Data, Information and Knowledge What's the difference?

Effective Knowledge Management is crucial in today’s complex world, however, the distinction between terms like data, information, knowledge, explicit, tacit, and implicit knowledge can often be confusing. It is important to have a clear understanding of the differences.

Introduction

The terms data, information, knowledge, explicit, tacit, and implicit knowledge are used carelessly in everyday language, and people have a very different understanding of their meanings. Their meaning can also vary with context.

Even amongst the KM profession, there has yet to be a complete agreement on the meaning of these terms.

In short, their meaning can be confusing, especially to people outside of KM circles.

Nick Milton describes the problem in these two posts:

• The problem with “tacit/explicit”
• Tacit, Explicit and …. what? Different types of knowledge, and the definition minefield

In this explanation, I will define and contrast these concepts as I use them in this blook.

What is the difference between data and information?

Data and information are closely related, but they are different. Data refers to raw facts and figures that have not been processed or organized. It is the raw material from which information is derived.

Conversely, information refers to data that has been processed, organized, and presented meaningfully. Information is often more helpful and valuable than raw data because it has been transformed into a more accessible form for people to understand and use.

What is the difference between information and knowledge?

Knowledge and information are related but distinct concepts.

Information refers to data or facts that are presented or communicated. It can be true or false and can be conveyed through various mediums, such as spoken or written language, images, or numbers.

On the other hand, knowledge refers to an understanding or comprehension of a subject or information. It involves the ability to think about and apply information meaningfully. In other words, knowledge results from learning, processing, and applying information.

For example, if you read a book about a specific topic, you are gaining information about that topic. If you understand and can apply the concepts discussed in the book, you have gained knowledge about the subject.

Three types of knowledge

Knowledge can be classified in three ways:

1. explicit
2. tacit
3. implicit.

It is essential to understand, though, that  Knowledge only exists in the mind, and once codified in some way; for example, written down, it becomes information.

Explicit Knowledge

Explicit knowledge can be explicitly stated, written down, or codified as information. It is knowledge that can be easily expressed and shared with others, either through language or other forms of communication.

Explicit knowledge is typically conscious and intentional, and it is often the result of learning or experience. Examples of explicit knowledge include facts, formulas, theories, and procedures that have the potential to be written down or recorded in some way.

What is the difference between tacit and implicit knowledge?

Tacit knowledge is knowledge that is difficult to express or communicate in words. It is often personal and subjective and is usually acquired through experience and practice. Examples of tacit knowledge include skills, habits, and understanding complex systems or processes.

Implicit knowledge, on the other hand, is knowledge that is not consciously known or understood but is demonstrated through our actions or behavior (e.g., knowing how to ride a bike). Implicit knowledge is often unconscious and automatic and can be difficult to recognize or change.

Simply put, the main difference between tacit and implicit knowledge is that tacit knowledge is difficult to express or communicate, while implicit knowledge is not consciously known or understood.

A common knowledge fallacy

A common mistake in understanding knowledge is assuming that knowledge in our mind is the same as knowledge that has been recorded, such as on paper or digitally. It is important to realize that explicit knowledge is not knowledge that has been recorded but rather knowledge that can be easily recorded.

This is an important distinction, as knowledge only exists within the mind, whether explicit, tacit, or implicit. Once knowledge is recorded or codified in any way, it becomes information.

For example, if you do not speak Arabic, you cannot understand an Arabic encyclopedia (book of knowledge) without knowledge of the Arabic language. Similarly, suppose you are not a physicist. In that case, you cannot understand a research paper on particle physics without some existing knowledge of physics, regardless of the language in which it is written.

In other words, we need knowledge to code knowledge into information and knowledge to decode information into knowledge.

So, another way of looking at things is to say that we have uncodified knowledge (knowledge that exists in the mind) and codified knowledge (information).

The DIKW model

The DIKW (Data, Information, Knowledge, Wisdom) model is a flawed model that proposes data, information, knowledge, and wisdom form a pyramid, with each level building upon the previous one.

Wisdom

Lastly, wisdom is the ability to apply knowledge and experience to make sound judgments and decisions. It is characterized by the ability to think and act with understanding, insight, and discernment.

Wisdom can be seen as a combination of knowledge, experience, and judgment, and it is usually acquired through a variety of formal education and life experiences. It is not necessarily tied to a specific field of study or area of expertise but is a general ability to apply our knowledge and experience practically and effectively.

Wisdom is associated with qualities such as patience, humility, and compassion. These traits can help us see the bigger picture and make decisions in the best interests of all involved. It is also often associated with the ability to adapt and change in the face of new challenges or circumstances.

An alternative perspective on data, information, and knowledge

Another perspective on data, information, and knowledge is through the lens of our senses.

From this stance, the brain processes sensory data from the environment to create information and knowledge.

