Non-governmental organisations · Monitoring and evaluation teams · Public-health programmes · Field research teams
NGO data quality audit in Kenya
Find the weak points in programme data before they reach a donor report.
For NGOs, M&E teams, public-health programmes and field-research teams that need to know whether their monitoring or survey data is complete, consistent, traceable and ready to support a management or donor decision.
Book a 20-minute demo ↗
Starting pointDataset audit: KSh 15,000–30,000Start with a 20-minute fit checkBook a 20-minute demo →
- A de-identified Kobo/ODK, Excel or CSV export
- Questionnaire and codebook where available
- Indicator/reporting requirements and known field context
Define the decision and data flow → Agree on safe access → Audit the records and controls → Separate errors from review signals → Prioritise corrective action
A bounded dataset audit is normally scoped after the fit check; timing follows record volume, number of form versions, issue depth and whether field clarification is available.
KSh 15,000–30,000 is the usual audit range. One written scope is used for every client; country or nationality does not change the fee.
Do not send identifiable participant data through the public enquiry. RMA agrees minimum necessary access, de-identification, retention and secure workspace controls before intake.
- A finding of fraud or misconduct
- Undocumented correction of source submissions
- Legal, clinical or regulatory certification
Substantial audits include one findings-review meeting and a seven-calendar-day correction window for factual or agreed-scope errors.
What you leave with
NGO data audit service in Kenya: clear findings and corrective action.
- A documented issue register covering missingness, duplicates, invalid values, inconsistent coding and broken skip logic
- A review of questionnaire controls, codebooks, indicators and the link between collection fields and reporting needs
- Timing, device, GPS and repeated-pattern signals flagged for human review without presenting them as proof of fraud
- A prioritised cleaning and remediation plan that distinguishes urgent corrections from longer-term system improvements
- A management-ready summary of material risks, limitations, actions and the evidence that supports each finding
The session flow
Define the decision and data flow
We map the programme questions, indicators, collection tools, exports, responsible roles and the reports the data is expected to support.
Agree on safe access
We identify the minimum necessary records, remove direct identifiers where possible and agree on a secure, role-appropriate transfer or workspace.
Audit the records and controls
We inspect structure, coding, completeness, duplicates, ranges, logic, field timing and available location or device signals using documented rules.
Separate errors from review signals
Confirmed data defects are distinguished from unusual patterns that require programme or field-team context before any conclusion is made.
Prioritise corrective action
The review closes with an issue register, cleaning priorities, collection-tool improvements and a practical monitoring plan for the next cycle.
See how review signals become an issue register and corrective-action plan.
Inspect a transparent NGO-style demonstration covering range and logic violations, interview-duration anomalies, possible straight-lining, GPS scope and a statistical test that was responsibly withheld when assumptions failed.
Questions, answered
Does a data audit prove that an enumerator committed fraud?
No. An audit can identify missing records, impossible values, suspicious repetition, timing or location anomalies and weak controls. Those are review signals—not proof of intent or misconduct—and must be assessed with field context and due process.
Which data sources can you review?
KoboToolbox and ODK exports, Excel, CSV, SPSS-ready files, codebooks, questionnaires and indicator tables are practical starting points. Other systems can be considered after confirming a safe export format and the access required.
Can the audit cover GPS and field-location patterns?
Yes, when the programme collected location data lawfully and the review is necessary. GPS accuracy, repeated coordinates and implausible movement can be assessed as probabilistic quality signals, with privacy safeguards and human validation.
Do we send participant names or identification numbers?
Not through the enquiry form. We first agree on de-identification, minimum necessary access, retention and secure handling. Direct identifiers should be removed unless there is a documented, lawful reason they are essential.
Can this become recurring monthly data-quality QA?
Yes. A one-off audit can establish the rules and baseline; recurring QA can then be scoped around agreed indicators, review thresholds, named responsibilities and a secure reporting cycle.
ONE CONTROLLED STANDARD
Review the quality and reliability method behind the service.
Scope acceptance, immutable source data, issue decisions, assumption checks, human QA and versioned reporting are part of the operating model.