Graph Databases and AI for Business Applications

Modern business challenges involve deeply interconnected data that traditional relational databases struggle to process efficiently. This text-based course guides you through the strategic combination of graph databases and artificial intelligence to solve complex organizational problems. You will explore the core concepts behind connected data models, see how graph structures power intelligent systems, and examine practical patterns used in fraud detection, recommendation engines, and enterprise knowledge retrieval. What you'll learn: - Understand foundational graph database concepts, including nodes, relationships, and property graphs - Learn how knowledge graphs enhance modern artificial intelligence and retrieval-augmented generation architectures - Analyze real-world business scenarios where connected data delivers superior performance and insights - Explore graph traversal algorithms used for pattern detection, risk analysis, and customer journey mapping - Apply evaluation criteria to identify high-impact graph and AI opportunities within your organization The course begins with clear definitions and core terminology before progressing through architectural concepts and detailed case-driven analysis. It is designed for beginners, business analysts, data professionals, and decision-makers with no prior graph database experience required. Start reading to unlock the value of connected data in modern intelligent systems.