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Knowledge Management comprises a range of practices used by organisations to identify, create, represent, and distribute knowledge for reuse, awareness and learning. It has been an established discipline since 1995 with a body of university courses and both professional and academic journals dedicated to it. Most large companies have resources dedicated to Knowledge Management, often as a part of ‘Information Technology’ or ‘Human Resource Management’ departments, and sometimes reporting directly to the head of the organisation. As effectively managing information is a must in any business, Knowledge Management is a multi-billion dollar world wide market.

Knowledge Management programs are typically tied to organisational objectives and are intended to achieve specific outcomes, such as shared intelligence, improved performance, competitive advantage, or higher levels of innovation.

One aspect of Knowledge Management, knowledge transfer has always existed in one form or another. Examples include on-the-job peer discussions, formal apprenticeship, corporate libraries, professional training and mentoring programs. However, with computers becoming more widespread in the second half of the 20th century, specific adaptations of technology such as knowledge bases, expert systems, and knowledge repositories have been introduced to further simplify the process.

Knowledge Management programs attempt to manage the process of creation (or identification), accumulation and application of knowledge across an organisation. Knowledge Management, therefore, attempts to bring under one set of practices various strands of thought and practice relating to:

• intellectual capital and the knowledge worker in the knowledge economy
• the idea of the learning organization
• various enabling organizational practices, such as Communities of Practice and corporate Yellow Page directories for accessing key personnel and expertise
• various enabling technologies such as knowledge bases and expert systems, help desks, corporate intranets and extranets, Content Management, wikis and Document Management

While Knowledge Management programs are closely related to Organizational Learning initiatives, Knowledge Management may be distinguished from Organisational Learning by a greater focus on specific knowledge assets and the development and cultivation of the channels through which knowledge flows.

The emergence of Knowledge Management (‘KM’) has also generated new roles and responsibilities in organisations, an early example of which was the Chief Knowledge Officer. In recent years, Personal knowledge management (PKM) practice has arisen in which individuals apply KM practice to themselves, their roles and their career development.

Contents [hide] 1 Approaches to Knowledge Management 2 Schools of thought in Knowledge Management 3 Key concepts in Knowledge Management 3.1 Tacit versus explicit knowledge 3.2 Knowledge capture stages 3.3 Ad hoc knowledge access 4 Drivers of Knowledge Management 5 Knowledge Management enablers 6 Knowledge Management roles and organizational structure 7 Knowledge Management lexicon 8 Related definitions 9 See also 10 Further reading 10.1 Articles 11 External links

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[edit] Approaches to Knowledge Management

There is a broad range of thought on Knowledge Management with no unanimous definition. The approaches vary by author and school. Knowledge Management may be viewed from each of the following perspectives:

• Techno-centric: A focus on technology, ideally those that enhance knowledge sharing/growth.
• Organisational: How does the organisation need to be designed to facilitate knowledge processes? Which organizations work best with what processes?
• Ecological: Seeing the interaction of people, identity, knowledge and environmental factors as a complex adaptive system.

In addition, as the discipline is maturing, there is an increasing presence of academic debates within epistemology emerging in both the theory and practice of knowledge management. British and Australian standards bodies both have produced documents that attempt to bound and scope the field, but these have received limited acceptance or awareness.

Knowledge mapping is commonly used to cover functions such as a knowledge audit (discovering what knowledge exists at the start of a knowledge management project), a network survey (Mapping the relationships between communities involved in knowledge creation and sharing) and creating a map of the relationship of knowledge assets to core business process. Although frequently carried out at the start of a Knowledge Management programme, is is not a necessariy pre-condition or confined to start up.

[edit] Schools of thought in Knowledge Management

There are a variety of different schools of thought in Knowledge Management. For example;

• The Intellectual Capital movement with Professor Nick Bontis, Professor Leif Edvinsson and Tom Stewart
• A body of work derivative of information theory associated with Prusak and Davenport.
• Complexity approaches associated with Snowden (see Cynefin)
• ‘Narrative’ with Denning, Snowden, Boje and others.

There are several other schools of thought on the matter.

[edit] Key concepts in Knowledge Management

[edit] Tacit versus explicit knowledge

A key distinction made by the majority of knowledge management practitioners is Nonaka’s reformulation of Polanyi’s distinction between tacit and explicit knowledge. The former is often subconscious, internalized, and the individual may or may not be aware of what he or she knows and how he or she accomplishes particular results. At the opposite end of the spectrum is conscious or explicit knowledge – knowledge that the individual holds explicitly and consciously in mental focus, and may communicate to others. In the popular form of the distinction tacit knowledge is what is in our heads, and explicit knowledge is what we have codified.

Nonaka and Takeuchi (1995) argued that a successful KM program needs to, on the one hand, convert internalized tacit knowledge into explicit codified knowledge in order to share it, but also on the other hand for individuals and groups to internalize and make personally meaningful codified knowledge once it is retrieved from the KM system.

The focus upon codification and management of explicit knowledge has allowed knowledge management practitioners to appropriate prior work in information management, leading to the frequent accusation that knowledge management is simply a repackaged form of information management. (Eg Wilson, T.D. (2002) “The nonsense of ‘knowledge management’” Information Research, 8(1), paper no. 144 [Available at http://InformationR.net/ir/8-1/paper144.html]

Critics have however argued that Nonaka and Takeuchi’s distinction between tacit and explicit knowledge is oversimplified, and even that the notion of explicit knowledge is self-contradictory.

Other commonly used types of knowledge include embedded knowledge (knowledge which has been incorporated into an artifact of some type, for example a tool has knowledge embedded into its design) and embodied knowledge (knowledge as learned capability of the body’s nervous, chemical & sensory systems). These two types, while frequently used, are not universally accepted, any more than is the distinction between tacit and explicit.

The latest wave of Enterprise 2.0 social computing tools provide a more unstructured approach information exchange and the development of new forms of community within and without the organisation. However such tools are still based on text and are thus explicit in nature. An additional challenge these tools face is how to distill meaningful, re-usable knowledge from the variety of other content also captured in tools like blogs and wikis.

[edit] Knowledge capture stages

Knowledge may be accessed, or captured, at three stages: before, during, or after knowledge-related activities.

For example, individuals undertaking a new project for an organization might access information resources to learn best practices and lessons learned for similar projects undertaken previously, access relevant information again during the project implementation to seek advice on issues encountered, and access relevant information afterwards for advice on after-project actions and review activities. Knowledge management practitioners offer systems, repositories, and corporate processes to encourage and formalize these activities.

Similarly, knowledge may be captured and recorded before the project implementation, for example as the project team learns lessons during the initial project analysis. Similarly, lessons learned during the project operation may be recorded, and after-action reviews may lead to further insights and lessons being recorded for future access.

