In the latest Analytics Engineering Roundup, dbt Labs CEO Tristan Handy emphasizes the importance of building systems that inherently prevent data quality issues rather than relying on detection and alerts after problems occur. Data quality is a multifaceted challenge involving technology, processes, and human error, with a significant portion of issues arising from changes made by people within an organization. Handy argues for a proactive approach to data quality, advocating for better change management processes that catch errors before they reach production. This includes implementing robust workflows with version control, staging environments, and automated checks to identify and correct potential issues early in the development process. The article highlights the importance of minimizing human error by understanding data dependencies and suggests that effective change management can lead to more robust data pipelines without sacrificing development speed.