Monitoring & Evaluation Data Management Training Course

Monitoring & Evaluation Data Management Training Course

Introduction

Effective data management is the backbone of successful Monitoring and Evaluation (M&E) programs. Monitoring & Evaluation Data Management Training Course equips professionals with advanced skills to collect, clean, analyze, and visualize program data for evidence-based decision-making. Participants will master data governance, quality assurance, and digital M&E tools while learning to integrate real-time monitoring, data dashboards, and actionable insights into their organizational workflows. By focusing on data integrity, security, and compliance, this course ensures M&E teams can deliver accurate and impactful program evaluations.

Through a combination of hands-on exercises, real-world case studies, and interactive simulations, participants will gain proficiency in database management, statistical analysis, reporting, and performance measurement. The course emphasizes trending methodologies, including cloud-based data solutions, mobile data collection, and AI-powered analytics, ensuring participants are prepared to handle the evolving demands of the M&E landscape. By the end of this program, learners will be capable of transforming raw program data into high-quality, actionable insights that drive organizational growth and program impact.

Course Duration

10 days

Course Objectives

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

1. Apply best practices in M&E data collection, storage, and management.
2. Ensure data quality, accuracy, and integrity throughout the M&E lifecycle.
3. Utilize digital tools and software for efficient data entry and reporting.
4. Implement real-time monitoring systems for program performance tracking.
5. Design dashboards and visualizations to communicate insights effectively.
6. Conduct statistical analysis and trend identification using advanced techniques.
7. Apply data governance, compliance, and privacy standards.
8. Perform data cleaning, validation, and verification processes.
9. Integrate cloud-based and mobile solutions for remote data collection.
10. Leverage AI and machine learning tools for predictive analytics in M&E.
11. Interpret and present key performance indicators (KPIs) for decision-making.
12. Develop data-driven reports and presentations for stakeholders.
13. Apply lessons from real-world M&E case studies to improve program outcomes.

Target Audience

1. M&E officers and managers
2. Program and project coordinators
3. Data analysts and statisticians
4. NGO and government staff in program evaluation
5. Development sector professionals
6. Research and academic professionals
7. ICT specialists supporting data collection
8. Policy and decision-makers

Course Modules

Module 1: Introduction to M&E Data Management

• Fundamentals of Monitoring & Evaluation
• Importance of data in program decision-making
• Overview of M&E data types and sources
• Data lifecycle in M&E projects
• Case study: Evaluating health programs using integrated data

Module 2: Data Collection Techniques

• Quantitative vs. qualitative data methods
• Surveys, questionnaires, and interviews
• Mobile and digital data collection tools
• Ensuring respondent confidentiality and ethical considerations
• Case study: Mobile data collection for education programs

Module 3: Data Quality Assurance

• Key data quality dimensions
• Data validation techniques
• Handling missing or inconsistent data
• Auditing and verification processes
• Case study: Improving agricultural survey data reliability

Module 4: Database Management & Storage

• Database design principles for M&E
• Cloud vs. local storage solutions
• Data security and encryption
• Version control and backups
• Case study: NGO program data management system

Module 5: Data Cleaning and Preprocessing

• Identifying errors and anomalies
• Standardizing formats and variables
• Removing duplicates and inconsistencies
• Preparing data for analysis
• Case study: Cleaning health program datasets for trend analysis

Module 6: Data Analysis Techniques

• Descriptive statistics and summaries
• Correlation and regression analysis
• Trend and pattern detection
• Using Excel, SPSS, and R for M&E data
• Case study: Analyzing youth employment program data

Module 7: Advanced Data Analytics

• Predictive modeling for program outcomes
• Machine learning applications in M&E
• Scenario simulation and forecasting
• Risk and impact analysis
• Case study: Predicting outcomes of water sanitation projects

Module 8: Data Visualization & Dashboards

• Principles of effective data visualization
• Tools: Power BI, Tableau, Google Data Studio
• Dashboard design for different stakeholders
• Visual storytelling with data
• Case study: Dashboard for maternal health program monitoring

Module 9: Key Performance Indicators (KPIs)

• Selecting relevant KPIs for programs
• Benchmarking and target setting
• KPI tracking and reporting
• Integrating KPIs into decision-making
• Case study: NGO KPI framework for nutrition programs

Module 10: Data Governance & Compliance

• Policies, procedures, and standards
• Data privacy and ethical considerations
• Legal frameworks for data management
• Accountability and reporting mechanisms
• Case study: Compliance in international development projects

Module 11: Reporting and Communication

• Designing clear M&E reports
• Tailoring reports for stakeholders
• Data storytelling techniques
• Presenting complex data simply
• Case study: Reporting impact to donors effectively

Module 12: Real-Time Monitoring Systems

• Implementing real-time tracking solutions
• IoT and sensor integration
• Alert systems for program deviations
• Dashboard synchronization
• Case study: Real-time monitoring of school attendance

Module 13: Cloud-Based & Mobile Solutions

• Cloud storage and sharing platforms
• Mobile data collection apps
• Offline data collection strategies
• Integration with analytics tools
• Case study: Cloud-based monitoring for remote communities

Module 14: Troubleshooting Data Issues

• Common data management challenges
• Problem-solving techniques
• Error detection and correction
• Workflow optimization
• Case study: Resolving discrepancies in financial program data

Module 15: M&E Data Management Capstone Project

• Designing a full M&E data workflow
• Collecting and cleaning sample data
• Analyzing and visualizing insights
• Presenting findings to stakeholders
• Case study: End-to-end M&E project simulation

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.