Why Graph Implementations Fail (Early Signs & Successes)

Organizations continue to invest heavily in efforts to unify institutional knowledge and data from multiple sources. This typically involves copying data between systems or consolidating it into a new physical location such as data lakes, warehouses, and data marts. With few exceptions, these efforts have yet to deliver the connections and context required to address complex organizational questions and deliver usable insights. Moreover, the rise of Generative AI and Large Language Models (LLMs) continue to increase the need to ground AI models in factual, enterprise context. The result has been a renewed interest in standard knowledge management (KM) and information management (IM) principles.