Different organizations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. There is controversy over the whether incentives work or not in this field and no firm consensus has emerged.

[edit] Ad hoc knowledge access

One alternative strategy to encoding knowledge into and retrieving knowledge from a knowledge repository such as a database, is for individuals to access experts on an ad hoc basis, as needed, with their knowledge requests. A key benefit of this strategy is that the response from the expert individual is rich in content and contextualised to the particular problem being addressed and personalized to the particular person or people addressing it. The downside is, of course, that it is tied to the availability and memories of specific individuals in the organisation. It does not capture their insights and experience for future use should they leave or become unavailable, and also does not help in the case when the experts’ memories of particular technical issues or problems previously faced change with time. The emergence of narrative approaches to knowledge management attempts to provide a bridge between the formal and the ad hoc, by allowing knowledge to be held in the form of stories.

[edit] Drivers of Knowledge Management

There are a number of ‘drivers’, or motivations, leading to organizations undertaking a knowledge management program.

Perhaps first among these is to gain the competitive advantage that comes with improved or faster learning and new knowledge creation. Knowledge management programs may lead to greater innovation, better customer experiences, consistency in good practices and knowledge access across a global organization, as well as many other benefits, and knowledge management programs may be driven with these goals in mind.

Considerations driving a Knowledge Management program might include:

• making available increased knowledge content in the development and provision of products and services
• achieving shorter new product development cycles
• facilitating and managing organizational innovation
• leverage the expertise of people across the organization
• benefiting from ‘network effects’ as the number of productive connections between employees in the organization increases and the quality of information shared increases
• managing the proliferation of data and information in complex business environments and allowing employees rapidly to access useful and relevant knowledge resources and best practice guidelines
• facilitate organizational learning
• managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals) as individuals retire and new workers are hired
• a convincing sales pitch from one of the many consulting firms pushing Knowledge Management as a solution to virtually any business problem, such as loss of market share, declining profits, or employee inefficiency.
• increasing the responsiveness or resiliance of organisations to environmental change

[edit] Knowledge Management enablers

Historically, there have been a number of technologies ‘enabling’ or facilitating knowledge management practices in the organization, including expert systems, knowledge bases, various types of Information Management, software help desk tools, document management systems and other IT systems supporting organizational knowledge flows.

The advent of the Internet brought with it further enabling technologies, including e-learning, web conferencing, collaborative software, content management systems, corporate ‘Yellow pages’ directories, email lists, wikis, blogs, and other technologies. Each enabling technology can expand the level of inquiry available to an employee, while providing a platform to achieve specific goals or actions. The practice of KM will continue to evolve with the growth of collaboration applications, visual tools and other technologies. Since its adoption by the mainstream population and business community, the Internet has led to an increase in creative collaboration, learning and research, e-commerce, and instant information.

There are also a variety of organisational enablers for knowledge management programs, including Communities of Practice, before-, after- and during- action reviews (see After Action Review), peer assists, information taxonomies, coaching and mentoring, and so on.

[edit] Knowledge Management roles and organizational structure

Knowledge management activities may be centralized in a Knowledge Management Office, or responsibility for knowledge management may be located in existing departmental functions, such as the Human Resource (to manage intellectual capital) or IT departments (for content management, social computing etc.). Different departments and functions may have a knowledge management function and those functions may not be connected other than informally.

[edit] Knowledge Management lexicon

Knowledge Management professionals may use a specific lexicon in order to articulate and discuss the various issues arising in Knowledge Management. For example, terms such as intellectual capital, metric, and tacit vs explicit knowledge typically form an indispensable part of the knowledge management professional’s vocabulary.

[edit] Related definitions

• Intellectual capital – the intangible assets of a company which contribute to its valuation.
• Chief Knowledge Officer – an executive responsible for maximizing the knowledge potential of an organization.
• Knowledge – that which can be acted upon.
• Personal knowledge management – the organization of an individual’s thoughts and beliefs.

[edit] See also

Terminology extraction Battle Command Knowledge System Community of practice Competitive intelligence Complexity theory and organizations Computer-supported collaboration Corporate memory e-learning Enterprise content management Enterprise social software Expert system Intellectual Capital Knowledge Knowledge Ecosystems Knowledge base Knowledge management for development KM concepts KM Journals Knowledge Management System

Knowledge representation Knowledge transfer Knowledge visualization Meta-knowledge Morphological analysis Organizational learning Organizational empowerment Personal knowledge management Public sector knowledge management Procedural knowledge Sensemaking Semantic Web Social network Skills management Tacit knowledge & Explicit knowledge Value network

