Conference Draft

June, 1999

 

Please contact the author to receive an updated and expanded version of this paper.

 

Introduction

What Does Knowledge Management Mean?

The Benefits of KM

Significance of KM

Relevant Technologies

Infrastructure Elements

Data Storage

Data Access

Artificial Intelligence

The Challenges of KM

Alternative Approaches to KM

Emerging Standards

Conclusions & Recommendations

Significance/Future of KM

Multi-stage KM

Standards Efforts

Bibliography

Selected Web Sites

Knowledge management is rapidly becoming widely accepted as a critical component of competitive business strategy.  With this acceptance comes considerable confusion.  For instance, do you know how knowledge management is likely to affect you in your role as a manager?  What do you need to know about it?  Is knowledge management really as revolutionary as vendors and consultants claim?  What is knowledge management, anyway?

 

None of these questions is easy to answer, but this paper will attempt to provide at least some of the answers.  An additional goal is to supply further insight into the fundamental concepts behind knowledge management, its potential benefits, why it could be important for your organization, as well as some of the thornier issues involved with its implementation.

 

 

The term knowledge management (or KM for short) has rapidly become one of the business community’s favorite buzzwords over the past few years.  However, what does it really mean?  Business consultants seem to have their own meaning for it, while software vendors have their own interpretation, and corporate executives sometimes appear to have yet a different definition.

 

As an example, the following quotations all purport to define or demystify the concept of knowledge management:

 

 

While there are many recurring themes in these definitions and comments, there are also some significant variances.  Moreover, as indicated by the last two quotes, we have not yet arrived at universal buy-in on the basic concept of KM.

 

As David Weinberger suggests in his quote above, whenever one compresses a very broad concept into a two-word phrase, it is natural that there will be widely varying interpretations of that phrase.

 

Some of the variance is no doubt due to the relative newness of the phrase.  Even the earliest academic and management books on the subject were only published several years ago.  Concepts and doctrines that are readily embraced by academia often take much longer to become accepted by the general business community.

 

Perhaps some of the confusion and ambiguity is due to the indiscriminate use of the term by vendors purporting to sell KM products.  Knowledge management is an ideal catch phrase for professional marketers.  It rolls off the tongue easily, takes up very little space in ad copy, sounds extremely important, and also carries with it substantial implicit meaning.  As a result, it is not only tempting for vendors to add the KM tag to their products, but it is also likely that the phrase is intended to convey different meanings by different vendors.

 

Earlier this year, members of the Association for Information and Image Management (AIIM), an industry group representing most of the players in the document management industry, discussed and debated the myriad definitions of knowledge management.  AIIM has initiated a standards effort around knowledge management, with one of its goals being to draw up a generally acceptable definition of knowledge management.

 

Obviously, this is an extremely ambitious task and it remains to be seen whether this will be more beneficial to vendors or to users of KM products and services.  As an example of the difficulty inherent in such an effort, Davenport and Prusak attempt in Working Knowledge to explain the significance of the term knowledge by describing the relative roles of data, information, and knowledge.

 

They first describe data as “…a set of discrete, objective facts about events…[that] by itself has little relevance or purpose.”  Data is of relatively low economic value since it does not include any judgment or interpretation.  The next step up in value, information, is “a message, usually in the form of a document or an audible or visible communication.”  With information, the roles of sender and receiver become important, since the receiver is the one who decides whether the data received gets elevated to the status of ‘information.’  One of the important differences between information and data is that information has meaning.

 

Higher in status than information is knowledge, which is broader, deeper, and richer than either data or information.  Their lengthy definition of knowledge is as follows:

They then acknowledge that the concept of knowledge is an intuitive one, difficult to express in words, and difficult to understand completely in logical terms.  The essential difference between information and knowledge is that humans must do the work to convert information into knowledge.

 

While this particular explanation and definition of knowledge is an excellent one, it is not by any means universal.  Other researchers, commentators, and consultants have injected their own unique twists into the term.  Given the considerable ambiguity in just the first part of the phrase, it is little wonder that knowledge management means many different things to different people.

