Anonymous, 12-Mar-06 @ 03:06AM

dangerous thinking as a practice

The "data, information, knowledge" triangle works as a tool for refinement when founded on common sense. It is by now embraced as a standard model, with little reference to whether it refers to crankshafts, molecules, people, "increased shoe production" or a policy decision? In the case of "people", the model may fall short. Some tech companys make minor errors such as selling knowledge as a product of "data refinement" with little notice is given to the magnitude and human repercussions of such assumptions by the group.

The "data, information, knowledge, wisdom" pyramid SEEMS valuable, as it allows for the catagorization of "out of context" input as "only data", but in a politically charged organization or an "everybody voted for Saddam" world, even lowly data should be taken with a grain of salt. False data should be included in the triangle as having negative value...as in (from the top) wisdom, knowledge, information, data, false data, false information, false knowledge..in that order.

One CEO of a "knowledge management" corporation insisted that "information drives action". Of course the only person who would be driven by information would be someone in a suggestive state...ie someone under extreme duress. Along that line, what type of model would work in an environment of duress??? Answer: No time for rocket science. Duress is a human condition to be met with understanding and standard solutions such as employment laws. Don't look to a model for an answer to human situations.

But refinement of data is valueable....and care should be given to the methods of refinement in proportion to the criticalness of decisions based on the resulting information and knowledge.

Matthew Rees, 17-Jan-06 @ 11:43AM

Knowledge consists of data, information and expertise

I have never been happy with the D/I/K/W pyramid because it creates an artificial divide between data, information and knowledge and it adds the spurious term wisdom.

Instead, in Lambeth we have adapted the definition developed by TFPL:

K=ei2 (that's "i" squared)
Knowledge = experience x information x ideas

We modified this slightly to give:

Knowledge may reside in people’s heads (expertise), structured information
systems (data) or documents (information).

This shows that data and information are aspects of knowledge, rather than separate things. The differences then between data, information and expertise are to do with the degree of complexity (or structure) to the knowledge. There is a continum from data through to expertise with (put simply) data stored in artefacts, expertise in people's heads and information in a combination of the two.

Saeema Ahmed, 11-Jan-06 @ 14:04PM

model

Denisa.

Here is a model, which is contexted dependant which i published( with others) on the topic.
AHMED, S., BLESSING, L.T.M. and WALLACE, K.M. (1999) 'The relationship between data, information and knowledge based on a preliminary study of engineering designers' in ASME Design Theory and Methodology, Las Vegas, Nevada, USA,

the paper can be downloaded from (under 1999): http://www.web.mek.dtu.dk/staff/sah/publications/publications.htm

Mark McElroy, 02-Jan-06 @ 20:06PM

False Data is Not Wisdom

Dear Jose:

I think it is a big mistake to ignore the content of what we refer to as data, information, knowledge, and wisdom. Indeed, we already have terms for the kinds of distinctions you make. We simply say 'individually-held' knowledge versus, say, 'organizationally-' or 'collectively-held' knowledge. The same goes for data, wisdom, etc.

Of vital importance to our lives is the quality of the semantic content in data, information, etc. that we rely on as a basis for action. And so while I think the ontological kind of distinctions you propose have a role to play in a good theory of knowledge, your idea completely fails to address the issue of truth versus falsity (in the case of factual knowledge); and legitimacy versus illegitimacy (in the case of valuational knowledge).

And this we should not accept. Why? Because according to your proposal, false data or information would be knowledge or wisdom simply because it is held by an individual. How and by whom it is held would validate it regardless of its semantic merits.

Thus, there would be no difference between false information and true information. Any information held by an individual would be deemed 'wisdom'. Try to run a business on that basis and I think you will see that it simply will not work. It is just another form of relativism -- like saying that anyone's knowledge is as good as any other's, and that none of us should pay any attention to the facts in the world. That's not wisdom, it's a suicide pact.

Regards,

Mark

José Sánchez-Cerezo, 02-Jan-06 @ 15:47PM

another perspective (with a map of knowledge)

Hello all!

I think that there is a distinction between data, information, knowledge and wisdom, but perhaps we must not look at the content of knowledge itself. How about considering the "owner" of what is known.
All the data would correspond with all humankind, and wisdom can be achieved only by an individual (although the way to get to it is a different one than any other subject we can think of).

To illustrate this here goes a map of all knowledge (all traditional subjects)

www.filosofos.net/mapa/conocimiento.html

and its relation with the owner or owners of it and the level of organization of it (data, information, knowledge, wisdom)

www.filosofos.net/mapadelconocimiento.htm

Hope you like it

José Sánchez-Cerezo de la Fuente

Mark McElroy, 27-Dec-05 @ 01:56AM

Forget the Tree and the Pyramid

Dear Denisa:

In KM, the distinction between data, information, knowledge, and wisdom is a perennially confused and misinformed one and should be discarded as quickly as possible. Instead of concerning oneself with competing definitions, one should ask the questions that lie behind them.

In this case, the question is not 'What is the distinction between these terms?'. Rather, the question is 'Can there be a correspondence between an explicit belief, a knowledge claim or statement, and a fact or value in the world?'

When you come at the problem from this perspective, you are able to see that data, information, and knowledge are all alike in the sense that they SAY SOMETHING about the world. That is, they ASSERT something, usually in a descriptive sense. Thus, they are all alike at that level of analysis.

