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.