Knowledge Management and Intellectual Capital

• Overview of Knowledge Codification and Management
• The Argument for Building a Strong R&D Organization Versed in Knowledge Management and Intellectual Capital Formation
• Implementing Knowledge Management Systems
• How Knowledge Management Builds Intellectual Capital
• Knowledge Management Best-Practices Options
• Sources, References and Selected Bibliographic Information

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Overview of Knowledge Codification and Management

Business leaders in the intellectual capital community have been struggling for a long time to figure out how to gain maximum value from the tremendous amount of knowledge capital that exists in organizations with only sporadic success. The true sources of knowledge and intellectual capital in an organization are not as obvious as people might think. The information is often buried in the bowels of the organization, hidden from everyone else in people’s heads, meetings and other conversations, notepads, planners or PDAs, email,computers, home, or on the road, and voicemail. Additionally ethnographic study show that, of the information knowledge that workers use to do their work, only 10 to 20% is managed in a way that enables enterprise to leverage anywhere near the knowledge’s full potential. For example:

1. The average amount of time a knowledge worker spends doing routine work is approximately 25%. Routine work is work that is predictable or pre-specified in detail. It is focused on putting predefined solutions into repetitive practice. Examples are expense and time reporting, supply requisitioning,meeting scheduling, routine problem-solving and decision-making, and other relatively simple bureaucratic work.

2. The average knowledge worker spends 75% of their time doing knowledge work. This work is more unpredictable, unspecified, nonlinear, fairly unique each time it is done, and more focused on defining and solving difficult problems. Examples are research, most human interactions, planning, complex problem-solving and decision-making, reflection learning, innovation, and knowledge sharing.

3. The average worker spends 30% of their time looking for information or knowledge they need to do their work. This is any work, routine or knowledge based.

4. When performing routine work, 50% of the information or knowledge used comes from data via the Enterprise’s information systems such as finance, HR,purchasing, product data management, specifications, etc. This information is extensively managed by the enterprise.

5. This means that 50% of the information and knowledge workers use for routine work is unmanaged, at least at the enterprise level. But when performing knowledge work, only 10% of the information/ knowledge needed come sin the form of data from the Enterprises managed information systems.

6. 90% of the information used for complex knowledge work is unmanaged by the enterprise. It is everything else people use in their work such as project documentation, presentations, email messages, spreadsheets, work practices, meeting minutes and notes taken by individual attendees, lessons learned, strategies, and all of the tacit information knowledge that they have in their own heads and hear from others through verbal communications and knowledge sharing.

7. Putting the above statistics together, knowledge management and intellectual capital value to the enterprise coming from increasing the efficiency of accessing information and knowledge through knowledge codification results in gaining over 12,000 hours of knowledge work per year and 30,000 hours of routine work per year per employee for every 5% increase inefficiency and accessing managed data. For every 5% increase in the efficiency of accessing unmanaged information, the knowledge potential gain is over 100,000 hours of knowledge work per year and 30,000 hours of routine work per year.

8. Most previous attempts to codify large amounts of knowledge amended have ended in very costly failures. Organizations found it impossible to find,capture, and especially maintain the massive amounts of knowledge that exist in large organizations which is constantly changing and changing in an ever increasing rate. When done in a predefined manner, significant amount of the codified knowledge is already obsolete by the time it was documented.

To make knowledge useful to an organization, sufficient context must be provided with the knowledge to give meaning to the user in doing work that is important to the enterprise. A simple way to think about the difference between data, information, and knowledge is that as you move through the data-to-knowledge-continuum, the material takes on increasing levels of context and meaning so the perceiver user gains increasing levels of understanding, to the point where they can effectively apply it to work that produces high value for the enterprise. Thus there are three important messages about knowledge:

1. The difference between information and knowledge is one of meaningful context. This can be expressed in different ways but a simple way to think of it is that knowledge is applied-information or information-in-action.For knowledge codification, this means that the codification should be about the context and productive use of the knowledge, not on a mechanistic tacit to explicit codification process.

