The Data Governance Podcast The difference between automated and manual data quality - with Maarten Masschelein
7 snips
May 21, 2024 Maarten Masschelein, The CEO of Soda, discusses the differences between manual and automated data quality processes. Topics include the shift towards software engineering practices, empowering different user types with data quality initiatives, the future of data quality with Gen AI automation, and the importance of balancing automation and human input in maintaining data quality.
AI Snips
Chapters
Transcript
Episode notes
Manual Data Quality Explained
- Manual data quality involves collecting end user requirements and enforcing them through producers to ensure data fits its purpose.
- It usually consists of business checks prioritized to meet operational needs.
Automated Data Quality Benefits
- Automated data quality commonly runs standard or inferred checks on data using tools and automation techniques.
- Its goal is to make data quality processes more efficient and scalable, especially with human oversight.
Use No-Code Checks for Inclusion
- Use no-code checks to enable domain experts to express business rules without advanced SQL skills.
- Involve business users to steer data quality efforts and make them more relevant and effective.