[edit] Further reading

• Allee, V.(1997) The Knowledge Evolution: Expanding Organizational Intelligence , Elsevier, ISBN 0-7506-9842-X.
• Allee, V (2003) The Future of Knowledge: Increasing Prosperity through Value Networks, Elsevier ISBN 0-7506-7591-8.
• Bhagat, P. M. (2005), Pattern Recognition in Industry, Elsevier, ISBN 0-08-044538-1.
• Boisot, M. (1998), Knowledge Assets, Oxford, ISBN 0-19-829086-1.
• Bontis, N. (2002), World Congress on Intellectual Capital Readings, Elsevier Butterworth-Heinemann , ISBN 0-7506-7475-X.
• Buckman, R. H. (2004), Building a Knowledge-Driven Organization, McGraw Hill, ISBN 0-07-138471-5.
• Bray, D. (2006). Exploration, Exploitation, and Knowledge Management Strategies in Multi-Tier Hierarchical Organizations Experiencing Environmental Turbulence, North American Assoc. for Computational Social and Organizational Science (NAACSOS) Conference, June 2006. Article available on SSRN
• Callaghan, J. (2002), Inside Intranets & Extranets: Knowledge Management and the Struggle for Power, Palgrave Macmillan, ISBN 0-333-98743-8.
• Choo, C. & Bontis, N. (2002), The Strategic Management of Intellectual Capital and Organizational Knowledge , Oxford University Press, ISBN 0-19-513866-X.
• Clare, M. and Detore A. (2000), Knowledge Assets Professional’s Guide to Valuation and Financial Management, Apsen Publishers, ISBN 0-15-607000-6.
• Collison, C. & Parcell, G (2004), Learning to Fly – Practical Knowledge Management From Leading and Learning Organizations, Capstone Publishing, ISBN 1-84112-509-1
• Cross, R. and Parker, A. (2004), The Hidden Power Of Social Networks, Harvard Business School Press, Boston, Mass, ISBN 1-59139-270-5.
• Davenport, T. and Prusak, L. (1997), Working Knowledge, Harvard 1998, ISBN 0-87584-655-6.
• Desouza, K.C. (2005),New Frontiers of Knowledge Management,Palgrave Macmillan, ISBN 1-40394-240-4
• Desouza, K.C. and Awazu, Y. (2005), Engaged Knowledge Management: Engagement with New Realities, Palgrave Macmillan, ISBN 1-40394-510-1.
• Drucker P. F., D. Garvin, D. Leonard, S. Straus and J. S. Brown (1998), Harvard Business Review on Knowledge Management, HBS Press, ISBN 0-87584-881-8.
• Edvinsson, L. and Malone, M. (1997), Intellectual Capital: Realising Your Company’s True Value by Finding its Hidden Brainpower. New York: HarperBusiness, ISBN 0-88730-841-4.
• Dixon, N. M. (2000), Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press, Boston, MA, ISBN 0-87584-904-0.
• Becerra-Fernandez, I., A. González and R. Sabherwal (2004), Knowledge Management: Challengers, Solutions and Technologies, ISBN 0-13-101606-7.
• Garvin, D. A. (2000), Learning in Action: A Guide to Putting the Learning Organization to Work, Harvard Business School Press, Boston, MA, ISBN 1-57851-251-4.
• Easterby-Smith, M. and M. A. Lyles (editors). (2003). The Blackwell Handbook of Organizational Learning and Knowledge Management, Oxford, Blackwell Publishing, ISBN 0-631-22672-9.
• Malhotra, Y. (1996-2007) A Case For Knowledge Management: Rethinking Management for the New World of Uncertainty and Risk, BRINT Institute, New Hartford, NY. Online at http://www.kmbook.com/.
• Malhotra, Y. (2000), Knowledge Management and Virtual Organizations, Idea Group Publishing, Hershey, PA, ISBN 1-878289-73-X.
• Malhotra, Y. (2001), Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey, PA, ISBN 1-878289-98-5.
• Nonaka, I. and Takeushi, H. (1995), The Knowledge-Creating Company, New York: Oxford University Press.
• Frid, R. (2004), Frid Framework for Enterprise Knowledge Management: A Common KM Framework for the Government of Canada, IUniverse Publishing, ISBN 0-595-30699-3.
• O’Dell, C. and C. J. Grayson Jr. (1998), If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice, Free Press, New York, ISBN 0-684-84474-5.
• O’Sullivan, K. J. (2007), “Strategic Knowledge Management in Multinational Organizations” Idea Group Publishing, Hershey PA. ISBN 1-59904-633-4
• Polanyi, M. (1967), The Tacit Dimension, Doubleday, Garden City, NY, ISBN 0-385-06988-X.
• Rumizen, M. C. (2001), Complete Idiot’s Guide to Knowledge Management, Alpha, ISBN 0-02-864177-9.
• Schwartz, D, editor (2005), Encyclopedia of Knowledge Management, Idea Group Reference, ISBN 1-59140-574-2.
• Stankosky, M., editor (2004), Creating the Discipline of Knowledge Management: The Latest in University Research, Butterworth-Heinemann, ISBN 0-7506-7878-X
• Sveiby, K. E. (1997), The New Organizational Wealth: Managing & Measuring Knowledge-Based Assets, Berrett-Koehler, ISBN 1-57675-014-0.
• Suresh, J. K. and Mahesh, K. (2006), Ten Steps to Maturity in Knowledge Management: Lessons in Economy, Chandos, Oxford, UK, ISBN 1-84334-130-1.
• Stewart, T. (1997) Intellectual Capital: The New Wealth of Organisations, New York: Doubleday, ISBN 0-385-48228-0.
• Tiwana, A. (2002), The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002, ISBN 0-13-009224-X.
• United Nations (2003), Expanding Public Space for the Development of the Knowledge Society, Report of the Ad Hoc Expert Group Meeting on Knowledge Systems for Development, 4-5 September 2003, United Nations Department of Economic & Social Affairs, United Nations, New York, 2003, PDF: http://unpan1.un.org/intradoc/groups/public/documents/UN/UNPAN014138.pdf
• Wissensmanagement Forum (Hg.): An Illustrated Guide to Knowledge Management, Graz 2002, URL: http://www.wm-forum.org Download PDF Version

[edit] Articles

• Alavi, M. and Leidner, D. (2001). “Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,” MIS Quarterly, 25, 1, 107-136.
• Bellenger, Gene (2002) “Emerging Perspectives”, Systems Thinking Knowledge Management – Emerging Perspectives
• Bontis, N., Dragonetti, N., Jacobsen, K. and G. Roos. (1999) “The Knowledge Toolbox: A review of the tools available to measure and manage intangible resources”, European Management Journal, 17, 4, 391-402.
• Bontis, N. (1999). “Managing Organizational Knowledge by Diagnosing Intellectual Capital: Framing and advancing the state of the field”, International Journal of Technology Management,18, 5/6/7/8, 433-462.
• Bontis, N. (2002). “The rising star of the Chief Knowledge Officer”, Ivey Business Journal, March/April, 20-25.
• Cross, R., Parker, A., Prusak, L. and Borgatti, S.P. (2001), “Knowing what we know: supporting knowledge creation and sharing in social networks”, Organizational Dynamics Vol 30, No 2, pp. 100-120.
• Ekbia, H. and Hara, N. (2004) The Quality of Evidence in Knowledge Management Literature: the Guru Version. At http://www.slis.indiana.edu/research/working_papers/files/SLISWP-04-01.pdf
• Hamburg, Terstriep & Rehfeld (2006 Nov), “Knowledge-Based Services for Economic Agencies based on Internet Technologies”,Icfai Journal of Knowledge Management, Icfai University Press. Article available on SSRN
• Hansen, M. R., N. Nohria and T. Tierney (1999). ‘What’s your strategy for managing knowledge?’ Harvard Business Review (March-April).
• Huijsen, W., Driessen, S. J. and Jacobs, J. W. M. (2004a), “Explicit Conceptualizations for Knowledge Mapping”, Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Vol 3, pp. 231-236, Porto, April 2004.
• Knorr-Siedow, T. (2005) Knowledge management and enhanced policy application; in: Van Kempen, R. et alter: Restructuring large housing estates in Europe, Bristol, pp 321-341
• Malhotra, Y (2005) “Integrating Knowledge Management Technologies in Organizational Business Processes: Getting Real Time Enterprises to Deliver Real Business Performance”, Journal of Knowledge Management Vol . 9 no. 1 pp 7-28.
• Malhotra, Y (2000) “Knowledge Management for E-Business Performance: Advancing Information Strategy to “Internet Time”", Information Strategy: The Executive’s Journal Vol . 16 no. 4 pp 5-16.
• Markus, M. (2001) “Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success,” Journal of Management Information Systems, 18, 1, 57-93.
• Powell, J and Swart, J (2005) “This is what the fuss is about”- a systemic modeling for organizational knowing , Journal of Knowledge Management Vol . 9 no. 2 pp 45-58
• Powell, J and Swart, J (2005) “Men and Measures” – capturing knowledge requirement in firms through qualitative system modeling, Journal of Operational Research.
• Snowden, D J. “Complex Acts of Knowing: Paradox and Descriptive Self-Awareness.” Journal of Knowledge Management, Special Issue 6, no. 2 (2002): 100-11. [1]
• Swart, J (2006) “Intellectual Capital” : Disentangling an enigmatic concept, Journal of Intellectual Capital Vol 7 No 2 pp 136-159.
• Thomas, J. C., Kellogg, W.A., and Erickson, T. (2001) The Knowledge Management puzzle: Human and social factors in knowledge management. IBM Systems Journal, 40(4), 863-884.
• Vail III, E.F. (1999), “Mapping Organisational knowledge”, Knowledge Management Review, Vol 8, May/June, pp. 10-15.
• Wexler, M.N. (2001), “The who, what and why of knowledge mapping”, Journal of Knowledge Management, Vol 5, No 3, pp. 249-263
• Wilson, T.D. (2002) “The nonsense of ‘knowledge management’” Information Research, 8(1), paper no. 144 [Available at http://InformationR.net/ir/8-1/paper144.html]

