When the person get it’s first interested in knowledge as a concept, and then knowledge management, it was because of the connections between studies and the data, information, knowledge, and wisdom. Finally that became interested to understatement as I’m generally either not interested or possessed, and infrequently anywhere in between.
Person managed to survive the Formula Fifties, the responsive Sixties, the Strategic Seventies, and the brilliant Eighties to exist in the Nanosecond Nineties, and for a time we thought that it was headed for the Learning Organizational of the next decade. The misdirection that was fixed up in was a focus on Knowledge Management not as a means, but as an end in itself. Yes, knowledge management is important, and I’ll address reasons why shortly. But knowledge management should simply be one of many co-operating means to an end, not the end in itself, unless your job turns out to be corporate knowledge management director or chief knowledge officer. I’m quite sure it will come to this, for in some ways we are inevitable dependable.
Now, let us take an example of the companies which are associate with the cause of my indirection and they are also been associated with in the past. These companies had adapted TQM or reengineering, not in support of what they were trying to achieve, but as ends in them because they simply didn’t know what they were really trying to achieve. And, since they didn’t know what they were really trying to achieve, the misdirection was actually a relief, and pursued with a passion and shy it just didn’t get them anywhere in particular.
What are the important to know about the knowledge management?
Mission: What are we trying to achieve?
Competition: How do we gain a spirited edge?
Performance: How do we deliver the results?
Change: How do we manage with change?
So, finally everything is important if it helps the organization ability and capacity to some extent, and also develops in these four dimensions.
Nonaka’s Model of Knowledge
Nonaka is the combination of term tacit knowledge and explicit knowledge. The key to knowledge creation lies in the way it is mobilized and converted through technology.
According to Professor Ikujiro Nonaka, knowledge creation is a spiraling process of interactions between explicit and tacit knowledge. The interactions between the explicit and tacit knowledge lead to the creation of new knowledge. The combination of the two categories makes it possible to conceptualize four conversion patterns.
Tacit to tacit communication (Socialization): Takes place between people in meetings or in team discussions.
Tacit to explicit communication (Externalization): Articulation among people trough dialog (e.g., brainstorming).
Explicit to explicit communication (Communication): This transformation phase can be best supported by technology. Explicit knowledge can be easily captured and then distributed/transmitted to worldwide audience.
Explicit to tacit communication (Internalization): This implies taking explicit knowledge (e.g., a report) and deducing new ideas or taking constructive action. One significant goal of knowledge management is to create technology to help the users to derive tacit knowledge from explicit knowledge.
Critical Analysis of SECI Model
The model assumes tacit knowledge can be transferred through a process of socialisation into tacit knowledge in others and that tacit knowledge can become explicit knowledge through a process of externalisation (top 2 squares of the model in Figure 1). The model also assumes (bottom 2 squares) that explicit knowledge can be transferred into tacit knowledge in others through a process of internalisation, and that explicit knowledge can be transferred to explicit knowledge in others through a process of combination.
Therefore, the transforming processes are assumed to be socialisation (everyday comradeship), externalisation (formalising a body of knowledge), internalisation (translating theory into practice) and combination (combining existing theories). However, perhaps knowledge transfer in organisations is much more complicated and convoluted than this simple matrix suggests.
DIFFERENCE BETWEEN DATA, INFORMATION AND KNOWLEDGE.
That a collection of data is not information, as Neil indicated, implies that a collection of data for which there is no relation between the pieces of data is not information. The pieces of data may represent information, yet whether or not it is information depends on the understanding of the one perceiving the data. I would also tend to say that it depends on the knowledge of the interpreter, but I’m probably getting ahead of myself, since I haven’t defined knowledge. What I will say at this point is that the extent of my understanding of the collection of data is dependent on the associations I am able to discern within the collection. And, the associations I am able to discern are dependent on all the associations I have ever been able to realize in the past. Information is quite simply an understanding of the relationships between pieces of data, or between pieces of data and other information.
While information entails an understanding of the relations between data, it generally does not provide a foundation for why the data is what it is, nor an indication as to how the data is likely to change over time. Information has a tendency to be relatively static in time and linear in nature. Information is a relationship between data and, quite simply, is what it is, with great dependence on context for its meaning and with little implication for the future.
Beyond relation there is pattern, where pattern is more than simply a relation of relations. Pattern embodies both a consistency and completeness of relations which, to an extent, creates its own context. Pattern also serves as an Archetype with both an implied repeatability and predictability.