2. Knowledge is in the eyes of the holder; that is, it is the amount of meaning provided to the user, not the creator, which determines whether or not it is knowledge. Note that the creator can also be a user of the knowledge,including their own knowledge. For knowledge codification, this means that the key is to view the information and knowledge from the point of view of the useror potential user, not the creator codifier.

3. The knowledge the organization should care about is focused knowledge, not all the knowledge that exists in the organization and its people. Knowledge by itself is of no value to the organization. The implication for knowledge codification is that you need to focus the very limited amount of time and attention the enterprise has on what matters most, and this includes knowledge. Knowledge codification, in whatever form it is employed, can be expensive, time-consuming, and distracting from important matters, if not focused. To achieve this focus, you need a systems perspective of your enterprise,your vision, mission, goals, and values, combined with a detailed understanding of what your people need and have to offer to support the enterprise.

Thus an important role for knowledge codification is to provide access to the information the workforce needs to do their jobs in context,as well as help in creating meaning from it in doing their work.

When knowledge is codified it is important that the traditional separation between value-in-use and value-in-exchange must be abandoned in favor of an appreciation that knowledge has economic value only when used. This value in the eyes of the beholder is why context is required to give value and meaning to information. To codify knowledge, storage in addition to the knowledge itself, is its context, framing/problem representation, configurable effects/Gestalt’s, dynamics / temporal context, and network externalities.  Without this complete repertoire of attributes or picture, true knowledge storage is not possible. Fortunately the advent of extremely large databases and artificial intelligence is making the problem solvable.

For example to understand a company’s customer’s transactions and value, relevant information needs to be stored. This starts with customer (a person or company) characteristics such as age, revenue, net worth or value,child or subsidiary, age, etc. This rich information needs to be gathered along with the firm’s decisions to launch a new product. At that point the customers attributes of what they are, where they are, when they engage with the company, and how and why they do so also need to be mapped over many purchase and non-purchase decisions. Likewise the purchase history of what they bought and from who needs be coupled with the cost of goods sold and profitability so the lifetime profit potential of the whole system of customers and firm decisions can be understood. In 2018 only a few companies in the world were undertaking such massive projects and carefully evaluating each of their decisions around launching new products and services. The deep level of insight gained in doing so however added true value to the innovation process and what to launch next. The complication for companies is to find a methodology by which to store all the knowledge that was previously left as tacit or discarded in decades past. At some point this will include stories by top organizational leaders telling everybody what’s not in the manual. As artificial intelligence engines mine the seemingly unrelated data, algorithms of best practices will emerge to push companies ahead at faster rates.

The Argument for Building a Strong R&D Organization Versed in Knowledge Management and Intellectual Capital Formation

The basis for fast high-quality innovation and commercial development is found in the organization’s knowledge management and building of intellectual capital. This can be viewed at a high level by looking at the three eras through which society has passed.  The first was the agricultural era where the possession of land and labor to work the land wasking. This gave way to the industrial era were financial capital in machines took center stage. As shown in the “Basis for Three Eras” figure the knowledge era strongly depends upon knowledge management, with labor and capital still significant.  Just as labor was a constraint resource in the agricultural era, in capital a constraint resource in the industrial era, knowledge is a constraint resource in the knowledge era.Finding ways to make knowledge ever more productive will create large increases in corporate value is implied in the “Transformation of Knowledge to Corporate Value” figure.

The challenge for the knowledge era is the unprecedented speed of change impacting society and businesses. The globalization of the economy, automation robotics,artificial intelligence, and the global sharing of data are all causing the business windows of opportunity become shorter and shorter. The half-life of corporations’participation in the S&P 500 index has dropped from 60 years in the last century to under 18 years today. The dynamics of competing in the knowledge era requires increasing sophistication of understanding customer requirements,patterns of disintermediation, fast-changing customer preferences, faster competitive counteraction tactics and strategies, and increased value of partnering and networking.

What this means is that in the knowledge era, the creation of corporate value will result from acceleration of organizational learning and the generation of knowledge capital as shown in the “Benefits of Transformation of Knowledge to Corporate Value” figure. This means the objectives are to build knowledge infra structures that allow the systematic sharing of knowledge, thus enabling faster learning. At the next level it requires enhancement of business competencies that relate to enhanced learning at all levels, so that a superior ability to reach the customer results. The next step is to accelerate the organizational capability through increased innovation methods so that at the highest level of growth opportunities are generated, which in turn requires the skill to transfer and build new opportunities in expanded worldwide markets. All of these requirements and abilities create the highest corporate value.