[edit] External links

• Knowledge Management at the Open Directory Project

Knowledge Management—Emerging Perspectives

from: http://www.systems-thinking.org/kmgmt/kmgmt.htm

Yes, knowledge management is the hottest subject of the day. The question is: what is this activity called knowledge management, and why is it so important to each and every one of us? The following writings, articles, and links offer some emerging perspectives in response to these questions. As you read on, you can determine whether it all makes any sense or not.

Content

Developing a Context A Continuum An Example Extending the Concept Knowledge Management: Bah Humbug! The Value of Knowledge Management References

Developing a Context

Like water, this rising tide of data can be viewed as an abundant, vital and necessary resource. With enough preparation, we should be able to tap into that reservoir — and ride the wave — by utilizing new ways to channel raw data into meaningful information. That information, in turn, can then become the knowledge that leads to wisdom. Les Alberthal[alb95]

Before attempting to address the question of knowledge management, it’s probably appropriate to develop some perspective regarding this stuff called knowledge, which there seems to be such a desire to manage, really is. Consider this observation made by Neil Fleming[fle96] as a basis for thought relating to the following diagram.

• • A collection of data is not information.
  • A collection of information is not knowledge.
  • A collection of knowledge is not wisdom.
  • A collection of wisdom is not truth.

The idea is that information, knowledge, and wisdom are more than simply collections. Rather, the whole represents more than the sum of its parts and has a synergy of its own.

We begin with data, which is just a meaningless point in space and time, without reference to either space or time. It is like an event out of context, a letter out of context, a word out of context. The key concept here being “out of context.” And, since it is out of context, it is without a meaningful relation to anything else. When we encounter a piece of data, if it gets our attention at all, our first action is usually to attempt to find a way to attribute meaning to it. We do this by associating it with other things. If I see the number 5, I can immediately associate it with cardinal numbers and relate it to being greater than 4 and less than 6, whether this was implied by this particular instance or not. If I see a single word, such as “time,” there is a tendency to immediately form associations with previous contexts within which I have found “time” to be meaningful. This might be, “being on time,” “a stitch in time saves nine,” “time never stops,” etc. The implication here is that when there is no context, there is little or no meaning. So, we create context but, more often than not, that context is somewhat akin to conjecture, yet it fabricates meaning.

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[bat88], 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[sen90] 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 a 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.

An Example

This example uses 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.

Note: If the mechanics of this diagram are unfamiliar, you can find the basis in Systems Thinking Introduction[bel96] .

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.

A Continuum

Note that the sequence data -> information -> knowledge -> wisdom represents an emergent continuum. That is, although data is a discrete entity, the progression to information, to knowledge, and finally to wisdom does not occur in discrete stages of development. One progresses along the continuum as one’s understanding develops. Everything is relative, and one can have partial understanding of the relations that represent information, partial understanding of the patterns that represent knowledge, and partial understanding of the principles which are the foundation of wisdom. As the partial understanding stage.

Extending the Concept

We learn by connecting new information to patterns that we already understand. In doing so, we extend the patterns. So, in my effort to make sense of this continuum, I searched for something to connect it to that already made sense. And, I related it to Csikszentmihalyi’s interpretation of complexity.

Csikszentmihalyi[csi94] provides a definition of complexity based on the degree to which something is simultaneously differentiated and integrated. His point is that complexity evolves along a corridor and he provides some very interesting examples as to why complexity evolves. The diagram below indicates that what is more highly differentiated and integrated is more complex. While high levels of differentiation without integration promote the complicated, that which is highly integrated, without differentiation, produces mundane. And, it should be rather obvious from personal experience that we tend to avoid the complicated and are uninterested in the mundane. The complexity that exists between these two alternatives is the path we generally find most attractive.

On 4/27/05 Robert Lamb commented that Csikszentmihalyi’s labeling could be is bit clearer if “Differentiation” was replaced by “Many Components” and “Integration” was replaced by Highly Interconnected.” Robert also commented that “Common Sense” might be another label for “Mundane.” If the mundane is something we seem to avoid paying attention to then “Common Sense” might often be a very appropriate label. Thanks Robert.

What I found really interesting was the view that resulted when I dropped this diagram on top of the one at the beginning of this article. It seemed that “Integrated” and “Understanding” immediately correlated to each other. There was also a real awareness that “Context Independence” related to “Differentiated.” Overall, the continuum of data to wisdom seemed to correlate exactly to Csikszentmihalyi’s model of evolving complexity.

I now end up with a perception that wisdom is sort of simplified complexity.

Knowledge Management: Bah Humbug!

When I first became interested in knowledge as a concept, and then knowledge management, it was because of the connections I made between my system studies and the data, information, knowledge, and wisdom descriptions already stated. Saying that I became interested is a bit of an understatement as I’m generally either not interested or obsessed, and seldom anywhere in between. Then, after a couple months I managed to catch myself, with the help of Mike Davidson[dav96], as to the indirection I was pursuing.

I managed to survive the Formula Fifties, the Sensitive Sixties, the Strategic Seventies, and the Excellent Eighties to exist in the Nanosecond Nineties, and for a time I thought I was headed for the Learning Organizational Oh’s of the next decade. The misdirection I was caught 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 cooperating 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 predictably consistent.

I associate the cause of my indirection with the many companies I have been associated with in the past. These companies had pursued TQM or reengineering, not in support of what they were trying to accomplish, but as ends in themselves because they simply didn’t know what they were really trying to accomplish. And, since they didn’t know what they were really trying to accomplish, the misdirection was actually a relief, and pursued with a passion­­it just didn’t get them anywhere in particular.