When a pattern relation exists amidst the data and information, the pattern has the potential to represent knowledge. It only becomes knowledge, however, when one is able to realize and understand the patterns and their implications. The patterns representing knowledge have a tendency to be more self-contextualizing. That is, the pattern tends, to a great extent, to create its own context rather than being context dependent to the same extent that information is. A pattern which represents knowledge also provides, when the pattern is understood, a high level of reliability or predictability as to how the pattern will evolve over time, for patterns are seldom static. Patterns which represent knowledge have completeness to them that information simply does not contain.
Wisdom arises when one understands the foundational principles responsible for the patterns representing knowledge being what they are. And wisdom, even more so than knowledge, tends to create its own context. I have a preference for referring to these foundational principles as eternal truths, yet I find people have a tendency to be somewhat uncomfortable with this labeling. These foundational principles are universal and completely context independent. Of course, this last statement is sort of a redundant word game, for if the principle was context dependent, then it couldn’t be universally true now could it?
So, in summary the following associations can reasonably be made:
Information relates to description, definition, or perspective (what, who, when, where).
Knowledge comprises strategy, practice, method, or approach (how).
Wisdom embodies principle, insight, moral, or archetype (why).
Now that I have categories I can get hold of, maybe I can figure out what can be managed.
The best way to understand difference between the data, information and knowledge is an example of a bank savings account to show how data, information, knowledge, and wisdom relate to principal, interest rate, and interest.
Data: The numbers 100 or 5%, completely out of context, are just pieces of data. Interest, principal, and interest rate, out of context, are not much more than data as each has multiple meanings which are context dependent.
Information: If I establish a bank savings account as the basis for context, then interest, principal, and interest rate become meaningful in that context with specific interpretations.
Principal is the amount of money, $100, in the savings account.
Interest rate, 5%, is the factor used by the bank to compute interest on the principal.
Knowledge: If I put $100 in my savings account, and the bank pays 5% interest yearly, then at the end of one year the bank will compute the interest of $5 and add it to my principal and I will have $105 in the bank. This pattern represents knowledge, which, when I understand it, allows me to understand how the pattern will evolve over time and the results it will produce. In understanding the pattern, I know, and what I know is knowledge. If I deposit more money in my account, I will earn more interest, while if I withdraw money from my account, I will earn less interest.
Wisdom: Getting wisdom out of this is a bit tricky, and is, in fact, founded in systems principles. The principle is that any action which produces a result which encourages more of the same action produces an emergent characteristic called growth. And, nothing grows forever for sooner or later growth runs into limits.
If one studied all the individual components of this pattern, which represents knowledge, they would never discover the emergent characteristic of growth. Only when the pattern connects, interacts, and evolves over time, does the principle exhibit the characteristic of growth.
Now, if this knowledge is valid, why doesn’t everyone simply become rich by putting money in a savings account and letting it grow? The answer has to do with the fact that the pattern described above is only a small part of a more elaborate pattern which operates over time. People don’t get rich because they either don’t put money in a savings account in the first place, or when they do, in time, they find things they need or want more than being rich, so they withdraw money. Withdrawing money depletes the principal and subsequently the interest they earn on that principal. Getting into this any deeper is more of a systems thinking exercise than is appropriate to pursue here.
Knowledge network partners provide development practitioners with access to cutting-edge knowledge and information in their fields and across sectors and disciplines. They also facilitate sharing of experience about what works and what doesn’t. These networks typically are established as a result of, or lead up to, a learning program or event—although they can stand alone. They often use information and communications technologies to facilitate ongoing learning among people working on similar challenges from different geographical locations without anyone having to leave home. They bring together—virtually—communities of practice in a wide range of subjects providing electronic discussions and websites to encourage research and disseminate best practice.
COMMUNITIES OF PRACTICE: A HISTORICAL VIEW
Communities of Practice (CoPs) as a fact that have been around for many years but the term itself was not coined until 1991 when Jean Lave and Etienne Wenger used it in their exploration of Situated Learning (Lave and Wenger, 1991). Situated learning is learning that takes place through working practices, for example, an apprenticeship where an employee learns skills “on the job”.
Lave and Wenger (1991) saw the acquisition of knowledge as a social process where people can participate in communal learning at different levels depending on their level of authority or seniority in the group, i.e. whether they are a newcomer to the group or have been a member for a long time. Central to their notion of a CoP as a means of acquiring knowledge is the process by which a newcomer learns from the group; they term this process Legitimate Peripheral Participation (LPP).