The takeaway from this line of thought is that learning is a requisite response to change. Learning must equal or exceed the level of change if an organization is to be successful. 

The Learning Versus Change Matrix figure shows how the environment’s rate of change and the organization’s rate of learning can create one of four generalized outcomes. If the rate of change of the environment is low and the rate of learning in an organization is also low that organization is in a stagnant stable state awaiting a crisis. The crisis occurs when the rate of change of the environment increases and the company does not respond by increasing its rate of learning.  In this case it experiences a paralyzing chaotic situation typically causing bankruptcy the organization. On the other hand if the rate of the learning of an organization is high it can either be doing well in a low rate of change environment or evolving and adapting with other high-performance organizations if the rate of change is high. It is pretty straight forward that the rate of learning has to be high in the Knowledge Era’s high change environment. Thus there is a need for the systematic creation and value through knowledge because fundamentally ideas drive competitiveness,innovation is cheaper than competition, clients are changing and demand integrated solutions, a larger number of competencies are required to compete,speed must be provided to match the pace of the environment, and businesses must constantly be reinvented by redefining the boundaries.

Implementing Knowledge Management Systems

Implementing knowledge management systems into the day-to-day activities and processes into an R&D or corporate entity is difficult. First of all there are two types of knowledge in a variety of forms that must be utilized.

Explicit knowledge is knowledge that is been articulated or codified in words or numbers. This knowledge can be retrieved and harvested from the knowledge grid(internet and databases/data-stores) and transmitted relatively easily. Most knowledge management systems today deal with this type of knowledge.

The second type of information is tacit knowledge. This knowledge is comprised of the intuitions, perspectives, some beliefs and values.  It results from a person’s experience. This knowledge can best be communicated interpersonally through dialogue or use of metaphors. It aligns the mindsets and mental models of individuals within teams,organizational culture, and society.

But a further segmentation of knowledge is useful for creating corporate value. As shown in the “Forms of Knowledge” figure, the completeness of information and the clarity of information are both important in developing the best practices forgathering the information, learning how to best utilize it, transferring it others, and taking actions to improve corporate value.

How company value is based on building explicit knowledge is shown in the “Explicit Knowledge Utilization” figure. The process includes gathering information from a variety of sources and digesting it through individuals and artificial intelligence engines to create databases.  This includes both the original information as well as key metadata. This information is then transferred to other individuals or groups who can utilize that information in a company’s operations and new products development processes. Thus this knowledge is a both a sum of organizational knowledge and personal knowledge.When the data warehouses and artificial intelligence engines capable of mining raw data for relevant metadata and algorithms are combined, the sum total knowledge available for building customer value is increased greatly.

The infrastructure required to gather information includes building knowledgeable teams with a mandate to encompass all the social and technical dimensions of knowledge available. Such a mandate was not possible to fulfill until the 2010s and the advent of large customer and operational data stores.

To address the next step of learning and transferring the information and metadata that is been created requires building communities of practice around discipline, strategic themes, business teams, and project teams. Finally to utilize this knowledge in actions and activities to help the Corporation,the knowledge infrastructure focused on linking the flow of information to the right individual or groups is required. Thus an overall electronic railroad on which knowledge can be engaged seamlessly across the enterprise is necessary.

In contrast of the above processes for explicit knowledge, tacit knowledge relies instead on the formation of knowledge communities. These individual communities of practice need to be tightly connected in order to function well. As humans have only limited bandwidth and time to participate in multiple communities, artificial intelligence entity/robots hold promise when their participation in interpersonal discussions coupled with their understanding and recording the intuitions, perspectives, beliefs and values associated with various contextual experiences, is possible. Until this possibility arises the formation of small human teams to share tacit knowledge is required, as shown in the “Tacit Knowledge Utilization” figure. The individual and team processes involve taking diverging outlooks and looking at them through individual and team lenses to understand issues, trends and opportunities in the world, so that an aligned view of the environment is created, and profitable company strategies developed.