According to Mike Davidson[dav96], and I agree with him, what’s really important is:

• Mission: What are we trying to accomplish?
• Competition: How do we gain a competitive edge?
• Performance: How do we deliver the results?
• Change: How do we cope with change?

As such, knowledge management, and everything else for that matter, is important only to the extent that it enhances an organization’s ability and capacity to deal with, and develop in, these four dimensions.

The Value of Knowledge Management

In an organizational context, data represents facts or values of results, and relations between data and other relations have the capacity to represent information. Patterns of relations of data and information and other patterns have the capacity to represent knowledge. For the representation to be of any utility it must be understood, and when understood the representation is information or knowledge to the one that understands. Yet, what is the real value of information and knowledge, and what does it mean to manage it?

Without associations we have little chance of understanding anything. We understand things based on the associations we are able to discern. If someone says that sales started at $100,000 per quarter and have been rising 20% per quarter for the last four quarters, I am somewhat confident that sales are now about $207,000 per quarter. I am confident because I know what “rising 20% per quarter” means and I can do the math.

Yet, if someone asks what sales are apt to be next quarter, I would have to say, “It depends!” I would have to say this because although I have data and information, I have no knowledge. This is a trap that many fall into, because they don’t understand that data doesn’t predict trends of data. What predicts trends of data is the activity that is responsible for the data. To be able to estimate the sales for next quarter, I would need information about the competition, market size, extent of market saturation, current backlog, customer satisfaction levels associated with current product delivery, current production capacity, the extent of capacity utilization, and a whole host of other things. When I was able to amass sufficient data and information to form a complete pattern that I understood, I would have knowledge, and would then be somewhat comfortable estimating the sales for next quarter. Anything less would be just fantasy!

In this example what needs to be managed to create value is the data that defines past results, the data and information associated with the organization, it’s market, it’s customers, and it’s competition, and the patterns which relate all these items to enable a reliable level of predictability of the future.What I would refer to as knowledge management would be the capture, retention, and reuse of the foundation for imparting an understanding of how all these pieces fit together and how to convey them meaningfully to some other person.

The value of Knowledge Management relates directly to the effectiveness[bel97a] with which the managed knowledge enables the members of the organization to deal with today’s situations and effectively envision and create their future. Without on-demand access to managed knowledge, every situation is addressed based on what the individual or group brings to the situation with them. With on-demand access to managed knowledge, every situation is addressed with the sum total of everything anyone in the organization has ever learned about a situation of a similar nature. Which approach would you perceive would make a more effective organization?[bel97b]

References

• Alberthal, Les. Remarks to the Financial Executives Institute, October 23, 1995, Dallas, TX
• Bateson, Gregory. Mind and Nature: A Necessary Unity, Bantam, 1988
• Bellinger, Gene. Systems Thinking: An Operational Perspective of the Universe
• Bellinger, Gene. The Effective Organization
• Bellinger, Gene. The Knowledge Centered Organization
• Csikszentmihalyi, Miahly. The Evolving-Self: A Psychology for the Third Millennium, Harperperennial Library, 1994.
• Davidson, Mike. The Transformation of Management, Butterworth-Heinemann, 1996.
• Fleming, Neil. Coping with a Revolution: Will the Internet Change Learning?, Lincoln University, Canterbury, New Zealand
• Senge, Peter. The Fifth Discipline: The Art & Practice of the Learning Organization, Doubleday-Currency, 1990.

theWay of Systems * Feedback * Musings
Copyright © 2004 Gene Bellinger

Whois

• http://www.whois.net
• http://www.who.is
• http://www.networksolutions.com/whois/index.jsp

http://www.whois.net/whois_new.cgi?d=ntfp&tld=org

[whois.publicinterestregistry.net]
NOTICE: Access to .ORG WHOIS information is provided to assist persons in
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Domain ID:D2728258-LROR
Domain Name:NTFP.ORG
Created On:11-Dec-1998 05:00:00 UTC
Last Updated On:16-Apr-2006 00:29:40 UTC
Expiration Date:10-Dec-2007 05:00:00 UTC
Sponsoring Registrar:Dotregistrar, LLC (R114-LROR)
Status:CLIENT TRANSFER PROHIBITED
Registrant ID:1379378-R
Registrant Name:i4 Asia Incorporated
Registrant Street1:20th Floor, Strata 100 Building
Registrant Street2:Ortigas Center
Registrant Street3:
Registrant City:Pasig City
Registrant State/Province:Metro Manila
Registrant Postal Code:1605
Registrant Country:PH
Registrant Phone:+1.6312718
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Registrant Email:admin@i4asiacorp.com
Admin ID:1379378-A
Admin Name:Dexter Ang
Admin Street1:20th Floor, Strata 100 Building
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Admin Street3:
Admin City:Pasig City
Admin State/Province:Metro Manila
Admin Postal Code:1605
Admin Country:PH
Admin Phone:+1.6312718
Admin Phone Ext.:
Admin FAX:
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Admin Email:admin@i4asiacorp.com
Admin ID:31135100-NSI
Admin Name:Easynet Nederland B.V.
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Admin Street1:Joop Geesinkweg 220
Admin Street2:
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Admin Postal Code:1096 av
Admin Country:NL
Admin Phone:+1.31207989898
Admin Phone Ext.:
Admin FAX:+1.31207989899
Admin FAX Ext.:
Admin Email:registry@nl.easynet.net
Tech ID:31136863-NSI
Tech Name:Support Easynet Nederland B.V.
Tech Organization:Support Easynet Nederland B.V.
Tech Street1:Joop Geesinkweg 220
Tech Street2:
Tech Street3:
Tech City:Amsterdam
Tech State/Province:NH
Tech Postal Code:1096 av
Tech Country:NL
Tech Phone:+1.31207989898
Tech Phone Ext.:
Tech FAX:+1.31207989899
Tech FAX Ext.:
Tech Email:support@nl.easynet.net
Tech ID:1379378-T
Tech Name:Dexter Ang
Tech Street1:20th Floor, Strata 100 Building
Tech Street2:Ortigas Center
Tech Street3:
Tech City:Pasig City
Tech State/Province:Metro Manila
Tech Postal Code:1605
Tech Country:PH
Tech Phone:+1.6312718
Tech Phone Ext.:
Tech FAX:
Tech FAX Ext.:
Tech Email:admin@i4asiacorp.com
Name Server:NS01.I4ASIACORP.COM
Name Server:NS02.I4ASIACORP.COM
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
Name Server:
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—–

http://www.who.is/whois-org/ip-address/ntfp.org/

Domain Name: ntfp.org

Status: CLIENT TRANSFER PROHIBITED

Registrar: Dotregistrar, LLC (R114-LROR)