COMMUNITIES OF PRACTICE TODAY
Lave and Wenger’s (1991) CoPs attracted a lot of attention and gradually other researchers and practitioners extended the notion of a CoP and applied it in a Knowledge Management (KM) context in commercial settings. Since then much work has been undertaken to observe CoPs, how they work and what sort of defining characteristics there are. Many definitions have been put forward – indeed, in this book you will find a number of definitions in the chapters.
In this Introduction, we do not intend to try to create a single definition that will cover the whole book. Rather we prefer to note some of the characteristics that might be found in a CoP:
What it is about?
This represents the particular area of activity/body of knowledge that the CoP has organized itself around. It is a joint enterprise in as much as it is understood and continually renegotiated by its members.
How does it function?
People become members of a CoP through shared practices; they are linked to each other through their involvement in certain common activities. It is mutual engagement that binds members of a CoP together as a social entity.
What has it produced?
The members of a CoP build up an agreed set of communal resources is over time. This “shared repertoire” of resources represents the material traces of the community. Written files can constitute a more explicit aspect of this common repository although more intangible aspects such as procedures, policies, rituals and specific idioms may also be included.
The term Common Ground is taken from the work of Clark and Brennan (1991). For communication to take place, certain information must be shared; this information is called common ground. Similarly, for a CoP to function the members need to be sympathetic to the ideas around which the group is based and will probably have a common background or share common a common interest.
The CoP members will have some sort of common goal or common purpose and it is often the case that the CoP is internally motivated i.e. driven by the members themselves as opposed to some external driver.
There is often some sort of evolution in a CoP. It may be that the CoP has developed because of a common interest of a group of people. On the other hand, it may be that the CoP was a formally constituted group that has evolved into a CoP because of the relationships that have developed amongst the members.
This is a key part of a CoP and is what makes it possible for a team to become a CoP – as the informal relationships develop so the source of legitimation in the group shifts in emphasis. These relationships are key to the issues of trust and identity in a CoP.
THREE THINGS WHICH I WILL AND I WILL NOT DO AS CHIEF KNOWLEDGEOF MY COMPANY
A Chief Knowledge Officer is an organizational leader, responsible for ensuring that the organization maximizes the value it achieves through “knowledge”. CKO responsibilities include such things as:
- Developing an overall framework that guides knowledge management,
- Actively promoting the knowledge agenda within and beyond the company,
- Overseeing the development of the knowledge infrastructure, and facilitating connections, coordination and
That’s quite a nice description. One special thing about this systems is, that it is an Open Source company, thus the borders between internal and external communication often do not exist. In fact, an Open Source company is just as much about an open communication as it is about open software.
This is actually the part I am most excited about: to explore the potentials of open knowledge management, which includes the systems team just as much as the developers community, the partners, etc. In an Open Source ecosystem, knowledge management is very much a joint effort of all actors involved and can only follow a bottom-up approach.
As a Knowledge Manager, I would like to make my organization as a Knowledge Resource along with the business operation. The business operation is prevalent since the existence of the organization. Looking at the potential needs of the organization a strategy is planned to ensure existence of Knowledge Management.
To enhance of knowledge within the organization, there is a necessity of providing or adapting Training and Development Strategies. This strategy will change the mindset of employees by imposing innovate and creative thinking towards establishment of Knowledge Management.
Secondly, by establishing the Social Communication Channels the employee are given an opportunity to express their ideas to other employees. This will help tacit knowledge to be transformed into partially explicit knowledge. The transformed explicit knowledge is combined with innovation and creative approach will become more absolute. The above mentioned approaches are gained as a result of training and development strategy.
The developed innovative & creative approaches of transforming the knowledge are to be stored either on a physical media or digital media. Here the physical media are referred to paper based documentation and digital media are referred to electronic devices.
- Alberthal, Remarks to the Financial Executives Institute, October 23, 1995, Dallas, TX
- Aldo de Moor & Efimova L. (2004) , “An Argumentation Analysis of Weblog Conversations”, 9th International Working Conference on the Language-Action Perspective on Communication Modelling
Balswin J., Hanel P (2004), “Innovation and Knowledge Creation in an Open Economy – Canadian Industry and International Implications”, Cambridge Press
Bateson, Mind and Nature: A Necessary Unity, Bantam, 1988
- Barksy, 2006. Introducing Web 2.0: Weblogs and Podcasting for Health librarians . 27, p. 33-34. Journal of Canada Health Library Association. Available at: http://pubs.nrc-cnrc.gc.ca/jchla/jchla27/c06-013.pdf
- Bontis N. & Chun Wei Choo (2002), “The Strategic Management of Intellectual Capital and Organizational Knowledge”, Oxford University Press (US)
- Cothrel J. & Williams R. (1999), “On-line communities: helping them form and grow”, Journal of Knowledge Management, Volume 3 · Number 1 Google Jobs, Retrieved on 3-Mar-2008 http://www.google.com/support/jobs/bin/static.py?page=about.html&about=top10
- Hasan, H and Pfaff, 2006. Emergent Conversational Technologies that are Democratising Information Systems in Organisations: the case of the corporate Wiki . University of Wollongong. Available at: http://ro.uow.edu.au/commpapers/284/
- Bellinger, The Knowledge Centered Organization
- Cougar, J. D. 1995, Creative Problem Solving and opportunity finding, boyd & Fraser Publishing, Retrieved March 01, 2008.
