THE DIFFERENT TYPES OF KNOWLEDGE

To effectively manage knowledge, it‘s crucial to recognize the various forms it can take. This includes the ability to differentiate between types of knowledge, a fundamental step towards effective knowledge management.

For businesses, knowledge is a highly prized asset. But how do you determine whether your organization has an optimal amount of it? There are numerous metrics available, but one commonly used approach is to distinguish between explicit and tacit knowledge. The former refers to information learned through training, while the latter is knowledge gained organically over time.

Nonaka introduced a concept in the 1990s that remains essential to our discipline today: knowledge is acquired through the interaction and relationships between two knowledge types.

Explicit knowledge (KNOW)

Formalized and codified knowledge, also known as explicit knowledge, is efficiently identified, stored, and retrieved. Knowledge management systems (KMS) excel at managing this type of knowledge, making document and text storage, retrieval, and modification seamless.

From a management standpoint, the main challenge of explicit knowledge aligns with managing information. The goal is to ensure people can access necessary knowledge, prioritize important information for storage, and periodically review, update, or dispose of irrelevant knowledge.

„Implicit knowledge is personal, contextual, and therefore difficult to formalize and communicate. On the other hand, explicit or „codified“ knowledge refers to knowledge that is formally transferable, systematic language.“

Ikujiro Nonaka

While experience-based know-how may be favored over explicit knowledge by many, it‘s important to recognize that the latter plays a crucial role in industries prone to technological shifts. Explicit knowledge may lack in-depth insights, but it offers straightforward guidance. However, companies that rely solely on explicit knowledge may face challenges in adapting to changing trends.

In today‘s digital age, knowledge management initiatives that solely focus on explicit information are not enough. Understanding the deeper aspects of implementation knowledge is necessary. As technology advances, the nature of knowledge changes, and it‘s crucial to keep up.

The integration of artificial intelligence and machine learning in decision-making processes is underway. However, it‘s essential to note that these technologies aren‘t ideal yet. Certain AI systems prioritize data exclusively while overlooking other key factors such as relevance and credibility.

Tacit knowledge (CAN)

Know-how, a type of knowledge that has existed for decades, is often difficult to define. It encompasses intuitive skills that can be gained through experience or observation. This observational process, known as implementation, involves watching someone else perform a task before attempting it yourself.

While the traditional view of knowledge may prioritize formal, explicit information, Nonaka‘s research reveals that this perspective falls short in many types of situa-