SkillBridge Liberia
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What this engagement solves

Many institutions in Liberia are working to strengthen their monitoring, evaluation, accountability, and learning systems. While data collection efforts are widespread, the challenge often lies in verification and use—ensuring that information supports decision-making and meets donor requirements. International evaluations have noted opportunities for improvement in data quality review processes and the linkage between monitoring and management. This course supports institutions in building practical routines that make MEAL systems function effectively: clear data collection protocols, quality checks, and reporting flows that supervisors can rely upon with confidence.

Engagement performance & assurance

Core KPIs, with verification and evidence steps.

🧮 Attendance ≥ 85% Participants attend at least 85% of sessions. 85%
📝 Tool completion ≥ 90% At least 90% submit all six workplace tools. 90%
⭐ Quality score ≥ 80 / 100 Clarity, completeness, accuracy, practicality, consistency. 80%
📌 +90 day adoption ≥ 70% Still using at least one core tool at +90 days. 70%

Verification & Evidence

Baseline to +90-day adoption, with QA tags for each engagement.

Baseline

Quick diagnostic + supervisor inputs before training.

Endline

Tools scored (0-100) + supervisor sign-off at completion.

+90-Day

Follow-up confirming whether tools became routine.

Accuracy Completeness Timeliness Traceability Integrity
Equity target: >=40% women, >=30% youth
Safeguards: zero tolerance, do-no-harm, safe space
Data: minimal data, secure storage, limited access

This course is suitable for:

  • M&E officers and data managers in ministries, NGOs, and donor projects.
  • Program coordinators responsible for reporting to donors such as USAID, EU, World Bank, AfDB, and Global Fund.
  • Field staff who collect data and seek to strengthen verification systems.
  • Supervisors who review reports and wish to enhance data quality assurance.
  • PIU staff managing World Bank, USAID, EU, or AfDB-funded projects.
  • Ministry planning and statistics officers responsible for sector-wide data aggregation.
  • NGO monitoring officers preparing for donor Data Quality Reviews.
  • Anyone involved in preparing for GAC or donor audits where data integrity is assessed.

The course focuses on practical data quality and reporting systems. Participants learn how to:

  • Build data collection instruments that align with specific reporting requirements for different donor formats.
  • Establish routine data quality checks covering completeness, accuracy, timeliness, and precision.
  • Create verification workflows between field teams, county offices, and Monrovia-based supervisors.
  • Design simple dashboards that support decision-making rather than merely satisfying reporting obligations.
  • Establish feedback loops so data informs program adjustments and operational improvements.
  • Document data sources, assumptions, and limitations to create clear audit trails.
  • Manage indicator tracking tables with version control and handover protocols that preserve institutional memory.
  • Prepare for donor Data Quality Reviews and GAC audits through organized evidence packaging.
  • Address Liberia-specific operational contexts: limited connectivity, transitions from paper to digital systems, and staff mobility between institutions.
  • Incorporate gender-responsive M&E practices, including disaggregated data collection and analysis as required by most donor agreements.

Each participant develops practical tools adapted to their institution, including:

  • Data collection instruments (forms, checklists, mobile protocols using ODK or KoboToolbox adapted for low-connectivity environments).
  • Data quality verification checklist with five-criteria rubric.
  • Indicator tracking table with data source documentation, baseline values, and target trajectories.
  • Monthly data review template for supervisors with variance analysis and action triggers.
  • Simple dashboard layout for program decision-making using basic Excel or Power BI.
  • Data flow map showing information pathways across administrative levels.
  • MEAL SOP covering the full data cycle from collection to reporting.
  • Data quality improvement plan with timelines and responsible parties.
  • Backup data collection protocol for paper-based systems when digital platforms are unavailable.

Institutions gain the following practical benefits:

  • More reliable data reaching supervisors and donors in a timely manner.
  • Improved preparedness for audits with organized, verifiable evidence.
  • Reduced time spent addressing data queries or reconstructing information.
  • Clearer accountability for data collection and verification roles at each level.
  • Enhanced decision-making based on consistent, well-documented information.
  • Stronger donor confidence supporting sustained partnerships and funding continuity.
  • Reduced risk of disbursement delays related to reporting processes.
  • Better mission preparedness with systematically organized evidence.
  • Preservation of institutional knowledge through documented systems.

Verification evidence is collected through:

  • Completed data collection tools and quality checklists with supervisor sign-off.
  • Supervisor validation of indicator tracking tables confirming data source documentation.
  • Comparison of data completeness and accuracy metrics using actual project information.
  • +30-day check on data quality routine implementation with field spot-checks.
  • +90-day adoption verification with supervisor confirmation of continued tool use.
  • Data flow documentation and dashboard usage evidence.
  • Mock Data Quality Review results demonstrating improved readiness.
  • Documentation of data quality improvements achieved through the new system.