To create the most corporate value both the tacit and explicit knowledge processes must be coupled. This is shown in the complementarity of “Tacit and Explicit Knowledge” figure. Note that the arrows in this figure are drawn as one way, moving tacit knowledge into explicit knowledge. From a knowledge management standpoint this transfer from one type of knowledge to the other greatly improves productivity in decision-making.

It is the ability to develop a common means of making sense of the environment and having a common basis for communication along with a culture conducive to effective interdependence that creates corporate value as shown in the “Integration of Knowledge and Intellectual Capital Management Processes”figure.  This whole process is based on a change in fundamental beliefs. In the industrial era the belief was that if we can make it they will buy it. In the knowledge era the belief is that they will buy integrated solutions perceived as bringing superior value added. So rather than divide and subdivide work for greater efficiency it is now important to cluster capabilities and utilize cross discipline teams. Likewise rather than prospering by focusing on one’s own interest in a win-lose competitive context it is now more important a complement one’s own values and capabilities with those of others in a win-win context both within and between companies.  Further rather than creating value by transforming raw material into finished goods it is better to create value by building on ideas, including those of customers and suppliers. From an organizational standpoint rather than leading through hierarchical command and control methods the best practice is to now lead by fostering values in a culture based on interdependence.

Creating the most value for the Corporation requires a tight integration between the technology infrastructure, the corporation’s values,and the knowledge architecture. This is shown in the “Schematic View of Knowledge Management” figure. Note that for human systems the tasks needing to be done, as outlined in the figure, involve mostly soft human skills versus hard scientific or financial disciplines. Again with artificial intelligence entities improved capability to understand such soft values and correlate them to successful business decisions offers the promise of rapidly advancing corporate value. Thus the best practice will be to build knowledge into communities of practice in an interactive and dynamic way, which is driven by productive inquiry and centrally available to all individuals and artificial intelligence entities in a codified and accessible manner.

How Knowledge Management Builds Intellectual Capital

Intellectual Capital is a summation of several other forms of capital. These are human capital, relationship capital, intellectual assets, and time capital. Over time they all work with one another in the way shown in the “Forms of Intellectual Capital” figure. When thinking about knowledge management problems this view of an organization is much more useful than an organizational chart or financial spreadsheet to develop best practices. 

Knowledge Management Best-Practices Options

Best practices in knowledge management have changed over the last two decades. Of the four views of knowledge management, the first, business strategy, has stayed the same. That is because knowledge management still begins with the business objective. To be competitive in the knowledge economy,companies focus on key high-level strategies: the serve the customer, build an integrated value chain, accelerate product cycles, and add value with information, develop new markets, and maximize intellectual capital.

The second view, business processes, has also stayed the same.Knowledge management is a process that can be broken down to a series of subparts and examined in detail: knowledge capture, transformation,communication and utilization. With application of technology and business practices, each stage may be viewed as an opportunity for process improvement.

It is the third view, business technology, which has changed the most. Knowledge management’s specific software is now designed to capture,store and transfer tacit knowledge with increasing levels of sophistication.Likewise software specialties such as business intelligence, documentmanagement, search and retrieval, customer management, and collaborative software have all made huge strides over the last decade. What started in the1990s as crude database solutions have now evolved into a quite sophisticated software databases and artificial intelligence engines. As such the older versions of knowledge management from a technology standpoint are undergoing rapid change.  The best practices in this area even a few years ago are now obsolete.

The fourth viewpoint of knowledge management, business culture, is also changing. This is because the whole organizational model of the industrial economy is changing. In hierarchical organizations each departmental pillar, sales, finance, manufacturing, R&D, admin and more,was an island. In the knowledge enabled company, all the functions of the organization act in concert toward the same objectives. Flat, not silo,organizations are now best practice.                          

The eight capabilities needed for best practice knowledge management are:

1. The organization considers conversation to be the heart of the real work of knowledge creation and building of intellectual capital.

2. Members of the organization focus on principles and practices of good conversation as they engage with colleagues, customers, and suppliers.

