Data Quality Management for Data-Driven Organizations
About This Course
This course offers a comprehensive approach to improving data quality within research and organizational processes by applying the Total Data Quality Management (TDQM) framework. Learners will explore how to drive consistency and accuracy in data analysis and develop strategies for measuring and improving data quality across various stages of a project or research study.
Through hands-on learning, participants will gain the ability to evaluate and synthesize findings from diverse research studies, applying best practices and techniques that lead to higher organizational data quality. The course emphasizes practical tools and strategies, equipping learners to address challenges related to data quality and to create measurable improvements in their organizations' data management systems.
The course is highly relevant for industries where accurate and reliable data is essential for decision-making and organizational efficiency. This includes sectors such as healthcare, finance, research, manufacturing, and technology. Professionals will enhance their skills in managing and improving data processes, ultimately making them valuable assets in roles that require high-quality data handling and analysis.
This course is designed for intermediate learners who have a foundational understanding of data management or research methodologies and are looking to deepen their expertise in data quality management.
What You'll Learn
LO1: Drive consistency and accuracy in research data analysis by applying total data quality framework,
LO2: Synthesize findings from diverse research studies to measure total quality.
LO3: Evaluate research results using design strategies, referencing current professional practices in data analysis to improve organizational data quality.
LO4: Identify opportunities for data analysis synergies across research studies using cross-organisational and sector-wide knowledge.
Course Outlines:
LU1: The Total Data Quality Framework
T1: What is data quality?
T2: Why data quality is important?
T3: Key data quality dimensions for total data quality framework
T4: Potential threats to data quality along each of these dimensions.
LU2: Measuring Total Data Quality
T1: Data quality rules
T2: Data quality process
T3: Metrics for evaluating data quality at each stage.
T4: Create a quality concept map that tracks relevant aspects of operational processes.
T5: Evaluate relative trade-offs between quality aspects, relative costs, and practical constraints imposed by a particular project or study.
T6: Metrics that can be computed to measure quality processes
LU3: Design Strategies for Maximizing Total Data Quality
T1: Data quality techniques and tools
T2: Data quality roles
T3: Data quality governance
T4: Data quality best practices
T5: Data quality improvement and assurance
T6: Develop solutions to hypothetical design problems
LU4: Synergies in Cross-Organisational Research Data Analysis
T1: Overview of cross-sector research landscapes
T2: Sources of related research studies and open data
T3: Methods for identifying alignment opportunities across studies
T4: Case examples of research synergy and integration
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience
Target age group: 21-65 years old