tl;dr:Michael emphasizes that "data quality is paramount for accurate insights," highlighting the challenge of ensuring data reliability. Michael introduces Lyft’s in-house data quality platform, Verity, which has an exhaustive flow that starts with the following steps: (1) Data Profiling: Incoming data is scrutinized for its structure, schema, and content. This allows it to identify potential anomalies and inconsistencies. (2) Customizable Rules Engine: Enables data experts to define specific data quality rules tailored to their unique needs. These rules encompass everything from data format validations to more intricate domain-specific checks. (3) Automated Quality Checks: Once the rules are set, they are applied to incoming data streams, scanning each data point, seeking discrepancies.