However, there is general agreement on many of the economic benefits of and requirements for knowledge management initiatives, so this paper will next focus on those areas.

 

 

As with the definition of KM itself, the list of promised benefits of knowledge management may vary depending upon whom you talk to, but the most commonly mentioned ones are:

 

·        improved innovation

-          both nurturing it and sustaining it

·        improved decision-making

 

·        competitive advantage

 

·        transfer of best practices

 

·        the ability to adapt more quickly to change

 

·        improved efficiency

 

·        improved productivity

 

·        shorter development cycles/reduced time to market

 

·        improved customer service

-          both care and retention

·        improved bottom line and ROI

 

 

If knowledge management can do all these things for business organizations, why is it so much more important today that it was 25 (or 50, or even 100) years ago?  Was it not just as important then to manage an organization’s knowledge?

 

In his recent book, Corporate Instinct, Thomas Koulopoulos argues that today’s business environment is indeed different.  In the past, competitive standing was improved mostly by a mix of economies of scale, marketing and sales proficiency, quality, and customer service.  Technology has advanced to the point where even small startups can readily compete with larger, well-established companies in these areas.  In today’s rapidly changing business environment, innovation has become the most important competitive differentiator, and an organization’s capacity for innovation depends primarily upon the ability to use its ‘knowledge.’

 

Koulopoulos uses the term ‘corporate memory’ to describe the approach used in the past when industries evolved more slowly.  Companies could be most effective by relying upon their corporate memory – that is, applying the same or similar strategies previously applied in the same situation.  By making small adjustments to these strategies, companies could refine their products and abilities and still gain competitive advantage.

 

Thomas Stewart, in Intellectual Capital, translates knowledge into economic terms.  He states that knowledge “has become the primary ingredient of what we make, do, buy, and sell.  As a result, managing it – finding and growing intellectual capital, storing it, selling it, sharing it – has become the most important economic task of individuals, businesses, and nations.”

 

Other consultants and commentators share Stewart’s view of knowledge as an intangible asset.  Many have cited the often huge difference between a company’s market value and book value as evidence that traditional accounting methods can no longer accurately portray the true value of a company, since many of its assets are intangible ones.  While this has always been true for assets such as goodwill, today’s valuation gaps indicate that many organizations possess even more valuable intangible assets.

 

Perhaps the most significant of these intangible assets is corporate knowledge.  Judging by the difference in magnitude of these valuation gaps, some organizations appear to have more ‘knowledge’ than others do.  Some of this advantage is due to the quality of their employees.  Organizations that can recruit and retain ‘the best and the brightest’ are obviously at an advantage in today’s knowledge-based economy.  However, employees are changing jobs more frequently today than they ever have in the past, so this alone cannot explain the difference.  Indeed, current research shows that these organizations have all devised their own particular way of using their wealth of corporate knowledge to achieve competitive advantage.

 

 

Infrastructure Elements

1)     messaging/collaboration

-          once thought of as separate application, but now considered a mission-critical ‘plumbing’ element

 

2)     workflow

 

Data Storage

1)     Databases

 

2)     Document Management

 

3)     Imaging

 

Data Access

1)     Data mining

 

2)     Data warehousing

 

3)     Intelligent searching

-          Boolean and context-based search techniques are commonly used in Web search engines today, but they do not provide sufficient power or sophistication for use in knowledge management efforts.

-          Semantic networks (preconstructed knowledge bases, derived from dictionaries, thesauri, and other lexical resources) can distinguish between multiple meanings of a word based upon context

-          Pattern recognition (allows ‘fuzzy’ searches) covers misspellings by the person searching and in the stored data itself

 

4)     XML

-          Tags/index for KM repositories

 

5)     Portals

-          Corporate intranet access to knowledge base

 

Artificial Intelligence

1)     natural language processing

-          allows semantic encoding of stored material

 

2)     pattern recognition

-          neural networks

-          speech recognition

 

3)     Bayesian networks

-          Invented by researchers at Stanford and UCLA in early 80’s

-          Supports predictive models that capture cause and effect relationships

-          Possible representation for combining prior knowledge and other data

 

4)     Knowledge representation

 

5)     Memetics

-          Theoretical parallel to genetics

-          a meme is an information pattern, held in an individual's memory, which is capable of being copied to another individual's memory

 

 

A recent survey of IT managers by Information Week (4/5/1999) listed behavior modification on the part of employees as the biggest challenge in implementing knowledge management.  Also found to be significant issues were buy-in from corporate management, and the inherent difficulty of classifying knowledge.  While one could argue that IT managers are not the ideal sample group for this survey, their responses tend to match the opinions of many industry commentators.

