Cloud Platforms for M&E Data Storage Training Course

Cloud Platforms for M&E Data Storage Training Course

Introduction

The rapid growth of digital data in development, humanitarian, and governance programs has made cloud platforms a critical backbone for modern Monitoring & Evaluation (M&E) systems. Cloud-based data storage enables secure, scalable, real-time, and cost-efficient management of large volumes of quantitative and qualitative M&E data. From baseline surveys and routine monitoring to evaluation datasets and learning repositories, cloud platforms support data integrity, accessibility, interoperability, and collaboration across dispersed teams and stakeholders.

Cloud Platforms for M&E Data Storage Training Course equips participants with practical, hands-on knowledge of using cloud platforms for M&E data storage, governance, security, and analytics readiness. Participants will learn how to design cloud-based M&E data architectures, select appropriate platforms, manage data lifecycles, ensure compliance with data protection regulations, and integrate cloud storage with dashboards, mobile data collection tools, and analytics engines. The course emphasizes real-world case studies, applied exercises, and implementation strategies relevant to development programs, NGOs, governments, and donors.

Course Duration

10 days

Course Objectives

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

1. Understand cloud computing fundamentals for M&E systems
2. Compare leading cloud platforms (AWS, Azure, GCP) for M&E use cases
3. Design scalable cloud-based M&E data storage architectures
4. Implement secure data storage and access controls
5. Apply data governance frameworks in cloud environments
6. Manage structured and unstructured M&E datasets
7. Ensure data privacy, compliance, and ethical data management
8. Integrate cloud storage with mobile data collection tools
9. Optimize cost management and cloud budgeting for M&E
10. Enable real-time data access and collaboration
11. Prepare cloud-stored data for analytics, dashboards, and AI
12. Implement backup, disaster recovery, and data resilience strategies
13. Develop a cloud migration roadmap for existing M&E systems

Target Audience

1. Monitoring & Evaluation Officers and Managers
2. Data Analysts and MIS Specialists
3. Development Program Managers
4. NGO and CSO ICT/Data Teams
5. Government M&E and Statistics Units
6. Donor Agency M&E and Learning Staff
7. Research Institutions and Consultants
8. Digital Transformation and Innovation Leads

Course Modules

Module 1: Introduction to Cloud Computing for M&E

• Cloud concepts and service models
• Benefits of cloud storage for M&E data
• Common misconceptions and risks
• Cloud vs on-premise M&E systems
• Case Study: NGO transitioning from local servers to cloud storage

Module 2: Overview of Cloud Platforms

• Amazon Web Services (AWS) for M&E
• Microsoft Azure for development programs
• Google Cloud Platform (GCP) for data analytics
• Open-source and hybrid cloud options
• Case Study: Comparing platforms for a national M&E system

Module 3: M&E Data Types and Storage Needs

• Quantitative vs qualitative M&E data
• Survey, administrative, and sensor data
• File, object, and database storage
• Metadata and documentation standards
• Case Study: Multi-source data storage for a health program

Module 4: Designing Cloud-Based M&E Data Architecture

• Logical and physical data models
• Data lakes vs data warehouses
• Folder structures and naming conventions
• Scalability and performance planning
• Case Study: Architecture for a multi-country project

Module 5: Data Security and Access Management

• Identity and access management (IAM)
• Role-based access for M&E teams
• Encryption at rest and in transit
• Audit logs and monitoring
• Case Study: Preventing unauthorized data access

Module 6: Data Governance in the Cloud

• Data ownership and stewardship
• Version control and data quality rules
• Documentation and data catalogs
• Ethical data use principles
• Case Study: Governance framework for donor-funded programs

Module 7: Data Privacy and Compliance

• Understanding GDPR, DPA, and local regulations
• Informed consent and sensitive data
• Data anonymization and pseudonymization
• Cross-border data storage issues
• Case Study: Managing beneficiary data securely

Module 8: Integrating Mobile Data Collection Tools

• KoboToolbox, ODK, CommCare integration
• Automated data uploads to cloud storage
• API-based data pipelines
• Data validation and cleaning workflows
• Case Study: Real-time field data integration

Module 9: Managing Unstructured and Multimedia Data

• Storing photos, audio, and videos
• Qualitative data for evaluations
• File tagging and searchability
• Storage optimization techniques
• Case Study: Multimedia data in outcome harvesting

Module 10: Cost Management and Optimization

• Understanding cloud pricing models
• Storage tiering and lifecycle policies
• Cost tracking and budgeting
• Avoiding cost overruns
• Case Study: Reducing cloud costs for an NGO

Module 11: Backup, Disaster Recovery, and Resilience

• Data backup strategies
• Replication across regions
• Disaster recovery planning
• Business continuity for M&E systems
• Case Study: Data recovery after system failure

Module 12: Collaboration and Data Sharing

• Secure data sharing with partners
• Permissions for donors and evaluators
• Version control and change tracking
• Collaborative workflows
• Case Study: Multi-stakeholder data access

Module 13: Preparing Data for Analytics and Dashboards

• Connecting storage to BI tools
• Data pipelines for visualization
• Supporting real-time dashboards
• Data readiness for AI/ML
• Case Study: Cloud-backed M&E dashboards

Module 14: Migrating Legacy M&E Data to the Cloud

• Assessing existing data systems
• Data cleaning before migration
• Migration tools and approaches
• Risk management during migration
• Case Study: Migrating 10 years of M&E data

Module 15: Future Trends in Cloud-Based M&E

• Serverless architectures for M&E
• AI-ready cloud data storage
• Interoperability and open data
• Sustainability and green cloud computing
• Case Study: Future-ready M&E data ecosystems

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.