Expiration Date: 2007-12-10 05:00:00
Creation Date: 1998-12-11 05:00:00
Last Update Date: 2006-04-16 00:29:40

Name Servers:
ns01.i4asiacorp.com
ns02.i4asiacorp.com

P Address: 70.85.248.194
Website Status: active
Server Type: Apache/1.3.37 (Unix) mod_auth_passthrough/1.8 mod_log_bytes/1.2 mod_bwlimited/1.4 PHP/4.4.4 FrontPage/5.0.2.2635.SR1.2 mod_ssl/2.8.28 OpenSSL/0.9.7a
Cache Date: 2007-05-22 11:12:12 MST

——

IP Address:		70.85.248.194 (ARIN & RIPE IP search)
IP Location:		US(UNITED STATES)-TEXAS-DALLAS
Record Type:		Domain Name
Server Type:		Apache 1
Web Site Status:		Active
DMOZ		no listings
Y! Directory:		see listings
Web Site Title:		Non-Timber Forest Products Exchange Programme
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Data as of:		21-Jul-2006

2 books i want

Knowledge Management: A State of the Art Guide By Paul R. Gamble, John Blackwell

Table of Contents	v
Drivers of the …	25
Practice orient…	41
Process orienta…	56
Communities of …	73
Transparent mar…	100

Knowledge Management: Processes and Technologies By Herwig Rollett

 

able of Contents
Introduction	3
Two Complementa…	9
Lessons Learned…	19
Processes	20
Knowledge Planning	35

Important questions visitor tracking can answer

From: http://www.opentracker.net/en/articles/website-management.jsp

Below, we have drawn up a list of what we feel to be 10 important questions that you can answer using your website statistics. The answers will help you to make website content management decisions.

1. Where do your visitors come from, where do they go, what do they look at, and what pages do they exit from? What do the clickstreams tell you?
2. Where do people start and stop viewing, where do they lose interest?
3. How sticky is your site? Overall site and page stickiness is important. Do you have problematic leaks or drop-off points?
4. In other words, how good is your site navigability and usability?
5. Which referrers and PPC advertising campaigns are the most effective? Are you paying for ineffective traffic? Are you using the best search terms possible? Determine which advertising campaigns are most effective and concentrate your investment strategy there.
6. What are your conversion and retention rates?
7. When you make changes to your site, what is the effect? If you are trying to get people’s attention, it is good to know how they react.
8. What is the best time of week to start an email campaign or newsletter.
9. Are your visitors looking for something you don’t sell? Perhaps you should consider selling it!
10. Are your customers as satisfied as they can be?

Use your statistics for site management and decision-making

Your statistics are not only numbers. They are numbers that should help you to make actual site management decisions. Make changes to your website where necessary based on what your visitors are doing. If everybody leaves from one page, ask yourself why. If visitors are not surfing to the page you want them to see, make better links.

In conclusion, and to repeat the essential point of this article: the best way to make use of the data is to ask yourself questions about your statistics reports and to answer them. This process itself is valuable because it will tell you what information is being collected and lead you to ask important questions about

a) why the information is collected, and
b) what to do with it

Why web usage statistics are (worse than) meaningless

From: http://www.goldmark.org/netrants/webstats/

Why web usage statistics are (worse than) meaningless

Note: This document was originally written in 1995 to explain the stats situation at Cranfield University where I worked at the Cranfield Computer Centre. It was initially intended for local users there, but quickly gained popularity (notoriaty?) elsewhere. The content has had few changes and updates since then.

1. There is no discussion of cookie tracking (yet) in this document.
2. There is no discussion of the very similar problem of guessing web browser popularity from webserver logs.
3. I had over-estimated the extent to which caching (and hierarchical caching) would be used.
4. Cranfield University has a proud history of leadership in the web. It was one of the very first UK sites to have a webserver at all (in 1993), was at the forefront of the UK caching effort, and in enabling individual users to publish on the web (early 1994). I am grateful that they permitted what was essentially my personal rant to be hosted so prominantly for so long, and have provided a long term redirect to this page.
5. On re-reading my original, I see that this document is a bit hyperbolic. So be it. It is after all an acknowledged rant.

Web usage statistics, such as those produced by programs such as analog cannot be used to make strong inferences about the number of people who have read a website or webpage. Although those who compile these statistics usually try to make this clear, people still insist on misusing them to make overly strong inferences. Attaching meaning to meaningless numbers is worse than not having the numbers at all. When you lack information, it is best to know that you lack the information. Web statistics may give the user a false sense of knowledge which can be worse than being knowingly ignorant. A useful analogy is with putting up advertising posters. You will never really know how many people have noticed them or read them.

It is not enough to say that the statistics should be taken with a grain of salt; they should be taken with a salt lick. If you want to understand why no inference about the number of people reading your pages can be made from web statistics read on. Otherwise, you may wish to just trust that statement or may wish to skip to the section on Quick Questions and Answers.

What web stats are really good for?

Web stats are useful for web administrators to get a sense of the actual load on the server. This is useful for diagnostics and planning, and for detecting unusual behaviour that may require planning action. The goal of the administrator is to keep the server running smoothly under expected loads, while improving the speed and reliability of obtaining documents from the site. The best way to achieve this is to have browsers retrieve documents from places closer to where they will be used (and even from memory) than to get them from the disk on the server. It is only when the file is retrieved from the server that the server has the ability to keep track of the access.

Caching:
Essential for the web and disastrous for statistics

Let’s take a fictitious example of what might happen when someone in Nome, Alaska, say at Nome Community College (this would be a polytechnic in the UK), wants to read Cranfield’s Prospectus. The user would somehow select the URL with his/her browser, which would then try the following.

Browser Cache

The particular instance of the browser will look in its own memory (or what it may have saved on the its local disk). If it finds the page corresponding to the sought for URL there it will not go any further, and our site will never know that the request was made.

Local site cache

If the page was not in the browser cache, the browser may look to its site cache. That is, if someone at the user’s same site recently retrieved the page, it may be available to the user there. If it finds the page corresponding to the sought for URL there it will not go any further, and our site will never know that the request was made.

Local regional cache

The site cache may be configured to look in a local regional cache, say at the University of Alaska, Nome campus which might provide a caching service for smaller sites around Nome. If it finds the page corresponding to the sought for URL there it will not go any further, and our site will never know that the request was made.

Large regional cache

The local regional cache may be configured to look in a large regional cache, say in Fairbanks Alaska, which might provide caching for sites in Alaska that use it. If it finds the page corresponding to the sought for URL there it will not go any further, and our site will never know that the request was made.