 

Traditionally, the corporate environment has not naturally encouraged the sharing of information or transfer of knowledge between employees.  Obviously, some of these activities do take place within organizations, but typically, this knowledge transfer has occurred in an ad-hoc fashion.  For some employees, knowledge is job security; if they tell others how they perform their job duties, they feel that they are now replaceable.  By hoarding this knowledge, they can remain valuable employees.

 

Clearly, the corporate culture and reward systems of many organizations do not encourage knowledge sharing.  A KM program implemented by such an organization may produce positive results, but cannot be truly effective without the appropriate changes to the organization’s corporate culture and employee reward and compensation system.

 

None of these changes can happen in a purely evolutionary manner.  They must be supported at the very highest levels of the organization.  The cost of a robust KM initiative can be significant, and needs to be quite visible in corporate financial planning activities.  In addition, since corporate culture plays such a critical role in the success of a knowledge management effort, important KM activities such as informal chats, formal and informal information exchanges, post-project knowledge codification, etc. all should be officially sanctioned by upper management.  Otherwise, there is a risk that some of these activities will be perceived as time wasters and outside the scope of employees’ job descriptions, which will certainly have a chilling effect on continued knowledge sharing within the organization.

 

A further obstacle to meaningful knowledge sharing is the inherent difficulty in classifying knowledge.  It makes no sense to store knowledge in some repository if there is no way of retrieving it later.  However, as the volume of knowledge to be stored increases, the need for a classification system becomes more critical.

 

Unlike the card catalog used at libraries to keep track of books, a scheme to categorize stored knowledge is infinitely more complex.  Since the same information can mean different things to different people, each ‘chunk’ of knowledge needs to have multiple types of classification or keywords.  There is a delicate balance here.  If there are too few keywords for a particular ‘chunk’, it is likely that this knowledge will not be accessible to some people who would not think to categorize the information in one of those ways.  However, if there are too many keywords for a particular ‘chunk’, and this pattern is repeated for many of the other ‘chunks’, then it will be very difficult for users to narrow down a search.

 

Developments in the field of artificial intelligence are likely to assist in the categorization of knowledge.  However, since categorization of knowledge is not a precise science, considerable effort should be focused on knowledge storage and retrieval schemes as part of an overall KM initiative.

 

 

Despite the pervasiveness of technology in today’s corporate environment, it is not the only tool for furthering a knowledge management initiative.  There are many valuable contributions that can be made by more traditional means.

 

Implicit in the concept of knowledge management is the notion that some learning has to take place in order for knowledge to be acquired, and learning is not a passive process.  Furthermore, different people have different learning styles – some people learn best by reading instruction manuals or reference material (such as might be contained in knowledge databases), while others learn more effectively through a teacher-student interaction.

 

Several commentators argue persuasively that learning cannot effectively take place in a corporate environment without consistent, visible teaching activities.  These could take the form of traditional classroom gatherings, but could also be considerably more informal. The face-to-face teaching approach is especially important with tacit knowledge, which is difficult to capture and codify.  Often, the only way to transfer this knowledge is through a mentoring or apprentice relationship.

 

An old standby, the corporate meeting, can also be an effective tool in a comprehensive knowledge management program.  The “Dilbert” cartoon series succinctly captures an increasingly shared feeling about corporate meetings – a waste of time and a total bore.  This was not always the case, however.  In the early 1900’s, some businesses rejected the strict isolation between upper management and line management and initiated a program of regular meetings that included foremen, superintendents, department heads and other low-level managers.  The primary purpose of these meetings was knowledge-sharing: to exchange information and techniques, coordinate activities, and to address problems within the company.

