The era of artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) has brought about significant changes in data handling, with a focus on consolidating data silos into centralized data lakes to facilitate efficient governance and access control. Data virtualization enables companies to retrieve and analyze data from diverse sources without physically copying it to a centralized location, providing agility in data models. Real-time data processing and analytics have tangible impacts on business outcomes, particularly in high-demand industries such as financial services, personalized recommendations, and geospatially targeted marketing. However, GenAI also poses challenges like trust deficit, biased data, and intellectual property concerns, which can be mitigated by training models effectively on unbiased and diverse datasets. As companies integrate these technologies, they must also consider privacy regulations, data sovereignty restrictions, and the importance of real-time access and agility in their data-driven decision-making processes.