The right data management strategy can help democratize access to analytics across an entire team, without the need for a data scientist or data engineer as an intermediary. In 2022, finding specialized talent in data science and data analytics is increasingly difficult due to high demand and the Great Resignation, but complex data management challenges can be alleviated with technology. Enterprise data management is complicated due to multiple formats across various sources, with data warehouses following a schema-on-write approach that limits BI users' ability to interact with data in different ways. Data lakes, on the other hand, store data in its source format and encourage a schema-on-read process model, bypassing the ETL process and enabling faster time to insights. Choosing self-service data analytics platforms can empower business users to access and analyze data on demand without requiring data transformation or movement. These platforms sit on top of cloud-based data repositories, delivering key features that help organizations take advantage of a data lake architecture and activate low-cost cloud object storage. Consolidating certain data into a cloud data platform makes sense for query performance and faster time to insights, especially in distributed architectures like data meshes. Solutions like ChaosSearch can be used to virtually prep data for popular BI tools, democratizing access to clean, normalized, and trustworthy data. Focusing on a sound enterprise data management strategy can help eliminate complexity, freeing up resources for strategic initiatives and empowering employees to use self-service BI or machine learning tools.