 

Today, geographic separation and time constraints make such meaningful exchanges more difficult to arrange, but perhaps with the use of such recent technology as whiteboard software, desktop video, and real-time groupware applications, this type of meeting could be achieved.  However, no matter how sophisticated the technology, ‘virtual’ meetings have the same requirements for success as traditional face-to-face meetings.  They will only be effective if they are carefully planned, and if the political content and posturing can be minimized, so that worthwhile communication can take place.

 

Surprisingly, the age-old tradition of community storytelling may also prove to be an invaluable tool for knowledge sharing within corporations.  David Snowden, with IGS in the United Kingdom, has proposed a cultural model that resembles the pre-agricultural hunter and gatherer society to promote knowledge sharing.  He compares the hunter-gatherers, who were very effective at allocation of resources and distributing work according to individual skills, with a minimum of overhead, to the later agricultural societies, which developed hierarchical structures that naturally created divisions between different elements of the society.

 

He argues that recent economic trends (e.g., shrinking margins, outsourcing, lower surpluses, etc.) are making the hunter-gatherer model more relevant than the predominant agricultural model.  A key component of the hunter-gatherer society is community storytelling – the art of creating an easily repeatable tale that capture powerful themes of success and failure, and Snowden views this as a powerful tool within today’s corporate environment.

 

A group of social scientists, business managers, and journalists at MIT’s Center for Organizational Learning has created a more formal tool for storytelling called a learning history.  It is a written narrative of a company’s recent set of critical episodes.  The subjects could include a successful product launch, a corporate initiative, or a major restructuring or downsizing.

 

The document consists of two columns - on the right-hand side, relevant events are described by people closely involved with them.  The left-hand side contains analysis and commentary by a team of learning historians, a small team of trained outsiders coupled with selected insiders.  The purpose of the left-hand column is to identify recurring themes, raise questions about assumptions in the narrative, and to identify “undiscussable” issues that, while not explicitly mentioned in the narrative, appear to the learning historians to be critical ones.

 

Once the learning history is complete, the storytelling process begins.  The document is used as a basis for group discussions within the organization, and the goal is for the participants to gain a better understanding of the critical choices to be made in similar situations.

 

So far, the learning history approach has been successful in a number of large U.S. companies.  Its proponents state several reasons for this success.  First, learning histories build trust and a sense of camaraderie, both of which help create an environment that is conducive to continued learning.  Also, learning histories have proven to be effective in transferring knowledge from one part of a company to another, and in building a body of generalized knowledge about management.

 

 

On January 19, 1999, AIIM International (The Association for Information and Image Management) announced an initiative to develop ANSI/ISO standards and technical reports for knowledge management.  They outlined four initial goals:

 

·        to draw up a generally acceptable definition of knowledge management

 

·        to develop consensus on the cultural and sociological issues that affect how knowledge is produced, acquired and transmitted by individuals and groups within an organization

 

·        to develop a model to assist consultants in understanding how knowledge is processed, recognizing this may vary greatly from corporation to corporation

 

·        to set guidelines for software simulation tools, to be developed to automate the modeling procedure.

 

Another standards organization is the Customer Support Consortium, a non-profit alliance of “technical support organizations dedicated to developing innovative strategies, business models and standards for customer support organizations.”  Members include Compaq, Hewlett-Packard, Intel, Lucent, Microsoft and Unisys.  CSC has developed Solution-Centered Support, which is a knowledge management strategy for support organizations.  It defines a set of principles and practices that enable support organizations “to improve service levels to customers, gain operational efficiencies, and increase the support organization's value proposition to their company.”

 

Yet another group involved in the standards effort is the Knowledge Management Consortium, a non-profit corporation formed in 1997 devoted to developing a balanced view of knowledge management from the context of an organization.  The KMC bills itself as a user-based organization, instead of a vendor-driven one.  They are working with AIIM to develop KM standards.  Members of KMC include 3M, Andersen Consulting, Chevron, Dupont, Ernst & Young, Harvard, IBM, Intel, Merrill Lynch, Microsoft, MIT, and Unisys.

