Today, knowledge holds significant importance and resides at the highest level of human abstraction and understanding; it is a part of cognitive elements that require the synthesis of massive data. Nevertheless, knowledge is more valuable when it is interacted with in dynamic knowledge bases. Intelligence is based on knowledge, and these tacit and explicit knowledge bases provide a favorable environment for it. Several research projects aim to provide a reliable representation of knowledge to understand intelligence better. Nevertheless, these representations have difficulty in representing tacit knowledge; they have not been able to represent dynamic knowledge that changes over time adequately. This article adopts the positivist research posture and the Delphi method following deductive logic. We explore the main intelligence and knowledge representation models through a narrative literature review. Based on the opinions and consensus of experts, we deduce the effects of effective knowledge representation on intelligence development. Subsequently, we lay the foundation for an initial vision of the INTDATA (intelligent data) intelligence model inspired by the Unified Modeling Language (UML). We introduce the INTDATA model along with its two constituent parts. We also highlight its similarities with its predecessors, its potential applications, and its unique strengths that set it apart from other models.