The Cranfield accelerator

An accelerator is an out-going cache for a site. When a document is requested from the site, the accelerator sees whether it has it stored (it stores them in ways much faster to find and retrieve then the server does with files in the directory structure) and serves that up. While it would be possible to have the accelerator keep a record of which files it served up and to whom, this would defeat the purpose, because it would require a disk operation to make that record.

In addition to over-estimating the degree of caching that would be in place, this last step about accelerators is also no longer relevant. The accelerator was needed when Cranfield was running the original CERN server over an AFS filesystem. Given the nature of modern web server set-ups, accelerators are no longer needed.

Now that you have an idea of what caching is, you are in a better position to understand why it is impossible to make any inference about numbers of people reading your pages from web statistics. But there is more to come described in the section on multiple hits per users. What is necessary to understand about caching is that some users may go through a long and efficient cache chain (as described in the example) and other users may not. Much of this depends on how their site is set up or how they set things up themselves.

One user many hits

Imagine (in the extreme case) a user who is doing no caching whatsoever. Now if that user comes across the Cranfield Home Page 20 times while browsing around the Cranfield pages that will count as 20 hits. Remember the statistics are about accesses, not about people.

Big pages are little pages

When comparing hits for different directories, it is important to note how documents are structured. If you have a directory with a single document on one hand, and on the other you have another directory with the same amount of real content broken in to twenty smaller documents, you will find far more hits into that second section.

Quick Questions and Answers about web statistics

Most of everything listed here is either mentioned above or can be inferred from the explanations above. If there is a question that you would like to see added to this list, or if you have other comments on this document, please use the form at the end to submit queries. [Sorry, that form is now defunct.] A quick list of the questions is provided here.

• Can stats be used to assess changes over time?
• Can stats be used to assess relative popularity in different Internet domains such as .ac.uk, or .jp?
• Can stats be used to assess relative popularity of different pages?
• Is there some multiplier which can applied to the stats to get more meaningful results?
• Can I ensure that my document is never cached?
• Can I put counters in my page?
• Can we get stats from the sites that do caching?
• Can I infer from stats a minimum number of readers?
• How can I gauge interest in pages?
• If web stats are so bad why are they kept at all?
• Then why make the stats public?
• Is this all just an excuse to avoid the work of maintaining stats?

Can stats be used to assess changes over time?

Not really. The number of individuals and sites using caches is rising all the time, as is the amount of disk space and memory used for caching. When the Cranfield Accelerator goes live (early November, 1995), there should be an actual drop in our server stats, while an increase in accesses, due to increased speed and reliability of the server. Caching has been on the rise for more than a year now. Even so, loads on systems (including ours) have gone up dramatically.

Can stats be used to assess relative popularity in different Internet domains such as .ac.uk, or .jp?

Unfortunately not even this is possible. Suppose for example that Japan has a very high level of regional and national caching while Singapore does not (the example is fictitious). Under these circumstances, web statistics might show more accesses from Singapore than from Japan even if more people in Japan read our pages. A clear example of this is the number of accesses from “numerical domains” that have recently started to top various lists. These are accesses from sites that don’t have proper reverse DNS listings. Such sites are probably misconfigured single user machines, where either the particular machine that is used in misconfigured or the organisation they belong to has not straightened out its machine names properly. It is reasonable to assume that those running such misconfigured systems are far more likely to not have configured their proxies correctly, so far less caching will be seen from those sites.

Can stats be used to assess relative popularity of different pages?

Not really. The more popular pages will cache more, meaning that real differences between page hits will be dramatically distorted. It is probably safe to say this if one page shows more hits then another that there really were more accesses to that page, but there are circumstances under which even that weak inference won’t be true.

Is there some multiplier which can applied to the stats to get more meaningful results?

Not really. This is because any such multiplier would have to differ from page to page and differ from access region to access region.

Can I ensure that my document is never cached?

Yes you can. There are several ways to do so, and there are some circumstances for which it is even legitimate, but to do so merely to get better stats is seriously misguided. This is for two reasons:

1. You will make your page (much) harder for people to get to and add to network traffic unnecessarily.
2. If someone fails to reach your page at our site, they may give up on the site all together. Thus hard to get at pages (unless there is a clear reason for them being such) will be unfair to other providers at the site.

Quiet embarrassingly, many of the pages on this site don’t normally cache properly. This is because I had some technical difficulties with my configuration of server side includes and the so-called “XBitHack”. I’ve fixed that now, but now have to fix dozens of documents to use things properly.

Can I put counters in my page?

You may have noticed some pages with web counters. There are basically two ways to put them in your page: the wrong way and the very wrong way. The wrong way merely doesn’t work and will not be more useful than normal statistics. The very wrong way is counter productive because it subverts the caching mechanism which is not a good idea just to get statistics. Please note that even if you think that statistics can be made useful, counters on individual pages are displayed to the reader, who isn’t in the position to make the various adjustments needed to get some sense of true readership.

Can we get stats from the sites that do caching?

Yes and no, but mostly no. There are two reasons for “mostly no”. One is simply that there are too many small caches out there which may have cached our stuff (including the browser software internal cache). Clearly not all of these are going to send us records on a regular basis which we would then have to incorporate into all of the other records to process statistics. The other reason for “mostly no” is that even the large caches are willing to only send a byte count. That is, one major UK cache is considering sending out on a monthly basis how many bytes of data they served up in our name.

We must remember that the caches are doing us a favour by making our pages much easier to reach. We cannot ask them to take on a task that would degrade the service or place an additional administrative, disk, memory and CPU load on them. Without caching, the web would have collapsed long ago.

Can I infer from stats a minimum number of readers?

Yes and no. If by minimum you mean “at least one” then yes. If you have 400 hits from Japan then you can conclude that during that period you had at least one reader from Japan. You cannot infer that there were at least 400 readers, because the same reader may hit a page many times in a short period of time. So, the only certain inference that can be made is that there was at least one from a particular domain, or for a particular page.

How can I gauge interest in pages?

One way is to set up Mail Reply Forms in your pages like the one at the end of this document. Of course many more people will read your pages than will complete the form, but the form can be used to judge serious interest. Most people will, however, not fill out a form unless they think they will get some sort of useful response, even if they read the document seriously. (Did you fill out the form for this document?) Setting up these forms is not as difficult to do as it first appears, and courses are offered on it by the computing centre staff.

If web stats are so bad why are they kept at all?

They are useful for system administrators to judge the actual load on the server. The section on what stats are good for contains more information.

Then why make the stats public?

Popular demand. It is not the computer centre’s job to deny users some service just because we know the request to be misguided. Attempts to eliminate these statistics from the system met with complaints. However, no great effort will be put into maintaining statistics or access to them either. It is hoped that this document will make it easier for the computer centre to withdraw statistics altogether, except for what is required for system maintenance.

Is this all just an excuse to avoid the work of maintaining stats?

