Knowledge Management—Emerging Perspectives
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.
Knowledge Management ('KM') 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 organisation
various enabling organisational 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 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.
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.
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.
Advanced practice and leadership of tangibles & intangibles, living networks, co-creation and whole systems through value networks and value network analysis.
Complexity approaches associated with David Snowden (see Cynefin).
'Narrative' with Denning, Snowden, Boje and others.
Key concepts in Knowledge Management
Dimensions of 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 argued that Nonaka and Takeuchi's distinction between tacit and explicit knowledge is oversimplified and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads).
Another common framework for categorizing the dimensions of knowledge include embedded knowledge (knowledge which has been incorporated into an artifact of some type, for example an information system may have knowledge embedded into its design) and embodied knowledge (representing knowledge as learned capability of the body's nervous, chemical, and sensory systems). These two dimensions, while frequently used, are not universally accepted.
It is also common to distinguish between the creation of "new knowledge" (i.e., innovation) vs. the transfer of "established knowledge" within a group, organization, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both creation and transfer.
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.
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 make knowledge requests of subject matter experts on an ad hoc basis. A key benefit of this strategy is that the response from the expert individual is rich in content and contextualized to the particular problem being addressed and personalized to the particular person or people addressing it. The downside of this strategy is that it is tied to the availability and memory recall skill of specific individuals in the organization. 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.
Drivers of Knowledge Management
There are a number of claims as to 'drivers', or motivations, leading to organizations undertaking a knowledge management program.
Perhaps first among these is to gain the competitive advantage (in industry) and/or increased effectiveness 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. Government represents a highly active area, for example DiploFoundation Conference on Knowledge and Diplomacy (1999) outlines the range of specific KM tools and techniques applied in diplomacy.
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 and learning
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, leading to greater employee and team satisfaction
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
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
Knowledge Management Technologies
The early Knowledge Management technologies were online corporate yellow pages (expertise locators) and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid 1990s. Subsequently it followed developments in technology in use in Information Management. In particular the use of semantic technologies for search and retrieval and the development of knowledge management specific tools such as those for communities of practice.
More recently social computing tools (such as blogs and wikis) have developed to provide a more unstructured approach to knowledge transfer and knowledge creation through the development of new forms of community. However, such tools for the most part are still based on text, and thus represent explicit knowledge transfer. These tools face challenges distilling meaningful re-usable knowledge from their content.
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 not a necessarily pre-condition or confined to start up.
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.
Another aspect would be the creation of an incentive-system not only to provide the organisation with knowledge but also to manage and handle ideas of employees.
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.
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.
Knowledge Management Reasons of Failure or Success
There is no established evidence as to the reasons behind failure and success of Knowledge Management initiatives in organizations. Some argue that a failure to sustain investment is one factor, but it can equally be argued that if knowledge management delivered on its promises investment would continue. As with many management initiatives, particularly those with a heavy IT basis (as is the case in Knowledge Management), frequent questions are raised about the level of consultation necessary before a program is started; these questions are linked to issues of cultural change and a willingness to share and collaborate with colleagues There is no evidence that Knowledge Management, in all these respects, is any different from other management initiatives.
Avanish
