The modern data stack has made significant improvements in the quality and performance of analytics by providing automated data pipelines, data warehouses, transformation tools, and business intelligence platforms. A centralized and governed data layer is crucial for resolving challenges such as siloed reporting, conflicting findings, and sensitive data exposure. The same principles can be applied to modern data science architectures to address issues like lack of data governance, underdeveloped DataOps infrastructure, and regulatory compliance. By focusing on basic infrastructure and productionizing models, data scientists can improve their return on investment and bring machine learning projects into production using the same modern data stack used for business intelligence. This involves leveraging structured data in a data warehouse, utilizing governed data lakes, and applying analytics data storage technologies that support both relational and unstructured data.