Data Quality Framework & Methodologies - Data Professionals

African Data Management Community Forum 2023-07-20
Defining critical data elements is essential for achieving quality levels. Master data systems should only include necessary details. Currency and minimum/maximum values must be monitored. Data quality rules should be defined to assess data. Root cause analysis is vital for identifying system or process issues. Waterfall is recommended for initial development, with the option to transition to agile for monitoring. Association entities help simplify data modelling.
Chapters
00:00:00 Start Webinar
00:05:12 The Relevance of Shoe Size in a Master Data System
00:09:45 Challenges of Maintaining Accurate Shoe Size in Master Data System
00:13:00 Understanding the Importance of Critical Data Elements in Data Quality
00:16:31 Approaches to Defining Data Elements and Dimensions
00:19:02 Data Quality Expectations and Minimum/Maximum Values
00:23:01 Notes on Reasonableness and Data Quality
00:26:18 Importance of Setting Data Quality Expectations and the Role of a Data Architect
00:29:34 Ensuring Data Quality in the Application
00:31:41 Data Movement and Analysis
00:36:22 Importance of Understanding Root Causes and Implementing Improvement Plans
00:39:45 Notes on Data Quality Tool Architecture
00:43:07 Organizing and documenting data for analysis and data quality
00:46:39 Development Approach for Data Quality
00:51:06 The Importance of Effective Planning and Minimal Changes in Improvement Cycles
00:53:04 Importance of Data Quality in Agile Development
00:56:40 Clearing up confusion on entity associations