, and sometimes release funding, based on the numbers M&E teams report. If those numbers are wrong, the project loses credibility. That is why adverts ask for data quality assurance and donor reporting. As a junior, you may be the person who checks the numbers and drafts the monthly table.
What good quality data looks like
M&E teams often check data against a few qualities:
- Valid: it measures what the indicator says it measures.
- Reliable: it is collected the same way every time, by every officer.
- Precise: detailed enough for the decision, such as broken down by community.
- Timely: available when it is needed, not three months late.
- Complete: no missing communities, months or records.
- Integrity: protected from mistakes and from being changed to look better.
Data quality checks
A common check is to recount from the source. Take a reported number, such as 312 farmers trained in March (an invented figure), go back to the attendance sheets and count again. If the recount gives 298, find out why: double counting, a missing sheet, or a typing slip.
Other routine checks: