Monitoring & Evaluation, the data side of it.
M&E is how a programme proves what it did — indicators tracked, targets checked, results reported to people who weren’t in the field. That proof is only as strong as the data behind it. I handle the part that keeps it strong: cleaning what comes in from the field, reconciling what partners report, and building the tracking tables a programme actually reports from.
Monitoring and evaluation data analysis, in your terms.
Reconciling two record sets
Data Quality Assessments (DQA), back-checks, spot-checks against source
Deduplication
Unique reach vs total reach — removing beneficiaries double-counted across activities
Recurring reporting
Indicator Tracking Tables (IPTT), quarterly donor reports, target vs actual variance
Data cleaning
Enumerator error monitoring, form validation, sex and age breakdowns
Building data pipelines
Kobo, ODK and SurveyCTO submissions turned into analysis-ready datasets, DHIS2 extracts
Before / after analysis
Baseline, midline and endline comparison
Multi-source consolidation
Partner and sub-grantee data merged into one structure, 5W matrices (who, what, where, when, whom)
Payment reconciliation
Cash and voucher transfers checked against beneficiary lists
What it runs on.
KoboToolbox, ODK & SurveyCTO
Form design, validation rules, cleaning raw submissions
DHIS2
Extracts and data element mapping for indicator reporting
Excel & Google Sheets
Where most IPTTs and donor templates still live
SQL & Python (pandas)
Dedup logic and repeatable cleaning across large submission sets
Power BI, Tableau & Looker Studio
Dashboards built for programme managers, not just donors
Something in the numbers not reconciling?
If a figure won’t tie back to source, a reach number is being double-counted, or a reporting cycle is eating a week it shouldn’t — describe it and I’ll tell you honestly whether I’m the right person for it.
This page describes what I can do — not a list of programmes I’ve worked on.