In the pursuit of an effective analytics infrastructure, this article outlines a comprehensive framework that breaks down the "value chain" of data, from its origin to its integration into decision-making narratives. It emphasizes the importance of carefully selecting tools and processes for each stage of the data lifecycle, including specification, instrumentation, collection, integration, data warehousing, transformation, quality assurance, data discovery, and analysis. The author, drawing from extensive experience with companies like Autodesk, Lyft, and Phantom Auto, recommends tools like Segment for event tracking and integration, while also discussing alternatives such as GCP Pub/Sub and AWS Managed Kafka for cost-effective data collection. The piece underscores the necessity of a scalable and adaptable data infrastructure, highlighting tools like BigQuery and Snowflake for data warehousing and transformation, and stresses the value of making informed decisions about what data to track, rather than merely focusing on technology choices.