Data Quality Audits for M&E Coordinators Training Course

Course overview

Monitoring and evaluation coordinators are frequently asked to report indicator results from partner projects, field teams and multiple data systems while managing incomplete registers, duplicate records, inconsistent definitions and weak audit trails. When data quality problems are discovered late, programme managers may make funding, targeting or scale-up decisions on unreliable evidence. This course equips coordinators to plan and conduct structured data quality audits that test whether reported results are accurate, complete, timely, valid, precise and traceable to source documentation.

Participants learn to build a data quality audit protocol, define audit questions, select indicator samples and verify reported values against registers, case files, survey records and digital data exports. They practise indicator reference sheet reviews, source data verification, data flow mapping, desk-based checks and field-level audit techniques. The course also covers Excel-based error testing, KoboToolbox form controls, discrepancy classification, root-cause analysis, corrective action planning and presentation of findings to programme and partner management.

Delivery combines instructor-led demonstrations with a realistic humanitarian programme case involving health, protection and cash-assistance indicators. Participants work through an audit from planning to reporting, using sample datasets, registers, indicator tracking tables and partner submissions. By the end of the week, each participant leaves with a usable data quality audit pack: an audit plan, sampling worksheet, verification checklist, findings log, corrective action tracker and management-ready audit report outline.

The course is designed for M&E coordinators who need to establish or strengthen routine data assurance across projects, implementing partners or country programmes. It is equally relevant where teams are preparing for donor reviews, evaluations, annual results reporting or internal assurance processes.

Course objectives

By the end of this course, participants will be able to:

• Develop a data quality audit protocol using accuracy, completeness, timeliness, validity, precision and integrity criteria
• Construct indicator reference sheets that define numerators, denominators, disaggregation rules, sources and reporting frequencies
• Map end-to-end data flows from field collection through aggregation, validation and donor reporting
• Apply risk-based sampling methods to select indicators, sites, records and reporting periods for verification
• Perform source data verification by reconciling reported results with registers, case files and digital exports
• Use Excel error checks, pivot tables and reconciliation formulas to identify duplicates, missing values and calculation discrepancies
• Conduct root-cause analysis and convert audit findings into prioritised corrective and preventive actions
• Produce a data quality audit report and management dashboard that communicates evidence, risks, owners and deadlines

Benefits of attending

Target competencies

Who should attend

• Monitoring and Evaluation Coordinators — who oversee indicator reporting across projects, partners and field locations
• M&E Officers — who validate routine monitoring data before it enters organisational reports
• Programme Quality Managers — who need defensible evidence for programme adaptation and assurance reviews
• MEAL Managers — who establish data quality systems across humanitarian and development portfolios
• Programme Managers — who rely on credible results data to manage delivery, budgets and partner performance
• Implementing Partner M&E Focal Points — who must improve source documentation and reporting controls

Requirements and prerequisites

Participants should understand basic project monitoring concepts, including indicators, targets, actuals, disaggregation and routine reporting. Experience working with indicator tracking tables, paper registers, survey datasets or partner reports is useful, but prior audit experience is not required. Participants should be comfortable entering formulas, sorting and filtering data in Microsoft Excel. Bring a laptop with Excel installed if attending in person; live-online participants need reliable internet and access to downloadable course files. Statistical software, database administration, Power BI expertise and advanced evaluation design knowledge are not required. Complete beginners in M&E should expect a demanding course and should first understand logical frameworks and indicator definitions.

Training methodology

The course uses short instructor-led briefings followed by structured audit work on a simulated multi-partner humanitarian programme. Participants inspect indicator reference sheets, trace data flows, test KoboToolbox exports, reconcile reported figures in Excel and review sample registers against partner reports. Small groups conduct a scoped audit, classify discrepancies and defend findings in a management debrief. The instructor provides templates, worked examples and feedback throughout. On Day 5, participants adapt the audit pack to one live programme or reporting process from their own organisation and define immediate application actions.

Course outline

Day 1: Data quality assurance foundations

• Purpose and limits of data quality audits in development and humanitarian programmes
• Data quality dimensions: accuracy, completeness, timeliness, validity, precision and integrity
• Distinguishing routine data quality assessment from evaluation and financial audit
• Indicator reference sheets and operational indicator definitions
• Data source inventories for registers, surveys, case management and digital forms
• Data flow mapping from enumerator or service point to donor report
• Audit scope, independence, ethics and protection of sensitive beneficiary data

Workshop: Participants map the data flow and identify assurance risks for a cash-assistance indicator from field registration to a quarterly donor report.

Day 2: Planning a defensible audit

• Audit objectives, questions and criteria linked to reporting risks
• Risk assessment matrices for indicators, partners, locations and reporting periods
• Risk-based and purposive sampling approaches for source data verification
• Sample size considerations for record checks and site visits
• Audit protocols, terms of reference and evidence requirements
• Verification checklists for paper registers, spreadsheets and digital databases
• Audit schedules, opening meetings and document request lists

Workshop: Participants prepare an audit plan, risk matrix and sampling worksheet for a fictional health and protection programme.

Day 3: Verifying source data and digital records

• Source data verification procedures for reported numerator and denominator values
• Reconciliation of aggregate reports with beneficiary-level records
• Completeness testing for missing records, blank fields and absent disaggregations
• KoboToolbox form validation rules, skip logic, required fields and constraint checks
• Excel filtering, duplicate detection and conditional formatting for data errors
• Pivot tables and SUMIFS formulas for indicator recalculation
• Audit evidence logs, screenshots, document references and chain-of-custody practices

Workshop: Participants reconcile a KoboToolbox export, paper register extracts and a partner results report, then document verified discrepancies in an evidence log.

Day 4: Analysing findings and fixing systems

• Discrepancy classification by severity, recurrence and reporting impact
• Root-cause analysis using the five whys and fishbone diagrams
• Testing data entry, aggregation, approval and version-control controls
• Assessing partner capacity and supervision arrangements
• Corrective versus preventive actions for recurring data quality failures
• Action prioritisation using risk, effort, ownership and deadline criteria
• Follow-up verification and closure criteria for audit actions

Workshop: Participants conduct a root-cause workshop on recurring over-reporting and produce a prioritised corrective action tracker with accountable owners.

Day 5: Reporting findings and embedding routine assurance

• Writing evidence-based audit findings, conclusions and recommendations
• Rating data quality risks and communicating material limitations
• Management report structures for programme directors and partner leadership
• Excel audit dashboards for discrepancies, action status and repeat findings
• Presenting difficult findings without undermining partner relationships
• Integrating audits into reporting calendars, partner reviews and supervision plans
• Personal implementation planning for the first 90 days after training

Workshop: Participants produce and present a concise data quality audit report and 90-day implementation plan based on the week-long programme case.

Tools & standards covered

Microsoft Excel, KoboToolbox, Power BI, USAID Data Quality Assessment Guidelines

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions