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Tools for Knowledge Management

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Tools for Knowledge Management

Anthony K.P. Wensley

    Associate Professor of Information Systems

    Executive Editor, Knowledge and Process Management

    Joseph L. Rotman School of Management

    University of Toronto

    Toronto, Ontario, CANADA

Alison Verwijk-O’Sullivan

    Research Associate

wensley@home.com

Introduction

    I have deliberately drawn a wide net in the following discussion of tools for knowledge management. In the first part of the paper I could have restricted myself to only looking at information technology tools. However, although these tools are becoming increasingly important to many of the stages of knowledge management they are by no means the only tools available. I feel that it is important to investigate many of the issues surrounding the use of tools in knowledge management using a more general view of tools. This will afford me the opportunity of investigating the nature of knowledge management tools in general before focusing on some of the information technology tools that are available.

    Another reason why I have chosen to first look at the nature of the tools that are available in the field of knowledge management is that there are a vast number of information technology tools available that can potentially support knowledge management and they are constantly being extended and augmented. Rather than attempt to create an exhaustive catalogue of these tools my research assistant has created a list of some of the Web-based information technology tools that can be used in knowledge management.

    The following paper is divided into two distinct sections. In the first section I will discuss knowledge management and the nature of the tools that we may have at our disposal to manage knowledge. The second section of the paper presents an annotated list of some of the Web-based information technology tools that are available. As I have indicated above this list is necessarily incomplete but I hope that it will

give the reader some grasp of the range of tools available. I would also encourage the reader to undertake a

    voyage through the many Web sites that are referenced. There is really nothing to replace first hand

    experience (?) of these tools. In the opinion of many the Web will, in the coming years, present us with a

    rich set of new tools for managing knowledge. As I indicate below this will primarily arise from two

    characteristics of the Web. First, it provides an extremely rich common language for representing

    knowledge we have only just begun to explore the true nature of this richness. Second, it is an intensely interactive medium allowing for the sharing and cooperative development of knowledge. But more of

    these issues later.

It is interesting to note that much of the work in Web-based knowledge management tools derives

    considerable strength from what have been fairly mainstream research and applications in Artificial

    Intelligence. This relationship is likely to broaden and deepen in future years as I will indicate in more

    detail below.

    Knowledge Management Many researchers in the field of knowledge management seem to think that the field sprang into existence

    de novo a few years ago. This is demonstrated by a delightful attribution of the definition of knowledge as ‗justified true belief‘ to Nonaka when, in fact, such a definition, though not in precisely the same words,

    1was provided by Plato in the Socratic dialogues! Hubris of this order may be attributed to a number of

    factors. In the first place, knowledge management as a concept seems to have taken flight from the ashes

    of business process reengineering and a variety of other ideas first promulgated by management consulting

    firms. Newness and originality are often ascribed to old concepts on the belief that one can charge higher

    fees as a result! Second, information technology has given us data management, information management

    and now, logically, knowledge management. Information technology, from this perspective, created the

    opportunity for ‗really‘ managing knowledge using information technology.

     1 Though I would note that there are a variety of researchers who have corrected this oversight in recent

    Knowledge Management books and papers.

    Having unfairly set up to straw men (persons?) let me set them aflame in good pagan fashion. Is knowledge management a new phenomenon? No. Although I certainly see ways in which knowledge management can be seen to have been born of such movements as business process management, customer orientation and the like, it is certainly not a new phenomenon. Further, with respect to the part that information technology has to play its existence is neither a necessary or sufficient condition for knowledge to be managed.

    As I hope will be indicated in this paper information technology and the tools it provides can certainly support some aspects of knowledge management but knowledge management does not begin and end with information technology. I concede that data management probably does begin and end with information technology. In many ways information technology created the notion of data today. It allowed for the reduction of information into data and thus it would seem relatively uncontentious to argue that data management is only really possible with information technology. However, I certainly do not believe that this is true for information let alone knowledge.

    Knowledge management has to do with the management of all stages in the generation, codification, refinement and transmission of knowledge. To the extent that I have any unique perspective in this area it is as a researcher who has been intimately involved in creating and codifying knowledge in specialist domains for at least a decade and a half. Although I am no longer directly involved in such creation and codification my research is now directed to many of the issues that arose during this decade and a half of prior research.

    I have stated that the stages of knowledge management are generation, codification, refinement and transmission. What is involved in each of these stages? Ruggles (1997, p. 1) elaborates on the stage of knowledge generation as follows (p. 2)

    "Knowledge generation includes all activities which bring to light knowledge which is "new,"

    whether to the individual, to the group, or to the world. It includes activities such as creation,

    acquisition, synthesis, fusion, and adaptation."

Similarly, he expands on the concept of knowledge codification as follows (1997, p.2):

    "Knowledge codification is the capture and representation of knowledge so that it can be re-used

    either by an individual or by an organization."

Different domains use different approaches to the codification of knowledge and knowledge workers in the

    same domain may adopt different approaches to codification at the same time. Codification may involve

    conceptual analysis, mathematical modeling and the development of restricted languages for structuring

    and communicating knowledge. I will have more to say about codification below but it is worth noting that

    Information Technology has provided some new twists to codification from efficient implementations of

    proofs in First Order Predicate Logic (FOPL) to a variety of data analysis and visualization tools.

Finally, Ruggles defines knowledge transfer (1997, p.2) as follows:

    "Knowledge transfer involves the movement of knowledge from one location to another and its

    subsequent absorption."

When knowledge is transferred it is seldom transferred complete with all the details of its codification -

    indeed, it may be incoherent to state that knowledge could be transmitted so completely. In transferring

    knowledge there is an implied context - this context will relate to the way in which the knowledge was

    codified and how such codification should be interpreted.

Ruggles further notes (1997, p.2) that:

    "Generation, codification, and transfer all occur constantly, so management itself does not create

    these actions. The power of knowledge management is in allowing organizations to explicitly

    enable and enhance the productivity of these activities and to leverage their value for the group as

    well as for the individual."

With respect to a definition of knowledge management tools (1997, p.3):

    "Knowledge management tools are technologies, broadly defined, which enhance and enable

    knowledge generation, codification, and transfer."

Having created a very large canvas I will now proceed to examine a small portion of it. I will investigate

    how technology can be used to facilitate each stage of knowledge management. But first, I would like to

    talk a little about technologies and tools.

A distinction is often made between technologies and methodologies. I shall consider here that a

    technology is some human construct or artifact that potentially can enhance and enable human activities.

    Typically the way in which a technology is used is directed by some methodology a set of ways of

    interacting with the technology. Thus, many impressive tools exist to assist in medical diagnosis, for

    instance MRI scanners. In and of themselves these tools are inert, they do not play any part in knowledge

    management. It is only when these tools are used in certain defined ways by communities of trained

    individuals who are able to communicate with each other that the can assist in knowledge management. In

    this context, a tool is one aspect of a technology that is typically used to achieve some specific purpose or

    related set of purposes.

Methods, Tools and Contexts

    I think that it is appropriate to observe that mankind has, over the millennia, developed many different

    approaches to knowledge and knowledge management that have informed and been informed by both

    methodologies and technologies. These approaches are typically embedded in what Wittgenstein referred

    to as ‗forms of life.‘ As Collins notes (1997, p. 148):

    "If, so much knowledge rests upon agreements within forms of life, what is happening when

    knowledge is transferred via bits of paper or floppy disks? We know that much less is transferred

    this way than we once believed, but something is being encapsulated in symbols or we would not

    use them. How can it be that artifacts that do not share our forms of life can "have knowledge"

    and how can we share it? "

Clearly, one of the reasons that tools can support knowledge management is that they are embedded in

    particular ways of acting and value systems. Consider, for a moment, the Delphic Oracle. One could say

    that some of the tools for managing knowledge in this case were the women who made the oracular

    responses to questions. The women functioned as providers of knowledge partly through the

    implementation of a methodology concerning the interpretation of the oracular responses by the priests. Thus the tools gain their ability to be part of a knowledge management process through the use of methodologies that lead to the embedding of the tools in a particular ‗form of life‘.

    When we make the popular distinction between tacit and explicit knowledge it is easy to forget that even explicit knowledge is only explicit because of a deep and richly understood context that allows us to interpret so-called explicit knowledge. This shared context is such a natural part of our forms of life that it is easy to ignore its existence until we discover/explore its richness to find that there can be alternative interpretations of what constitutes knowledge and understanding.

    My central point here is that no knowledge management tool stands alone. It can only be understood in the context in which it is used and the methodologies that are associated with it. If we focus too much on the tools of knowledge management we may blind ourselves to this richness. So-called knowledge management tools can potentially be used to manage superstition and falsehood when used in inappropriate contexts.

    There is also a danger that, in forgetting the role played by context, that we fail to grasp alternative perspectives. We no longer perceive the foreground as being intimately related to an arising from the background - we only see the foreground.

    It is also worth remembering that much esoteric knowledge is difficult to interpret and requires expert interpreters. To some extent, though, this very esotericism can be created deliberately. Knowledge confers power and power is often be gained and jealously guarded in this manner. Any admission of the pedestrian nature of a particular type of knowledge would make it available to every one! We still see many of the vestiges of this ‗form of life‘ in organizations the world over. Esoteric knowledge is often considered to be dangerous, particularly in the hands of the uninitiated. Secret societies are established with rites of initiation, stages of progress and secret documents to protect the knowledge and retain its power. In these contexts knowledge management tools may either be resisted or given token acceptance. Some aspects of organizational knowledge many be represented using the tools but much may be deliberately left out!

    Of course, one of the most well developed sets of tools and methodologies are those the scientific method. Over many centuries this approach has been enhanced and refined. Technologies have been applied and tools developed to ‗create‘ knowledge along with methods. In addition, there is a well-developed social

    context for the assessment and refinement of scientific knowledge. Scientific knowledge has to be accepted by the scientific community before it IS scientific knowledge.

    The relevance of coming to understand something about what we might call scientific knowledge management is that it can direct our attention to potential gaps in our understanding of knowledge management in organizational contexts. Organizations have evolved into many interrelated ‗forms of life‘. The creation of functional disciplines has resulted in there being many different types of knowledge residing in organizations. Some of this knowledge certainly has the status of scientific knowledge

    Research and Development departments often have strong scientific cultures. They have many of the tools that are typically used by the scientific community. On the other hand much of the marketing department's understanding of consumer behaviour may be grounded in scientific disciplines but may be just as much hunch and intuition as science.

    The recognition that there are many different types of knowledge within an organization is the source of much of the richness of organizations. It is often the source of their complexity, the source of their flexible responses to the external environment, the source of competencies that are very difficult for their competition to copy. The fundamental issue at stake, however, is that we ignore such richness at our peril. If we place too much emphasis on one particular type of knowledge or knowledge culture we are likely to ‗hollow out‘ the knowledge of the organization and leave it competitively vulnerable. Knowledge management tools must be used to explore this richness rather than be used to slavishly enforce one particular type of knowledge or knowledge culture. As I have suggested before knowledge management tools, by focusing on one particular approach to codifying knowledge can destroy the richness of the organizational knowledge ecology.

There are some more general lessons that we can learn from the above with respect to knowledge

    management tools.

    ? Many tools may have very different functions depending on the context within which they are used.

    For example e-mail may provide the basis for sufficiently rich communication between individuals

    within the scientific community. It may be a tool that facilitates the creation, refinement and transfer

    of knowledge in this context. In contrast, when members of the general public share e-mail it may

    only be the source of rumour and innuendo.

    ? A particular tool may enforce a particularly restricted approach on the user. This is unlikely to be

    because there is some inherent inflexibility in the tool itself. It is likely to be the case that in many

    contexts the tool is perceived in a particular way. A parallel of this problem is the basis of the socio-

    technical systems approach. There are many different social contexts into which a particular

    technology may ‗fit‘. The different contexts may have very different values, behaviours and, indeed

    knowledge.

    ? Great care must be taken when trying to integrate knowledge from two different communities of

    knowledge within an organization. This is true even when they make use of the same tools. I would

    hasten to stress that much greater care has to be exercised when the communities are using the same

    tools - there needs to be extensive investigation and negotiation between the communities to ensure the

    appropriate integration of knowledge - or the decision that it simply cannot be integrated.

One other word of caution with respect to knowledge cultures. I have stressed the importance of

    recognizing their richness of knowledge cultures within an organization. It is also important to recognize

    instances of the inappropriate identification of a particular knowledge culture. When investigating the

    knowledge cultures within the organization we must compare their knowledge practices with their

    understanding of these knowledge practices. For example, some groups may feel that their knowledge is

    essentially scientific knowledge. On closer inspection we may find that the knowledge is not open to

    verification, it comes from unsubstantiated sources and so on.

Before moving to consider some of the Web-based knowledge management tools that are now available to

    knowledge management practitioners I would like to review, in a little more detail, the various stages of

    knowledge management starting with knowledge generation.

Knowledge Generation

    As noted above Ruggles (1997, p.2) states that knowledge generation includes the activities of

    knowledge creation, knowledge acquisition, knowledge synthesis, knowledge fusion, and knowledge

    adaptation. One of the most interesting features of most of these activities is the need for intensive

    communication and a culture that is accepting of new ideas and is prepared to support exploration. In

    addition, interestingly enough, there is need to provide barriers. New knowledge will not be created if

    there are not barriers to rail against. There needs be some structure, some established knowledge.

What are the tools that aid knowledge generation? Perhaps the most obvious ones are the ones that allow

    for the sharing of knowledge in the first place. It is only through the sharing of knowledge that we become

    aware of the gaps in our knowledge. In the case of many businesses and organizations it is critically

    necessary to be able to surface the current knowledge and assumptions of the business. It is particularly

    important to surface these fundamental assumptions (the tacit context within which the business operates) -

    the unwritten rules of the organization. In many organizations there have been many examples of

    traditional ‗knowledge‘ that has been handed down from one generation the next. Sometimes this

    knowledge has been explicitly handed down in company manuals or in training sessions. More often than

    not, however, it has been embedded in company processes hidden from view but very much there.

    Knowledge management tools can be used to surface this knowledge and make it available for critical

    scrutiny. As we will see below, the artificial intelligence community has build a variety of tools over the

    years that allow us to represent knowledge these tools will become central to some aspects of knowledge

    management over the coming decades.

Knowledge Codification and Refinement

    Traditionally we have codified knowledge in a variety of ways. Artificial intelligence research has provided us with a much clearer understanding of both the strengths and limitations of the approaches we have adopted. One popular approach is to codify our knowledge in terms of rules. This was first exemplified by Aristotle with his syllogism the rules of correct argumentation. Rules that would

    guarantee that if we started with true propositions we would end up with true deductions from those true propositions. We may think of the syllogism and the rules for developing mathematical proofs as rules for preserving truth. These rules do not establish truth they only preserve truth.

    Over the 80s and 90s vast numbers of researchers made use of a variety of tools to encode these rules and investigate their behaviour. Tools, such as those based on the programming language Prolog provided an unique opportunity to investigate the interaction of rules and the range of deductions that could be made from them. In some cases this lead to the refinement of the knowledge. In other cases it led to the recognition that the knowledge being investigated could only partially be represented in the form or rules or not represented at all. An interesting example of this arose in the Law. On the surface the Law would appear to be rule based and it is reasonably so in some areas. However, in many areas of the Law a very significant amount of knowledge is needed to interpret the rules and it does not appear that this knowledge can be embedded in the rules themselves.

    Unfortunately we do not have time or space to investigate all the tools that are available for codifying and refining knowledge. Readers who want to explore this area are best advised to seek out the artificial intelligence literature. However, along the lines of our previous discussion a severe caution is in order about tools that can be used to codify knowledge. These tools typically provide for one way of representing knowledge (though some are somewhat more flexible). The knowledge that you wish to represent may not be representable in this way or may only be partially representable, as in the case of the Law and a rule-based approach. Further, it may take considerable skill and knowledge to be able to interpret the knowledge and represent it using a particular representation. Many of us in the expert systems field spent many years learning how to represent specific knowledge in relatively ‗simple‘ rules. Finally,

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