3. The organization members consider one of their primary roles to be a convener host for conversations about questions that matter.

4. Members of the organization spend sufficient time discovering the right questions in relationship to the time spent finding the right answers.

5. Process tools and disciplines are systematically used to support good conversation.

6. The physical workspace or office area is designed to encourage informal interactions, good conversation and effective learning.

7. The organization has technology systems and professional resources devoted to harvesting the knowledge being cultivated at the grassroots level and making it accessible to others across the organization.

8. Sufficient training in the development budget is devoted to supporting informal learning conversations and sharing objective practices across organizational boundaries.

If knowing and knowledge are segmented to four segments as shown in the “Managing Knowing and Knowledge” figure, two sets of generic activities are best practices:

1. Designing and implementing techniques to identify and record both knowledge and ignorance (i.e. inventorying and auditing) and then designing processes to share, use and protect such knowledge and to remedy ignorance by learning or knowledge creation.

2. Designing and orchestrating contacts, environments and activities to discover and release what is not formally or explicitly known (i.e.socializing and experiencing) and perhaps coaching and encouraging people to be effective in these processes. Note that addressing the problem of not knowing what you don’t know may seem beyond the powers of formal knowledge management.However, innocent ignorance can be remedied through surprise encounters, with problems, with observations or with people. Often this is what we learn from experience: from doing new things, from visits to new places, or from handling unusual situations. Formal management in the domain of the bottom right cell of the figure could involve creating experiences for individuals and teams, i.e.something with life-changing, character building, or refreshing and renewing implications.

The focus of these best practices may seem like “soft” sciences;because such company’s tacit knowledge resides in informal communities, made up of people, places, and things. However it must be remembered the community interactions create business value because they responsibly and efficiently share four high-value activities. These are: SENSING: discovering capture. ORGANIZING:categorize and personalize. SOCIALIZE: collaborate and share. And finally INTERNALIZE:understand and create new knowledge.

Lastly it must be remembered that communities interact in both physical and virtual places. It was said by Bryan Bell of Lotus, “people come to communities for information and they stay for the people”. Tight Knit bonds are what make the building and sharing of tacit knowledge possible

As an example of how knowledge management practices relate to competitive strategies, consulting companies practices across the knowledge management spectrum provide insight to the extremes of R&D organizations. The practices focused on Personalization would best be applied to R&D Games in the upper left of the matrix, i.e. Technology Races. The practices focused on codification would best be used by R&D organizations playing R&D Games in the lower right of the matrix, i.e. Food & Clothing.

The “How Consulting Firms Manage Their Knowledge” figure shows the variation in Economic Model, Knowledge Management Strategy, Information Strategy, Information Technology, and Human Resources. As applied to R&D organizations what is most important is that each of these strategies are aligned, that is they are consistently up and down either the left or right column, not a mix!

Sources, References and Selected Bibliographic Information

1. “Anthropology and knowledge codification”, by Arian Ward & Beth Alexander, White Paper of Work Frontiers International, October 2000.
2. “Customer Knowledge Management” , by Rashi Glazer, the 3^rd Intangibles Conference, The Vincent Ross Institute of Accounting Research and the Intangibles Research Project at Stern School of Business, New York University,May 2000.
3. “Community Knowledge Sharing in Practice” by Daniel Bobrow, Robert Cheslow, and Jack Whalen, presentation to the 3^rd Intangibles Conference, The VincentRoss Institute of Accounting Research and the Intangibles Research Project at Stern School of Business, New York University, May 2000.
4. “Leading for Knowledge Value Creation”, presentation by Hubert Saint-Onge, the Mutual Group, circa 2000.
5. “Lenses: Four Views of KM” by unknown, Knowledge Management, October 1998.
6. “Conversation as a Core Business Process” by Juanita Brown and David Isaacs, The Systems Thinker, Jan. 1997.
7. “The Knowledge Management Connection”, White Paper by the IBM Lotus.
8. “Intellectual Capital”, slide by Work Frontiers, http://www.workfrontiers.com, 2000.
9. “What document management tool do you use?”, by IRI Community Forum, IRI blog, Mar. 2018.