Distinction between data, information and knowledge

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Distinction between data, information and knowledge

In the quest for organizational excellence, understanding the nuanced distinctions between data, information, and knowledge is not just an academic exercise; it's a strategic imperative.?

Imagine trying to measure the effects of a new initiative. Were we successful?? If yes - how much??

The success of solving this measurement challenge hinges not merely on the collection of data but on the transformation of that data into actionable insights and, ultimately, into knowledge that empowers decision-making.

From Raw Data to Refined Knowledge

Data represents the quantifiable aspects of the world around us, devoid of context or meaning. Information emerges as this data is processed, organized, and analyzed. Yet, it is knowledge, the culmination of this process, that equips individuals with insights and understanding to make informed decisions. This hierarchical flow from data to information to? knowledge is the cornerstone of informed decision-making, enabling us to assess the effectiveness of interventions with precision and clarity.

The essence of knowledge, as explored by thinkers like Dretske and echoed by Davenport and Prusak, lies not in its tangible form but in its possession by the human mind.?

Dretske describes information as ‘that commodity capable of yielding knowledge, and what information a signal carries is what we can learn from it. Knowledge is identified with information-produced (or sustained) belief, but the information a person receives is relative to what he or she already knows about the possibilities at the source[1] .

Davenport and Prusak give the following description: “Knowledge derives from information as information derives from data. If information is to become knowledge, humans must do virtually all the work” [2].’

Boisot's insights further illuminate this dynamic: "Knowledge builds on information that is extracted from data. Data may or may not convey information to an agent. Whether it does so or not depends on an agent’s prior stock of knowledge. Thus whereas data can be characterized as a property of things, knowledge is a property of agents predisposing them to act in particular circumstances. Information is that subset of the data residing in things that activate an agent – it is filtered from the data by the agent’s perceptual or conceptual apparatus. Information, in effect, establishes a relationship between things and agents. Knowledge either consolidates or undergoes modifications with the arrival of new information. In contrast to information, knowledge cannot be directly observed. Its existence can only be inferred from the action of agents.’”[3]

Tuomi challenges traditional hierarchies and argues that the hierarchy from data to knowledge is actually inverse; knowledge must exist before information can be formulated and before data can be measured to form information. His central argument is that knowledge does not exist outside of an agent (a knower)[4]

This inversion emphasizes the foundational role of the knower in shaping what is measured and known, a notion that resonates with the idea that information is converted to knowledge once it is processed in the mind of individuals and knowledge becomes information once it is articulated and presented in the form of text, graphics, words, or other symbolic forms[5] .

Perspectives and Practice on knowledge

Different perspectives on knowledge exist among scholars and practitioners.? Frequently, knowledge has been perceived as an object, defined as “justified true belief”. It is assumed that knowledge can be codified and separated from the minds of people. The idea extends beyond human cognition to include mechanized, documented, and automated forms of knowledge, broadening the scope of knowledge carriers. Some authors argue that knowledge can also be embedded in entities other than human beings[6][7]. They also identify:

• mechanized knowledge - where the knowledge necessary to carry out a specific task has been incorporated in the hardware of the machine,?
• documented knowledge - where knowledge has been stored in the form of archives, books, documents, ledgers, instructions, charts, design specifications etc. and?
• automated knowledge - where knowledge has been stored electronically and can be accessed by computer programs that support specific tasks.

The importance of information embedded in documented routines and technologies is acknowledged to be important in the process of knowing and doing. For example, technologies might yield knowledge with reverse engineering. However, documented? mechanized and automated knowledge are considered as information in this research, rather than knowledge, following the definition of [5]

A second perspective on knowledge stresses that knowledge could only reside in the mind of people and can be defined as “that which is known”, i.e. knowledge being embedded in individuals [8]. Only people can ‘know’ and convert ‘knowing’ into action, and it is the act of thinking that can transform information into knowledge and create new knowledge[9].

Although the first two perspectives on knowledge still guide many practitioners and academics, a third perspective is gaining ground. This perspective defines knowledge as “the social practice of knowing”, addressing the social character of knowledge[10]. Knowledge is considered to be embedded in a community rather than just in one individual. It suggests knowledge to supersede any one individual and to be highly context dependent[11][12][13][14]

Knowledge may be viewed from five other perspectives (1) a state of mind, (2) an object, (3) a process, (4) a condition of having access to information, or (5) a capability[5].

In line with defining knowledge as ‘justified belief that increases an entity’s capacity

for effective action’[15][16][17], in here knowledge is defined as: “collective understanding plus the ability to transform this understanding into actions, which yields performance being dependent of the situation in which it is learned and used”[18]

A call to Action

As we delve into the interplay of data, information, and knowledge, let us not lose sight of the ultimate goal: to harness this understanding in service of more effective action and enhanced performance. The journey from data to knowledge is both a challenge and an opportunity, inviting us to engage deeply with the world around us and to emerge, equipped with the insights and understanding necessary to shape a better future.

Let's continue this conversation. How do you see the interplay of data, information, and knowledge unfolding in your domain? Share your insights and experiences, and let's collectively build on our understanding to drive transformation and success.

#Knowledge #DataToInformation #InformationToKnowledge

1. Dretske, F. (1981). Knowledge and the flow of information. MIT Press
2. Davenport, T. H. and Prusak, L. (1998). Working knowledge : how organizations manage what they know. Harvard Business School Press
3. Boisot, M. H. (1998). Knowledge assets; Securing competitive advantage in the information economy. Oxford University Press.
4. Tuomi, I. (1999). Data is more than knowledge: implications of the reversed hierarchy for knowledge management and organizational memory. 32nd Hawaii International Conference on System Sciences. Los Alamitos.
5. Alavi, M. and Leidner, D. E. (2001). Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly 25, 107-136.
6. Boersma, S. K. T. and Stegwee, R. A. (1996). Exploring the issues in knowledge management. Information technology management and organizational innovations. Washington D.C.
7. Spek, R. v. d. and Spijkervet, A. (1997). Knowledge management; dealing intelligently with knowledge. CIBIT.
8. Polanyi, M. (1998). Personal Knowledge: Towards a Post-Critical Philosophy. Routledge.
9. McDermott, R. (1999). Why information technology inspired but cannot deliver knowledge management. California Management Review 41, 103-117.
10. Blackler, F. (1995). Knowledge, knowledge work and organizations: an overview and interpretation. Organization Studies 16, 1021-1046.
11. Brown, J. S. and Duguid, P. (1991). Organizational learning and communities of practice. Organizational Science 2, 40-57.
12. Lave, J. and Wenger, E. (1991). Situated learning: legitimate peripheral participation. Cambridge University Press.
13. Wenger, E. (1998). Communities of practice; learning, meaning and identity. Cambridge University Press.
14. Orr, J. (1996). Talking about machines: an ethnography of a modern job. Cornell University Press.
15. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science 5, 14-37
16. Nonaka, I. and Takeuchi, H. (1995). The knowledge-creating company: how Japanese companies create the dynamics of innovation. Oxford University Press
17. Huber, G. P. (1991). Organizational learning: the contribution process and the literatures. Organization Science 2, 88-115.
18. Boer, N.. (2005). Knowledge Sharing within Organizations.