Businesses are rapidly modernizing their customer data infrastructure to make faster, more informed decisions and integrate automated data intelligence into customer-facing applications. This modernization involves a progression from basic data structures, termed as "walk," to more sophisticated systems like "jog" and "run." Initially, businesses often focus on market fit and customer acquisition, leading to fragmented data systems with multiple digital properties generating unique data sets. The "walk" approach attempts to manage these with custom pipelines, but as data complexity increases, this becomes unmanageable. Transitioning to the "jog" stage involves creating a centralized data supply chain, where a Collection layer centralizes data management, reducing technical debt and streamlining operations. However, this can limit data model flexibility and efficiency. The evolution continues with a blending of responsibilities between the Collection and Storage layers, supported by tools like reverse ETL, allowing for more flexible data modeling and enrichment. Each stage of infrastructure development—supported by tools like Statsig—depends on a business's specific needs and resources, with options for integration at every stage.