Data Quality Framework and Methodology for Data Managers

Choosing the suitable DQ Framework is crucial for your organisation’s data quality goals.
It should integrate various techniques and tools, support different data types, and be flexible and straightforward, with clear goals for each stage.
Essential points discussed in this webinar are:
• Developing a data quality framework is important
• PDCA cycle involves planning, executing, checking, and acting for continuous improvement
• Pareto principle is used to prioritise data quality issues
• Root cause analysis is conducted to identify underlying reasons for data quality issues
• Juran, Deming, and ISO offer different approaches to achieving data quality
• Evaluating the cost of data quality issues is crucial
• Developing taxonomies for data value realisation and cost reduction is important
• Planning for data quality involves resolving data quality issues
• Various frameworks can be utilised based on organisational needs and objectives
Meetup Group: African Data Management Community Forum 2023-07-06
KZbin Chapter
00:00:00 Start Webinar
00:01:00 Introduction to Data Quality Framework (PDCA)
00:02:00 Data Quality Planning and Frameworks
00:03:00 Data Analysis and Data Quality
00:04:00 Different Frameworks for Data Quality Assessment and Improvement
00:05:00 Evaluating Data Quality Frameworks
00:06:00 Planning for Data Quality
00:07:00 The Importance of Quality Control in Data Quality
00:08:00 Quality Improvement in Healthcare Data
00:09:00 Importance of Frameworks in Quality Management
00:10:00 Quality Management Frameworks and Data Certifications in Saudi Arabia
00:11:00 Integrating Quality into the Source System to Avoid Bad Data
00:12:00 Data Quality Assessment and Improvement Process
00:13:00 Importance of Root Cause Analysis and Improvement in Data Quality
00:14:00 The Juran Trilogy and Data Quality Targets
00:15:00 Notes on Qualitative and Quantitative Metrics in Data Analysis
00:16:00 Exploring Data Quality Issues and Quality Frameworks
00:17:00 Clarification on Frameworks and Metrics for Data Analysis
00:18:00 The Impact of Distributed Systems on Data Quality
00:19:00 Data Quality Assessment and Accuracy
00:20:00 Great Expectations: A Framework for Ensuring Data Quality