Building knowledge graphs is gaining attention, especially among startups looking to leverage large language models (LLMs) and generative AI. The author emphasizes the importance of understanding the purpose behind creating a knowledge graph. Casual endorsements from influencers won’t suffice; businesses need to grasp the foundational value these graphs provide. A knowledge graph functions as a database, organizing information in a retrievable format. Unlike traditional relational databases, where joins and NULL values complicate data representation, knowledge graphs simplify relationships, allowing for more flexible and scalable data management. The article outlines the structural advantages of knowledge graphs over relational databases. These graphs can capture complex relationships and metadata, making them better suited for interconnected data. When the number of tables in a relational database exceeds about thirty, managing that complexity becomes challenging. Knowledge graphs thrive in such scenarios, efficiently handling vast amounts of data and relationships without the need for intermediate values. The author also highlights the importance of inferencing in knowledge graphs, which allows for dynamic relationships that traditional data models often overlook. On the business side, knowledge graphs enable organizations to define and manage relationships between various classes of data more effectively. They facilitate a shift in how applications are built, allowing process information and business logic to reside within the graph itself. This leads to simpler code and easier updates when conditions change. The author points out that while LLMs initially seemed revolutionary, they revealed limitations in handling complex data structures. Knowledge graphs offer a path to greater transparency and adaptability in managing information, positioning businesses to respond more effectively to evolving needs.