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.