 

In addition, IBM and Lotus Development have formed the Institute for Knowledge Management, which will conduct applied research on the creation and deployment of knowledge management systems.  Initial IKM members will include General Motors, Xerox, The World Bank, Boston University, Stanford University, and The Wharton School.

 

However, some industry analysts are not impressed by the flurry of standards activity.  In a research note published earlier this year, the Giga Information Group labeled AIIM’s standards goal as “wrong-headed and premature.”  One of their primary criticisms was that implicit in AIIM’s statements was the belief that knowledge processes can be captured and simulated in computer systems.  Giga feels that this approach may well apply to process-based knowledge, but will not work with complex personal knowledge commonly used in business interactions.  Also, the note expressed concern that prematurely defined and adopted standards would reduce the incentive for research into the multitude of ancillary disciplines that involve the use of knowledge in business.

 

 

Clearly, the phrase ‘knowledge management’ goes beyond “the normal mystical consultant happy talk” mentioned by David Weinberger in the earlier quotations.  However, is it possible to really put a finger on it?

 

Significance/Future of KM

An overly cynical view would be that the KM initiative is increasingly popular with large corporations because they realize that knowledge is now one of an organization’s most valuable assets and much of this knowledge resides in the heads of their employees.  They see knowledge management as a tool to capture and retain the knowledge of these workers, in order to defend themselves from the impact of eventual employee defection.

 

An alternate (and perhaps more reasonable) view is that corporations view KM as an important tool not only for today’s corporate environment, but also for a more dynamic and transient corporate environment in the future.  A recent article in Knowledge Management magazine discusses how the film industry might be an unlikely, yet ideal organizational model for the ‘Knowledge Age’.  One similarity between the film industry and knowledge-based corporations is that in both, there is an intense focus on intangible assets.  Within the film industry, the primary asset is intellectual property – the functional equivalent of corporate knowledge.

 

There are also telling parallels between employees in the film industry and in today’s corporations.  To produce a movie, film studios assemble transient project teams of actors and actresses, directors, cinematographers, and many others.  When the movie is complete, the team is dissolved, but the intellectual property (the completed movie, the script, theme music, etc.) remains behind.

 

Increasingly, today’s corporations are calling on contractors and temporary teams of employees to see a project through to completion.  It is imperative for the future competitiveness and viability of the organization that it retains the corporate knowledge created during the course of the project.  Otherwise, much of this valuable knowledge may end up being transferred to a competitor when it hires away an important member of the project team.  As a consultant interviewed for the article commented, “the nature of knowledge workers is that they are free birds.”

 

 

Multi-stage KM

Despite the desire of many vendors to treat knowledge management as one large, amorphous practice, perhaps it is useful to consider KM as a wide spectrum of concepts and technologies.  By dissecting the broad practice of knowledge management into several separate components, it is easier to see how implement a KM initiative within an organization.

 

Knowledge Referrals

Creating and maintaining a list of subject area experts is probably the easiest way to begin a KM initiative.  Many of the technical challenges of categorizing and storing knowledge are eliminated, since the list merely contains pointers to sources of knowledge.  It is then up to the knowledge consumer to track down the appropriate resources and obtain the desired knowledge.  The greatest amount of effort in this stage of KM implementation would be in keeping the referral list current.  However, this is consistent with the dynamic nature of knowledge, and if maintained properly, would have the greatest chance of rapid success.

 

Knowledge Nurturing

The next stage revolves around the culture of the organization, which requires considerably more effort.  The implementation details of this stage are beyond the scope of this paper (see, for example, Leonard-Barton’s Wellsprings of Knowledge), but the primary tasks would be to create the appropriate culture in order to:

 

·        encourage innovation

·        encourage continued learning

·        encourage knowledge sharing and transfer.

 

In many cases, the organization’s employee reward and recognition system will require a major overhaul.  At this stage, there is still a reasonable chance of success, but it will be more difficult than the knowledge referral stage.

