Transitioning to Agentic Data Testing
In this video, CCO Subu Desaraju discusses the evolving role of data quality in today’s data-driven organizations. He shares insights on why traditional QA approaches are no longer sufficient for modern data pipelines, the growing risks of poor data quality, and how teams must adopt a proactive, shift-left approach to ensure reliable and trustworthy data from the start.
We explore common data quality challenges, the risks of late-stage validation, and why building data quality checks early in the pipeline is critical for reliable analytics and decision-making. The video also touches on how automation, shift-left data testing, and emerging technologies like AI are shaping the future of data quality assurance.
Key Topics Covered:
1. Why data quality issues persist in modern data systems
2. Differences between application QA and data testing
3. The importance of shift-left data testing
4. Financial and business risks of poor data quality
5. How automation and AI can support scalable data testing
This episode shares practical, experience-driven insights into agentic data testing and its impact on modern data engineering.
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