Organizations face significant challenges in managing data access and trust, which intensify with growth and technological advancement, particularly in the age of AI. The evolution of data warehousing to data lakes has brought about governance challenges, such as data quality, error handling, and data discovery, leading to issues like "data swamps." These challenges affect business decisions, operational costs, and data trust. A shift-left approach, emphasizing early data quality controls, is proposed to address these issues, with tools like Tableflow facilitating this by integrating operational and analytical systems and automating data management tasks. This approach prioritizes metadata management and real-time data processing to enhance data reliability and discoverability. By fostering a data-driven culture and implementing robust governance frameworks, organizations can improve the efficiency and effectiveness of their data lakes, ensuring data is a valuable asset rather than a byproduct.