How to Enhance Your Business Analytics with Data Virtualization for AI/ML

Data virtualization provides a unified virtual layer for accessing and querying diverse data sources without physically moving or duplicating information, offering an alternative to traditional ETL-based integration. It supports AI and machine learning by reducing data silos and latency, combining real-time data from disparate systems, simplifying preparation, and providing the diverse, current datasets needed for training and inference. Applications include supply-chain optimization, customer segmentation, fraud detection, predictive maintenance, and retail inventory management, where integrated data can improve forecasting, personalization, anomaly detection, and operational decisions. Effective implementation requires assessing existing data systems, selecting an appropriate platform, maintaining governance and security controls, optimizing performance, and training teams. CData Connect AI is presented as a platform that provides governed, real-time access to SaaS, cloud, and on-premises data without replication.

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