But we can also say that some of our beliefs or claims manage to survive our further criticisms and others do not. In other words, some beliefs or claims we think are true, whereas others are false. What words can we use to differentiate between such things?

First, the very question provides us with a motivation for choosing a word that helps us to distinguish between what we regard as true beliefs or claims and non-true beliefs or claims. This is the root problem that your vocabulary question should try to solve. Let us use the word 'knowledge', then, to refer to what we think are true beliefs or claims. It is as good as any. If you disagree, then pick another word; it matters not. But do not lose the point of the distinction.

Thus, 'data' are simply claims of a structured kind that have not yet been judged. And 'unstructured information' (a term missing from your query) are also claims that have not yet been judged. 'Knowledge', as I say, then, could either be data or unstructured information that have survived our judgments and which we therefore believe are true. Until judged, data and unstructured information are just species of information of two sorts. And knowledge is a third one.

What then of wisdom? [This is a refinement to my earlier posting here.] There is a difference between theories of truth and theories of how to arrive at the truth (or at least near it), the latter of which are theories of evaluation. Theories of evaluation, then, are value theories. We can speak of value theories as not 'true' or 'false', but as 'legitimate', 'illegitimate', or 'non-legitimate'.

Wisdom, then, consists of factual beliefs or claims that we think correspond to the facts, made in conjunction with interrelated valuational beliefs or claims that we separately think correspond to the legitimate.

That said, wisdom is arguably just another type of knowledge, so it's not clear what the word brings to the table that wasn't already there in the form of 'knowledge'. I'd be interested to hear what you or others have to say about this.

Regards,

Mark

Denisa Neagu, 26-Dec-05 @ 19:57PM

Knowledge tree

Thank you all for your suggestions that I found very interesting.

Lakshman Pillai, 26-Dec-05 @ 11:48AM

Connected Learning Impressions in our Mind

Data and information are highly structured. Hence, there is definitely sequence, relation and multiple representations including relations (table), hierarchical, network or graph model. There is no doubt about it.

I very much agree, knowledge is fluid and continuously changing. It varies based on the perception, experience, understanding and belief of the people. But at any point in time, there is some level “structured representation” behind that knowledge. This representation (mind-map, concept map, taxonomy and meaning-based connections) helps us retain, recall and respond effectively. Tacit knowledge could be more fluid and less visible compared to explicit or implicit knowledge.

The learning impressions in our mind is structured but it goes through improvement on a daily basis. The frequency or speed of change depends on the learner. Our conscious living and learning defines how frequently we bring more clarity to our learning impressions. This process needs to undergo some level of restructuring in our mind.

We don't change our opinion on any subject completely all the time. We only fine-tune our learning.

Highly experienced person (expert) is able to respond to the problem quickly because that person is smart enough to relate things (connections). In a relatively simple form, it looks like a “knowledge tree”.

Denham Grey, 25-Dec-05 @ 02:00AM

DIKW

Denisa,

There is no real progression, order or sequence to data, information, knowledge and wisdom. What you work with and where you fit, depends on your context, past experience, level of tacit knowledge, current understanding and time.

http://denham.typepad.com/km/2003/12/dikw.html

The knowledge pyramid is a more common representation (model) than a knowledge tree.

http://gsdidocs.org/gsdiconf/GSDI-8/papers/wsva_01/wsva01_01_markus.pdf

Lakshman Pillai, 20-Dec-05 @ 04:43AM

Knowledge Taxonomy / Tree

Deinisa,

I can think of knowledge tree (in some cases it is called decision tree or knowledge connector) in the following cases. Sometimes, it is not represented in a perfect hierarchical (tree) model due to lateral connection that looks more like a network model.

1. Expert System:
- Rules or dialogue or questions + Inference engine = knowledge
- Here, I can think of 'decision tree' that represent the rules (information or data) in a hierarchical form that help probe and traverse through the tree to derive knowledge.

eg: In a medical diagnostic system, :
- Do you have cough? Yes
- Do you have fever? No
- Do you have throat irritation? Yes

Rules: (Cough, Fever) -> Flu

eg: In a trouble shooting environment, system could ask series of questions:

Issue: Am not able to connect to the network

Dialogue:

- Do you see the light in the network card? Yes
- Is ping working? Yes
- Do you have a firewall configured in your system? Yes

Based on the answer, the subsequent questions will be different.

Here the challenge is building this decision tree. Based on the answer it can give the solution (knowledge).

2. Connected Knowledge

In this case, the system could connect related knowledge so that when someone is going through a piece of knowledge or information then it can lead people to related knowledge.

Eg: Amazon.com – related books and readers or buyers information. Individual pieces are more of information, but the connected piece is a valuable knowledge. In LPCUBE (www.lpcube.com) we have knowledge connector and navigation.

3. Knowledge Taxonomy

This hierarchical (with lateral connection) can be used to represent the knowledge category that help organize information or knowledge.

4. Knowledge Encyclopedia or Dictionary

In some systems, they have visual tree based or network model to link related words in the case of dictionary and related information in the case of encyclopedia.

5. Learning Map

In this case, the training plan or calendar (information) can be represented in a tree form to help people to take the right course in the right order. Sometimes there can be link between skills level and learning map. Together all these pieces of valuable information are knowledge to people that gives insights into what to learn, why to learn, what are all the things I need to learn first before learning this, when to learn and so on.

I hope my answer is in line with your question. If you need further clarification, feel free to ask more specific questions.