Data Virtualization in Modern AI and Analytics Architectures
This gap is filled by data virtualization, which enables businesses to leverage their current data sources for AI and analytics projects with efficiency and flexibility
Data virtualization removes the need for physical duplication or migration, which speeds up the process of combining data from several sources
This minimizes the possibility of errors or data loss while also drastically cutting down on the time and cost of data integration
Data virtualization acts as a central hub to integrate real-time data streams from equipment logs and sensor data and eliminate data silos and fragmentation
Data virtualization integrates real-time and historical data from multiple sources to show an organization’s operational data environment
This holistic strategy helps firms improve procedures, make data-driven decisions, and gain a competitive edge
This vast data set is being used by foundation models due to the rise of generative AI chatbots