- Csikszentmihalyi, The Evolving-Self: A Psychology for the Third Millennium, Harperperennial Library, 1994.
- Davidson, The Transformation of Management, Butterworth-Heinemann, 1996.
- Doyle, J. L. (1985). Commentary: Managing the new product development process: How Japanese companies learn and unlearn. In K. B. Clark, R. H. Hayes, & C. Lorenz (Eds.), The uneasy alliance: Managing the productivity-technology
dilemma (pp. 377–381). Boston: Harvard Business School Press. Retrieved January 29, 2008, from http://www.emeraldinsight.com/
- Fleming, Coping with a Revolution: Will the Internet Change Learning?, Lincoln University, Canterbury, New Zealand
- Liew, A. June 2007, Journal of Knowledge Management Practice: Understanding Data, Information, Knowledge and Their Inter-Relationships, Vol. 8, No. 2, Retrieved February 28, 2008 from http://www.tlainc.com/articl134.htm
- McAdam, R and S. McCreedy (1999a) “A Critical Review of Knowledge Management Models”, The Learning Organization, 6(3), p.91. Retrieved January 27, 2008, from http://www.emeraldinsight.com/
- Malmberg, P. 2006, Scientific Proceedings: European Productivity Conference (EPC, 2006), Retrieved March 02, 2008 from http://www.epc2006.fi/EPC_Scientific_Proceedings.pdf#page=31
- Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. New York:
Oxford University Press. Retrieved January 28, 2008, from http://www.12manage.com/methods_nonaka_seci.html
- Kurt-martin Lugger, Herbert Kraus, 2001. Journal of Universal Computer Science: Mastering the Human Barriers in Knowledge Management, Vol. 7, No. 6 Retrieved March 01, 2008 from http://www.jukm.org/jucs_7_6/mastering_the_human_barriers/Lugger_K_M.pdf
- Poell, R. F., & van der Krogt, F. J. (2003). Learning strategies of workers in the knowledge-creating company. Human Resource Development International 6(3), 387–403. Retrieved January 29, 2008, from http://www.emeraldinsight.com/
- Retrieved February 25, 2008, from http://www.tfriend.com/cop-lit.htm
- Retrieved February 25, 2008, from http://www.answers.com/
- Retrieved February 25, 2008, from http://www.anecdote.com.au/archives/2006/07/the_difference.html
- Retrieved February 25, 2008, from http://en.wikipedia.org/wiki/Social_network
- Retrieved February 25, 2008, from http://www.elearningpost.com/articles/archives/communities_of_practice_at_the_core/
- Reverse Brainstorming, A different approach to brainstorming related variant: “Negative Brainstorming”, Retrieved March 01, 2008 from http://www.mindtools.com/pages/article/newCT_96.htm
- Rob Cross, Andrew Parker, Laurence Prusak, Stephen P. Borgatti, November 2001, Knowing What We Know: Supporting Knowledge Creation and Sharing in Social Networks, Retrieved March 01, 2008 from http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6W6S-451DFP2-2-K&_cdi=6606&_user=6703465&_orig=search&_coverDate=11%2F30%2F2001&_sk=999699997&view=c&wchp=dGLbVlb-zSkzk&md5=c0d7ee0e70b8a6768c5d653d7b5c3558&ie=/sdarticle.pdf
- Rowley, J. Feburary 15, 2007, Journal of Information Science, The Wisdon Hierarchy: Representations of the DIKW Hierarchy, Sage Publications, Retrieved February 28, 2008 from http://jis.sagepub.com/cgi/reprint/33/2/163
- Senge, The Fifth Discipline: The Art & Practice of the Learning Organization, Doubleday-Currency, 1990.
- Thomas, H. Davenport & Prusak, L. 1998, Working Knowledge: How Organisations Manage What They Know, A Book Summary by Jyrki J.J. Kasvi, Retrieved 28, February 2008 from http://www.knowledge.hut.fi/projects/itss/referDavenport.pdf