Scaling data products relies heavily on a solid foundational setup rather than advanced models. Weak data foundations lead to more failures than poor algorithms. To improve this, companies should treat data pipelines like products, assigning ownership and establishing service level objectives (SLOs). For instance, Joybird revamped its data stack using RudderStack, reducing engineering time for integrations by 93%. They implemented version control for transformations and enforced tracking plans, creating a consistent and observable pipeline that can adapt quickly. Data ownership is critical. Clear accountability allows for reliable access to clean and complete data. Shippit unified its fragmented customer data with RudderStack, solving long-standing attribution issues. Their head of data emphasized the importance of having a single source of truth, enabling all teams to access the same information without discrepancies. Standardizing data architecture is also vital. Companies like Kajabi switched to RudderStack to gain consistent schemas and easier debugging capabilities. By establishing a shared foundation, teams can work more efficiently. Furthermore, designing data pipelines with a focus on end-use—routing high-quality streams to various destinations—allows for quick adjustments based on departmental needs. Finally, maintaining human oversight through observability ensures that teams trust their data processes.