Data VS Information VS Knowledge | Elium

The terms “data,” “information,” and “knowledge” are often used interchangeably. However, it’s important to recognize that these terms hold distinct meanings. One might wonder, “Why be so precise about terminology? What difference does it make?” Well, a clear understanding of these terms is essential for effective knowledge management within an organization. Once we establish precise definitions for these concepts and understand their relationships, we can grasp their implications and their significance in organizational processes.

Once we define the basic terms and relate them to each other, we can comprehend their implications for practice and see their importance to our company processes. 

Data, Information and Knowledge

There are three generic things we usually refer to when we use the words ‘data’, ‘information’ and ‘knowledge’. First, when we talk about things that we know about: concepts, facts, and methods that we are familiar with. Second, when we deal with practical know-how: applying our grasp of concepts, facts, and methods to create action or make things happen. Third, when we refer to a body of knowledge: accumulated knowledge in books and other forms of documentation. 

Free Guide: Company-wide knowledge sharing

In their in-depth investigation of working knowledge, Davenport and Prusak (1998) established distinctions between data, information and knowledge. Their definitions of data, information and knowledge are as follows:


Data are raw facts and figures with no fundamental meaning. Data plainly reports part of a situation without providing an interpretation. It is an unprocessed form of knowledge that doesn’t convey value or significance. For data to have some useful meaning, it has to be organised, analysed and interpreted. In the context of an organisation, “data are most usefully described as structured records of transactions.”

Data can be:

  • Quantitative: when data can be counted or measured like cost, weight, and volume.

  • Qualitative: when data describes things like name, color, and shape.   


On the other hand, information is processed data. It is organised, classified, structured and provides meaningful and useful context. In contrast to data, information has meaning. “Data becomes information when its creator adds meaning. We transform data into information by adding value in various ways.” Here are several important processes that convert data to information:

  • Calculation: data are mathematically or statistically scrutinised

  • Categorisation: data are sorted into groups or classes

  • Condensing: summarising data to be more concise

  • Contextualising: gathering data for a purpose

  • Correcting: editing errors out from the data


Davenport and Prusak proposed a working definition of knowledge framed in a practical sense of organisational knowledge that it is “broader, deeper and richer than data or information”:

Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms.

This idea of knowledge is an amalgamation of information, that knowledge is a state of being, exists outside of the ‘knowers’, and has the capacity to effect action. This leads us to the two basic types of knowledge and the concepts of explicit, implicit, and tacit knowledge:

  1. Knowledge that is contained within a person alongside the person’s faculty to act on that knowledge

  2. Knowledge that is communicated and documented

Explicit, Implicit, and Tacit Knowledge

Here is a simple diagram by Nickols (2010) to distinguish explicit, implicit and tacit knowledge. 

Explicit Knowledge

Explicit knowledge is knowledge that is easily expressed, communicated and captured in text documents, diagrams, illustrations, and product specifications among other things. Examples of explicit knowledge includes scientific formulas, computer proograms, industry standards, and company best practices. It is “formal and systematic knowledge” (Nonaka, 1991).

Implicit Knowledge

On the other hand, implicit knowledge is displayed knowledge that can be captured. A simple example would be someone performing a task and how they execute that task is a display of implicit knowledge. Identifying and capturing that implicit knowledge can turn it into explicit knowledge that can be used across similar tasks.

Tacit Knowledge

Tacit knowledge is less tangible and more difficult to articulate and transfer. It is knowledge in the form of individual skills, wisdom, experience and ideas. Unlike explicit knowledge, tacit knowledge is usually passed on through exhaustive exposure to and continuous practice with the person of knowledge. Everyday life examples include language and intuition – knowledge that is harder to systematise or automate. 

Declarative and Procedural Knowledge

Psychologists also classify knowledge into declarative and procedural. But to touch on the terms briefly, declarative knowledge is practically similar to explicit knowledge as it is knowledge that describes facts, methods and procedures. 

Meanwhile procedural knowledge leans more towards the knowledge of doing something. Some experts in the field equate this to implicit knowledge while some view it as tacit knowledge. Regardless of where you stand in this difference of opinion, the fact remains that procedural knowledge is difficult to articulate and document in the form of text, diagrams and so on.

Declarative knowledge is akin to explicit knowledge, primarily focusing on facts, methods, and procedures. This knowledge describes the “what” – what things are, how they work, and the underlying principles. For example, an organization’s safety regulations or a software programming manual contain declarative knowledge. It serves as a reference point for employees to understand the fundamental aspects of their work.

Procedural knowledge, on the other hand, centers on “how” to do things. It pertains to the knowledge of processes, practices, and practical skills necessary for performing tasks effectively. This knowledge is often linked with implicit or tacit knowledge, as it involves the practical know-how required to execute tasks efficiently. For example, a skilled technician’s ability to troubleshoot complex equipment issues is a form of procedural knowledge.

Understanding the distinctions between declarative and procedural knowledge is crucial for organizations. While declarative knowledge provides the foundation, procedural knowledge equips employees with the skills and methods needed to apply that knowledge effectively.

Practical Use of Knowing the Terms

Why did we have to go in depth with all the different terminologies? It all comes down to effectively identifying what to capture, share and transfer within your company. Understanding what data, information, knowledge and the types of knowledge are is fundamental to driving your organisation’s success. 

Recognizing the precise definitions of data, information, and knowledge, as well as the types of knowledge (explicit, implicit, tacit, declarative, and procedural), has practical implications for organizations. It enables them to make informed decisions about what to capture, share, and transfer.

By distinguishing between data, information, and knowledge, organizations can streamline their processes and workflows. For instance, data can be efficiently collected and transformed into meaningful information, and this information can then serve as the basis for knowledge creation and sharing.

Moreover, understanding the types of knowledge empowers organizations to identify areas where explicit knowledge can be documented, making it accessible to all employees. It also sheds light on the importance of recognizing and leveraging implicit and tacit knowledge held by experienced personnel, ultimately leading to improved decision-making and problem-solving.

Maximise Your Company Potential Through Efficient Knowledge Management

Your company’s success depends on how you catalog and utilise all the data, information, experiences, and random knowledge you have acquired through individual employees and as an organisation. Filing explicit knowledge is not enough. To remain competitive, it is imperative that your company has the means to transfer and translate tacit knowledge of high-performing employees to their peers.

Effective knowledge management can substantially impact an organization’s success. A comprehensive knowledge management strategy goes beyond simply filing explicit knowledge. It encompasses the capture, sharing, and transfer of implicit and tacit knowledge, which often reside within the minds of high-performing employees.

By adopting knowledge management practices, companies can maximize their potential by harnessing the collective expertise and insights of their workforce. This not only enhances productivity but also reduces operational costs. In addition, efficient knowledge management fosters innovation by encouraging the dissemination of best practices and individual experiences.

Elium is an easy online tool that can help your team effectively capture individual knowledge so they can store and share critical know-hows of their work process. Turn data and information into actionable knowledge through Elium’s unique collaboration space where employees can share best practices and individual experiences as a learning point for their peers. 

Download our company-wide guide to find out how Elium can empower your organisation today. Or request a personalised demo to start building a knowledge-driven workplace.

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