This process begins with detecting stimuli by specialized sensory receptors, such as photoreceptors in the eyes or auditory hair cells in the ears.

These receptors convert physical stimuli, such as light and sound waves, into neural signals or sensory data that is transmitted to the brain.

This data is unprocessed and unorganized. However, once it reaches the brain, it is processed, organized, and given meaning, transforming it into information.

On reaching the brain, it is first processed in the primary sensory cortex, which is responsible for identifying the basic features of the stimuli, such as color, shape, and movement.

From there, the information is passed to higher-level areas of the brain, such as the visual association cortex, where it is interpreted and integrated with information from other senses and stored knowledge.

For example, when we see an object, the primary visual cortex detects its basic properties, like shape and color.

The visual association area of the brain processes this visual information alongside other sensory information, such as sound, to identify the object as a “ball,” which is integrated with previously stored knowledge to conclude that it is a soccer ball.

This process of interpretation and integration allows the brain to create meaning from sensory data and construct a coherent representation of the world around us.

It also allows us to predict future events based on past experiences and stored knowledge.

In addition, the brain also uses feedback loops to continuously update and refine its representations of the world by receiving new information and comparing it to stored knowledge to see if any revisions are needed.

In summary, the brain’s ability to process sensory data is a complex process that involves the detection of stimuli by specialized receptors, their initial processing in the primary sensory cortex, and their subsequent interpretation and integration with stored knowledge in higher-level areas of the brain, which allows us to create meaning, make predictions and continuously refine our understanding of the world around us.

Written language is not information

From this sensory perspective, written text can be viewed as simply a pattern of squiggles or dots on a surface such as paper or a computer screen.

Light reflects off this pattern, enters the eye, and is encoded and sent to the brain as a data stream.

This data stream is then processed by the brain and turned into information, such as the words and meaning behind the written text.

This means that written language has no inherent meaning until the brain interprets it. If a person has not learned a particular language, such as Arabic or Japanese, the text written in that language will have no meaning to them.

So from this perspective, data, information, and knowledge reside within the brain.

The brain-as-computer metaphor

The argument that data, information, and knowledge reside in the brain is known as the “brain-as-computer” metaphor.

This metaphor (incorrectly) suggests that the brain functions similarly to a computer, with sensory inputs being processed as “data,” transformed into “information” through the brain’s algorithms, and eventually becoming “knowledge” that can be stored and retrieved as needed.

This metaphor explains how the brain can perceive, process, and understand the world around it. It is often used as a basis for understanding cognitive functions such as memory, perception, and decision-making.

So what does this mean for reality?

Reality is the state of things as they exist in the world, independent of our perception or understanding.

In this sense, reality is the objective world outside the mind. The brain-as-computer metaphor suggests that the brain takes in “data” from the world through the senses and uses it to construct a representation of reality.

This representation is based on the brain’s interpretation of the data and is influenced by various cognitive processes such as perception, memory, and belief.

Therefore it can be seen that the reality we perceive and understand is not the same as the reality that exists independently.

So, according to this perspective, reality exists independently of our understanding of it.

The brain’s interpretation results from the cognitive processing of the data it receives. Thus, it can be seen as a model of reality but not reality itself.

What’s wrong with this metaphor?

The “brain-as-computer” metaphor is a valuable tool for understanding certain aspects of cognitive function, but it has some limitations and potential drawbacks when applied too broadly.

Here are a few examples:

• The brain is much more complex than a computer: While the brain and computer have some similarities in processing information, the brain is an incredibly complex organ with many different regions and functions that are not replicated in a computer.
• The brain is not a passive information processor: The brain is not just a passive processor of information. It is also actively generating and predicting its inputs.
• The brain is embodied and embedded: It is a part of the body, constantly interacting with the environment and being affected by it. The computer, on the other hand, operates in a separate environment.
• The brain is not digital: The brain operates with continuous and analog signals. While computers process information in a discrete and digital way, the brain uses continuous and analog signals.
• The brain is not rule-based: While computers rely on pre-programmed rules to process information, the brain can adapt to new situations and learn from experience.

In summary, while the brain-as-computer metaphor can be a helpful way to understand certain aspects of cognitive function, it is essential to recognize its limitations and not apply it too broadly.

The brain is a much more complex and dynamic system than a computer which we still don’t understand fully.

Credit: ChatGPT has assisted me in composing this post by acting as a thinking partner and writing assistant.

Posts that link to this post

•  Knowledge and Information Management (KIM) Distinguishing Knowledge Management from Information Management
•  Knowledge Only Exists in the Mind Everything else is information
•  Sharing Knowledge Through Conversation Knowledge isn't there the way ore is buried
•  The Importance of Tacit Knowledge Tacit knowledge is knowledge that is difficult to transfer
•  Data, Information and Knowledge Differentiating knowledge stuff

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