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Data Quality Assurance Techniques Training Course

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Upcoming Training Schedules 14 locations

Location	Duration	Next Start Date	Dates Available	Action
Nairobi, Kenya	5 days	Jul 13, 2026	104 dates
Accra, Ghana	5 days	Jul 20, 2026	31 dates
Addis Ababa, Ethiopia	5 days	Jul 27, 2026	31 dates
Cape Town, South Africa	5 days	Jul 27, 2026	52 dates
Dar es Salaam, Tanzania	5 days	Jul 27, 2026	26 dates
Dubai, UAE	5 days	Jul 13, 2026	52 dates
Istanbul, Turkey	5 days	Jul 27, 2026	16 dates
Kampala, Uganda	5 days	Aug 10, 2026	31 dates
Kigali, Rwanda	5 days	Aug 3, 2026	52 dates
Kuala Lumpur, Malaysia	5 days	Aug 10, 2026	31 dates
Mombasa, Kenya	5 days	Aug 3, 2026	52 dates
Pretoria, South Africa	5 days	Jul 13, 2026	52 dates
Singapore	5 days	Jul 20, 2026	31 dates
Zanzibar, Tanzania	5 days	Sep 14, 2026	16 dates

Data Quality Assurance Techniques Training Course

Course Overview

The Data Quality Assurance Techniques Training Course is designed to equip participants with practical knowledge and technical skills in ensuring the accuracy, completeness, consistency, reliability, validity, and timeliness of data throughout the data lifecycle. In today's data-driven environment, organizations depend on high-quality data to support evidence-based decision-making, strategic planning, monitoring and evaluation, research, policy development, and performance management. Poor data quality can lead to inaccurate reporting, ineffective interventions, and inefficient resource utilization. This course provides comprehensive knowledge on data quality management frameworks, quality assurance methodologies, data governance principles, and data verification techniques.

The course introduces participants to internationally recognized data quality standards and best practices for managing and improving data quality in development programs, public health systems, humanitarian operations, business intelligence initiatives, and research projects. Participants will learn practical approaches to data collection quality control, data cleaning procedures, validation rules, data audits, metadata management, and continuous data quality improvement systems.

Through practical exercises and case studies, participants will gain hands-on experience in designing data quality assurance frameworks, developing data quality indicators, conducting data verification and validation exercises, implementing quality control mechanisms, and producing reliable datasets for reporting and decision-making. The course emphasizes the integration of technology, digital tools, and automated quality checks in strengthening organizational data systems.

The training combines expert presentations, practical demonstrations, web-based tutorials, collaborative group work, and real-world case studies to ensure participants acquire practical competencies in data quality management. Upon successful completion of this course, participants will possess the skills necessary to establish and maintain robust data quality assurance systems that support organizational performance, accountability, and sustainable development outcomes.

Course Objectives

Upon successful completion of this course, participants will be able to:

1. Understand the principles and dimensions of data quality.

2. Design and implement data quality assurance frameworks.

3. Apply data verification and validation techniques.

4. Conduct data quality assessments and audits.

5. Develop data quality indicators and monitoring systems.

6. Implement data cleaning and data management procedures.

7. Establish quality control measures in data collection processes.

8. Apply data governance and data stewardship principles.

9. Utilize digital tools and technologies for data quality management.

10. Strengthen organizational decision-making through high-quality data.

Organizational Benefits

Organizations participating in this training will be able to:

1. Improve data accuracy and reliability across programs and projects.

2. Strengthen evidence-based planning and decision-making processes.

3. Enhance accountability and reporting credibility.

4. Reduce errors, inconsistencies, and data duplication.

5. Improve monitoring and evaluation systems performance.

6. Strengthen compliance with data management standards and policies.

7. Increase operational efficiency and resource optimization.

8. Enhance research quality and information integrity.

9. Improve stakeholder confidence in organizational reporting systems.

10. Establish sustainable data quality management frameworks.

Target Participants

This course is suitable for:

· Monitoring and Evaluation Officers

· Data Analysts and Data Managers

· Research Officers and Researchers

· Project Managers and Coordinators

· Program Managers

· Information Management Specialists

· Public Health Professionals

· Government Planning Officers

· Statistics and Survey Professionals

· Database Administrators

· Business Intelligence Specialists

· Development Practitioners and Consultants

Course Outline

Module 1: Foundations of Data Quality Assurance

· Introduction to data quality concepts and principles

· Dimensions of data quality and quality standards

· Data lifecycle management concepts

· Importance of data quality in organizational performance

· Data quality challenges and risk management

· General Case Study: Data quality assessment in a national health information system

Module 2: Data Collection Quality Assurance Techniques

· Data collection planning and protocol development

· Quality control procedures during data collection

· Questionnaire design and validation techniques

· Enumerator training and supervision approaches

· Real-time monitoring and field data verification methods

· General Case Study: Quality assurance in a household survey project

Module 3: Data Verification and Validation Methods

· Data verification principles and procedures

· Validation rules and consistency checks

· Cross-checking and triangulation techniques

· Handling missing data and outlier detection

· Automated data validation systems and workflows

· General Case Study: Data validation for monitoring and evaluation reporting systems

Module 4: Data Cleaning and Data Management Systems

· Data cleaning methodologies and procedures

· Data coding and standardization techniques

· Data transformation and integration methods

· Metadata development and management

· Database quality management principles

· General Case Study: Cleaning and management of large-scale research datasets

Module 5: Data Quality Assessment and Auditing

· Data quality assessment frameworks and methodologies

· Data quality indicators and performance metrics

· Conducting routine data quality audits (RDQA)

· Root cause analysis and corrective action planning

· Reporting and communicating data quality findings

· General Case Study: Routine data quality audit in a development project

Module 6: Data Governance and Continuous Quality Improvement

· Data governance frameworks and policies

· Roles and responsibilities in data stewardship

· Data security, confidentiality, and ethical considerations

· Continuous quality improvement approaches

· Digital technologies for data quality management

· General Case Study: Organizational implementation of enterprise data quality management systems

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.

2. Language Proficiency: Participants should have a good command of the English language.

3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9. Equipment: Tablets and laptops can be provided to participants at an additional cost.

10. Post-Training Support: We offer one year of free consultation and coaching after the course.

11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14. Website: Visit our website at www.fdc-k.org for more information.

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training@fdc-k.org • +254 712 260 031 • Nairobi, Kenya