In the modern data-driven world, the sheer volume and velocity of information can overwhelm traditional data quality checks, leading to flawed analysis and unreliable conclusions in monitoring and evaluation (M&E). This training course is designed to equip M&E and data professionals with the knowledge and skills to leverage AI for a proactive and automated approach to data quality assurance. By moving beyond manual inspections, participants will learn how to build intelligent systems that can automatically detect errors, anomalies, and inconsistencies in real-time, ensuring their data is consistently accurate, complete, and fit for purpose.

This comprehensive program provides a practical, hands-on roadmap for integrating AI-based data quality tools into the M&E workflow. Participants will gain skills in applying machine learning algorithms to identify data issues, using natural language processing to validate qualitative data, and establishing a robust governance framework for AI-powered systems. The course is a strategic investment for organizations seeking to enhance the credibility and efficiency of their M&E efforts, transforming their data quality assurance from a time-consuming, reactive task into a continuous, intelligent, and highly effective process.

Duration: 10 days

Target Audience:

• M&E Specialists
• Data Analysts
• Program and Project Managers
• Data Scientists
• Researchers
• Quality Assurance Officers
• Grant Managers
• Consultants
• Government Officials
• Public Health Professionals

Objectives:

• Understand the core principles of AI-based data quality assurance.
• Master the process of using AI to automate data validation and cleaning.
• Learn how to use machine learning to detect anomalies and errors.
• Develop a framework for integrating AI tools into existing M&E systems.
• Ensure ethical considerations and robust governance for AI-powered solutions.
• Overcome common challenges in implementing AI for data quality.
• Enhance the speed, accuracy, and reliability of data collection.
• Communicate the value of AI-driven data quality to stakeholders.
• Build a culture of proactive data management.
• Apply AI techniques to a wide range of data types and contexts.

Course Modules:

Module 1: Foundations of Data Quality in M&E

• The importance of data quality in M&E decision-making
• The five dimensions of data quality (accuracy, completeness, consistency, etc.)
• The limitations of manual data quality checks
• The business case for automating data quality assurance
• A review of common data quality challenges in M&E

Module 2: Introduction to AI for Data Quality Assurance

• Understanding the role of AI and machine learning (ML)
• Key AI concepts: supervised vs. unsupervised learning
• The difference between rule-based and AI-based validation
• An overview of the AI-powered data quality workflow
• Case studies of AI in data quality assurance

Module 3: Data Profiling with AI

• Using AI to automate the process of data profiling
• Identifying data types, formats, and value distributions
• Detecting anomalies and outliers in large datasets
• The role of unsupervised learning in anomaly detection
• Practical labs on using AI tools for data profiling

Module 4: Automated Data Validation

• Using machine learning to build custom validation rules
• The importance of a "golden record" for validation
• Automating cross-dataset consistency checks
• The role of AI in validating complex business logic
• Practical labs on building an automated validation pipeline

Module 5: AI for Data Cleaning and Standardization

• Using AI to identify and correct data entry errors
• The role of natural language processing (NLP) in cleaning text data
• Automating the process of data standardization (e.g., addresses, names)
• The importance of a clear data cleaning strategy
• Practical labs on using AI for data cleaning

Module 6: Anomaly Detection for M&E Data

• Understanding different types of anomalies (e.g., point, contextual, collective)
• Using machine learning to detect fraudulent or suspicious data
• Building an early warning system for data quality issues
• The importance of a feedback loop for model improvement
• Practical labs on building an anomaly detection model

Module 7: AI and Qualitative Data

• The challenges of ensuring quality in qualitative data
• Using NLP to analyze and validate open-ended text responses
• The role of sentiment analysis in M&E
• Automating the process of thematic coding
• Ethical considerations for using AI on qualitative data

Module 8: Integrating AI Tools into M&E Systems

• The importance of a seamless integration with data collection platforms
• The role of APIs and data connectors
• Building a real-time data quality monitoring dashboard
• The importance of a phased implementation strategy
• Case studies of successful integrations

Module 9: Ethical AI and Governance

• Addressing the risks of algorithmic bias in data quality
• The importance of model transparency and explainability
• Establishing a clear governance framework for AI use
• Ensuring compliance with data protection regulations
• The role of human oversight in all stages of the process

Module 10: AI-Powered Reporting and Communication

• Automating the generation of data quality reports
• Using AI to create clear and actionable dashboards
• The importance of communicating data quality metrics to stakeholders
• The role of a "data quality scorecard"
• Building a culture of data quality accountability

Module 11: AI-Based Data Quality in Practice: A Case Study

• A deep dive into a real-world AI data quality project
• Analyzing the model design and its performance
• Identifying the challenges and successes
• Group discussion on lessons learned and best practices
• Applying the case study concepts to personal work

Module 12: Advanced AI Techniques

• An introduction to generative adversarial networks (GANs) for synthetic data
• The potential of deep learning for complex data quality issues
• The use of reinforcement learning in data cleaning
• The role of computer vision for image-based data quality
• The future of AI in data quality assurance

Module 13: Building Your AI-Ready M&E Team

• Fostering a culture of data literacy and AI curiosity
• The importance of cross-functional collaboration
• Identifying the skills and roles needed for an AI-powered team
• The role of a "learning agenda"
• The importance of a supportive leadership

Module 14: Practical Application and Simulation

• A hands-on simulation of an AI data quality project
• Participants work in teams to build a model
• Troubleshooting common challenges in real-time
• Developing a mini-action plan for data quality improvement
• Peer review and feedback sessions on model design

Module 15: Measuring the ROI of AI in Data Quality

• The importance of a clear and measurable ROI
• The role of AI in reducing costs and improving efficiency
• The impact of high-quality data on program outcomes
• Building a business case for AI adoption
• The long-term implications for the organization

Module 16: Building Your AI Data Quality Roadmap

• Creating a customized plan for a new AI project
• Identifying key stakeholders and securing buy-in
• Developing a phased implementation strategy
• Measuring the ROI and business impact of the investment
• Final Q&A and course wrap-up

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.