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

• Understand the principles and importance of data quality in business and technical environments.
• Differentiate between data quality management, quality assurance, and quality control.
• Design and implement data quality frameworks and governance models.
• Apply QA and QC techniques to data processes and systems.
• Use tools and metrics to monitor and improve data quality.
• Align data quality initiatives with organizational strategy and compliance requirements.

This course is ideal for:

• Data managers and analysts
• Quality assurance professionals
• IT and database administrators
• Business intelligence and reporting specialists
• Compliance officers
• Project managers involved in data-centric projects
• Anyone responsible for data governance and quality initiatives

The course uses a blend of interactive lectures, group discussions, case studies, and hands-on exercises. Participants will engage in real-world scenarios, data audits, and quality assessments. The training encourages collaborative learning and practical application of concepts through workshops and simulations.

Day 1 – Fundamentals of Data Quality & Governance
The course begins with an overview of data quality concepts, dimensions (accuracy, completeness, consistency, timeliness, etc.), and the role of data governance. Participants will explore the business impact of poor data quality and the foundational elements of a data quality management system.

Day 2 – Designing a Data Quality Management System (DQMS)
Day two focuses on the architecture and components of a DQMS. Topics include data quality policies, roles and responsibilities, data lifecycle management, and integration with enterprise systems. Participants will learn how to assess current data quality maturity and plan for improvements.

Day 3 – Quality Assurance in Data Processes
This day covers QA principles applied to data environments. Participants will learn how to build QA frameworks, conduct process audits, and implement preventive measures. Emphasis is placed on documentation, validation protocols, and continuous improvement strategies.

Day 4 – Quality Control Techniques & Tools
Day four introduces QC methods for detecting and correcting data issues. Topics include data profiling, cleansing, validation rules, and exception handling. Participants will explore tools and technologies for automated QC and perform hands-on exercises using sample datasets.

Day 5 – Monitoring, Reporting & Continuous Improvement
The final day focuses on establishing data quality metrics, dashboards, and reporting mechanisms. Participants will learn how to monitor data quality over time, communicate findings to stakeholders, and drive continuous improvement. The course concludes with a capstone workshop where participants design a data quality improvement plan for their organization

A CDGA attendance certificate will be issued to all attendees completing a minimum of 75% of the total course duration