Nigel Turner on Data Mesh and Data Fabric related to Data Quality
Will Data Mesh and Data Fabric make Data Quality Management easier or harder to get right? As a foundational data discipline how should Data Quality principles and practices evolve and adapt to meet the needs of these new trends? Nigel Turner gave insights about these questions during the Data Warehousing & BI Summit in April this year, where he presented his session 'Data Mesh & Fabric: The Data Quality Dependency'.
Nigel Turner is Principal Consultant at Global Data Strategy. In this interview with BI-Platform he elaborates on the concepts and practices of Data Mesh and Data Fabric. He first extensively describes how Data Mesh and Data Fabric differ from 'traditional' architectures. After that he discusses the relationship with data quality, and speaks about topics like the role and impact of self service, data governance and stewardship.
Finally he points at the implications of the advancement of AI and Machine Learning for data quality in a Data Fabric.
Chapters:
00:00 Introduction
00:17 1. Can you briefly explain the concept of Data Mesh and Data Fabric and how they differ from traditional data architectures?
06:19 2. How do these architectures address data quality issues and ensure high-quality data?
08:50 3. Next to get-clean, do these architectures also address the need to start and stay clean?
10:11 4. What is the role and impact of self service on data quality within these two architectures?
12:25 5. How do you see the role of data governance and stewardship changing with the adoption of a Data Mesh approach?
14:21 6. What implications do the advancements of AI and Machine Learning have for data quality in a Data Fabric?
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