No. But you may have noticed that many of the individual problems and difficulties could be partially mitigated by collecting and using more information (from some caches for example or times of requests) and using that to make very rough estimates of various correction factors. It would take serious statistic analysis of the sort that professional market research firms may be able to undertake and still the estimates (and relative hits on pages or from regions) would remain iffy. Performing complicated analyses on dubious data only compounds the problem, and the marginal utility would be negative (ie, the large amount of extra effort would not be justified by the tiny gain in meaningfulness of the statistics).

Differences between tracking unique visitors and log analyzers

From: http://www.opentracker.net/en/articles/tracking-vs-logfile-analysis.jsp

Summarized overview

In this article you will find technical definitions of:

• Unique visitor tracking
• Log analysis
• Human events

You will also find information about:

• The difference between unique visitor tracking and log analysis
• Why log analyzers show higher numbers
• The difference between browser events and server events
• Tracking unique visitors from behind corporate firewalls and ISPs
• Advantages of using cookies to track unique visitors
• Measuring page views instead of hits
• Tracking spiders and bots
• Opentracker specialization in human events and unique visitor behavior

Human events versus server activity

Why do tracking services show a lower number of visitors than statistics recorded by log analyzers? The answer lies in the difference between unique visitor tracking and log analyzing. Log analyzers record all measurable activity, whereas tracking services distinguish between human activity and server activity.

Tracking service stats will show lower numbers than log analyzer stats. This is not because tracking services record fewer visitors. The reason is that tracking services are stricter in their definitions of a visitor. A tracking service should do its best to ensure that no visitor is recorded twice, and that only human clicks are counted as visits.

The reason that tracking services will report lower traffic numbers than log files is because good tracking services do not recognize the following factors as unique visits or human events:

• repeat unique visitors (after 24 hours)
• hits
• robot and spider traffic
• rotating IP numbers (i.e. AOL)

Equally important is the ability to distinguish how many unique visitors are visiting from either:

• the same ISP (Earthlink, At&t, Comcast, Cox, etc.)
• corporate firewalls, large organizations (Microsoft, IBM, Apple, etc.)

Otherwise all these users will be counted as the same visitor. This is a differentiation which can only be made by tracking cookies.

Where possible, tracking systems should only measure human events.

For years now, the standard measurement of website traffic on the internet has been ‘hits’. Hits are not a reliable indicator of website traffic. A hit is a single request from a browser to a server. When a visitor looks at a single page, many hits can be generated, both for the request itself, and for each component of the page.

Opentracker measures page views, not hits

Opentracker tracks page views. A page view is a single human event. A page view is also known as an impression. Each impression, or page view, represents an actual person who has viewed a specific web page. In this way, Opentracker differentiates between human events, and server-browser dialogues.

Opentracker specializes in human events and visitor behavior.

Opentracker tracks visitors over the long-term, and has the ability to recognize if a visitor has been at a site before. Opentracker uses browser cookies to track unique visitors over long periods of time. Examining a unique visitor’s clickstream, for example, can tell you how quickly new users adjust to site layout.

Drawbacks: we can miss visitors, in the event that a visitor clicks too quickly, i.e. does not wait for a page to load.

Case study

Example of a discrepancy between Opentracker and log analyzer

Log analyzers do not distinguish between humans, and spiders or bots. Spiders and bots are the devices sent out by search engines to scour and document all pages on the internet. This means that a log analyzer might record an extra several hundred visits for a given period, depending on the popularity of your site. The more popular that your site is, the more often it will be visited by search engine robots. This is especially true if your content is frequently updated.

Related reading:

• Clickstream analysis
• The difference between hits, visitors, and page views
• How does user tracking work?
• The difference between Search-engine spiders and visitors
• Why web usage statistics are (worse than) meaningless

The difference between hits, visitors, visits, and page views

From: http://www.opentracker.net/en/articles/hits-visitors-pageviews.jsp

Summary

In this article you will find discussion and technical definitions of:

• Hits, visitors and page views
• Unique visitors
• New and returning visitors

And information about:

• Why hits are not a good way to measure traffic
• The difference between server hits & hit counters
• Tracking unique visitors
• The difference between new & returning visitors

Hits, visitors, visits, page views: what are the differences?

Technical definition of a hit

each file sent to a browser by a web server is an individual hit.

Technical definition of a page view

a page view is each time a visitor views a webpage on your site, irrespective of how many hits are generated. Pages are comprised of files. Every image in a page is a separate file. When a visitor looks at a page (i.e. a page view), they may see numerous images, graphics, pictures etc. and generate multiple hits.

For example, if you have a page with 10 pictures, then a request to a server to view that page generates 11 hits (10 for the pictures, and one for the html file). A page view can contain hundreds of hits. This is the reason that we measure page views and not hits.

Hits are not a reliable way to measure website traffic.

Additionally, there is a high potential for confusion here, because there are two types of ‘hits’. The hits we are discussing in this article are the hits recorded by log files, and interpreted by log analysis. A second type of ‘hits’ are counted and displayed by a simple hit counter. Hit counters record one hit for every time a webpage is viewed, also problematic because it does not distinguish unique visitors.
Here is an article discussing hit counters.

Technical definition of a visit

a visit happens when someone or something (robot) visits your site. It consists of one or more page views/ hits. One visitor can have many visits to your site.

Technical definition of a visitor

a visitor is the browser of a person who accepts a cookie. Opentracker tracks cookies through a javascript. By this definition, a visitor is a human being, and their actions are ‘human’ events, because only humans use javascript to navigate the internet. If a cookie is not accepted, then we use IP numbers to track visitors.

Opentracker measures unique visitors, which we track over long periods of time by giving them a cookie, this cookie is unique to their browser. We have found that cookies are often more reliable over the long term, as many servers re-assign IP addresses on a regular basis. IP usage patterns are changing. AOL, for example, has recently implemented a rotating IP address technology, to stop log files from tracking their members’ search term queries.

How reliable are cookies when tracking unique visitors? Unless the user deletes their cookies continuously, they will be measured as the same visitor with each visit.

Strictly speaking, “one visitor” means “one person” based on the definitions given above. So that if someone continuously visits your site over long periods of time, they will be recorded only as one visitor.

How does Opentracker distinguish between new and returning visitors?

1. A returning visitor is a visitor who visits your site with a 24 hour period in between. It’s very strict.
2. Secondly, we measure visits, a visit is a visitor’s click-stream broken by a ten minute period, (minimum of ten minutes). So you have a cup of coffee, and return to the site after ten minutes, this will be a second visit. Say you go to bed, and you return to the site 24 hours later; you will be a returning visitor.

Additional reading:

• The difference between visitor tracking and log analyzers
• Internet Myth #4: I Need Lots of Hits on My Website

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