 

Knowledge Extraction, Storage & Retrieval

This is the ultimate goal of some of the proponents of KM – the capture, storage, and later retrieval of the organization’s valuable knowledge.  It is also the stage that has the least chance of success.  This stage depends heavily upon the knowledge nurturing stage above, but also has many other obstacles.

 

The biggest obstacle is the difficulty in capturing tacit knowledge.  Explicit knowledge is more readily captured, but tacit knowledge is much more elusive.  Included in the category of tacit knowledge are the tasks that an employee performs without really having to think about them.  Furthermore, when asked to explain how to perform the task, the person may be unable to do so, since the performance of the task comes so naturally.  How can this knowledge ever be reduced to a megabyte or two in a database?

 

The issues with employee behavior and corporate culture also resurface at this stage.  Unless the corporate environment has been adequately prepared, there will still be some employees who will resist the effort to extract and store their portion of the organization’s knowledge.

 

Standards Efforts

Finally, the various KM standards efforts should be discussed.  To the casual observer, these seem like reasonable and desirable goals.  However, because of its brevity, the term ‘knowledge management’ may imply a degree of precision that may be impossible to achieve.

 

First, human nature and personality traits play too large a role in the practice of knowledge management to attain a high level of precision in the results.  Unfortunately, both software vendors and consultants exaggerate the degree to which knowledge can be ‘managed’, since to state otherwise would limit sales of their products and services.

 

In addition, knowledge management can fall victim to the ‘false precision’ phenomenon that is common with computerized applications.  To many people, data that may be otherwise suspect takes on a higher degree of precision if it is entered into a spreadsheet application, neatly formatted, and then printed.  Similarly, corporate ‘knowledge’ that is stored in an easily accessible repository or database may be credited with a much higher degree of validity than it would when proffered by an office colleague.

 

Undoubtedly, knowledge management is a powerful and important concept for organizations in today’s rapidly evolving business environment.  However, just as in real life, knowledge management implementations will provide many answers that will vary - in timeliness, completeness, price, accuracy, and trustworthiness.

 

 

 

 

Davenport, Thomas H., and Lawrence Prusak. 1998. Working Knowledge. Boston: Harvard Business School Press.

 

Fisher, Kimball, and Mareen Duncan Fisher. The Distributed Mind: Achieving High Performance Through the Collective Intelligence of Knowledge Work Teams. 1998. New York: AMACOM.

 

Kleiner, Art, and George Roth. “How to Make Experience Your Company’s Best Teacher.” 1977. Harvard Business Review. Boston: Harvard Business School Press.

 

Koulopoulos, Thomas M., Richard Spinello and Wayne Toms. 1997. Corporate Instinct: Building a Knowing Enterprise for the 21st Century.  1997. New York: Wiley.

 

Leonard-Barton, Dorothy. 1995. Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation. Boston: Harvard Business School Press.

 

Nonaka, Ikujiro, and Hirotaka Takeuchi. 1995. The Knowledge-Creating Company. New York: Oxford University Press.

 

O’Dell, Carla, C. Jackson Grayson, Jr., with Nilly Essaides. 1998. If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice. New York: the Free Press.

 

Polyani, Michael. 1958. Personal Knowledge. Chicago: University of Chicago Press.

 

________. 1966. The Tacit Dimension. New York: Doubleday.

 

Stewart, Thomas A. 1997. Intellectual Capital. New York: Doubleday

 

Svieby, Karl. 1997. The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets. San Francisco: Berrett-Koehler Publishing.

 

 

Selected Web Sites

 

Association for Information and Image Management

          http://www.aiim.org/

 

Customer Support Consortium

          http://www.customersupport.org/

          http://www.outsights.com/

 

Institute for Research on Learning

          http://www.irl.org

 

Journal of the Hyperlinked Organization

          http://www.hyperorg.com/

 

Knowledge Management Central

          http://www.icasit.org/km/

 

Knowledge Management Consortium

          http://www.km.org/

 

Knowledge Sharing Effort Public Library

          http:// www.ksl.Stanford.edu/knowledge-sharing/README.html

 

WWW Virtual Library on Knowledge Management

          http://www.brint.com/km