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Learn how to implement comprehensive data quality testing using open-source tools to prevent unreliable data from undermining AI initiatives in this conference talk. Discover why data teams struggle with customer distrust, ignored data quality issues, and constant firefighting instead of generating valuable insights. Explore a practical solution that involves implementing extensive data quality tests across all tables and columns, running them in production, and integrating them into development workflows to achieve data quality scores and drive source system improvements. Understand the challenges data teams face when working with hundreds or thousands of tables while lacking sufficient time for comprehensive test coverage. Get introduced to DataKitchen's open-source data quality and observability framework designed to automatically generate 80% of necessary data tests with minimal effort, while providing an intuitive interface for collaborating on the remaining 20% of organization-specific tests that require custom attention.
