Knowledge can be shared through personal communication and interaction, as we saw in the first quadrant, socialization, of the Nonaka and Takeuchi KM model. This occurs naturally all the time and is very effective, though rarely is it cost-effective. Knowledge codification is the next stage of leveraging knowl- edge. By converting knowledge into a tangible, explicit form such as a docu- ment, that knowledge can be communicated much more widely and with less cost. Interaction is limited in scope to those within hearing or able to have face-to-face contact. Documents can be disseminated widely over a corporate intranet, and they persist over time, which makes them available for reference as and when they are needed, both by existing and by future staff. They con- stitute the only “real” corporate memory of the organization.
There are, of course, costs and difficulties associated with knowledge codi- fication. The first issue is that of quality, which encompasses (1) accuracy, (2) readability/understandability, (3) accessibility, (4) currency, and (5) authority/
credibility.
Knowledge codification serves the pivotal role of allowing what is collec- tively known to be shared and used. Knowledge held by a particular person enables that person to be more effective. If people interact to share their knowl- edge within a community of practice, then that practice becomes more effec- tive. If knowledge is codified in a material way (i.e., it is rendered explicit), then it can be shared more widely in terms of both audience and time dura-
tion. Knowledge must be codified in order to be understood, maintained, and improved upon as part of corporate memory. The codification of explicit knowledge can be achieved through a variety of techniques such as cognitive mapping, decision trees, knowledge taxonomies, and task analysis.
Cognitive Maps
Once expertise, experience, and know-how have been rendered explicit, typ- ically through some form of interviewing, the resulting content can be repre- sented as a cognitive map. A cognitive or knowledge map is a representation of the “mental model” of a person’s knowledge and provides a good form of codified knowledge. A mental model is a symbolic or qualitative representa- tion of something in the real world. It is how human minds process and make sense of their complex environments. A cognitive map is a powerful way of coding this captured knowledge because it also captures the context and the complex interrelationships between the different key concepts. It is in fact also very important to include individual views, perceptions, judgments, hypothe- ses, and beliefs as they form part of the interviewee’s subjective worldview. The nodes in a map are the key concepts, and the links represent the interrela- tionships between the concepts. These may be drawn manually by taping small note pages on a wall, a whiteboard, or visualization software, ranging from simple brainstorming mapping tools to 3-D depictions. Figure 4-6 shows an example of a cognitive map in response to the question: “describe the major differences between tacit and explicit knowledge objects.”
Cognitive mapping is based on concept mapping (Leake et al., 2003), which allows experts to directly construct knowledge models. Concept maps repre- sent concepts and relations in a two-dimensional graphical form, with nodes representing key concepts connected by links representing propositions. These are quite similar to semantic networks used by such diverse disciplines as lin- guistics, education, and knowledge-based systems. The goal of such systems is
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XAMPLE OF A CONCEPT MAPCodified
Tacit Knowledge Object Explicit
Knowledge Object Location
Format
Language Print/Electronic
Knowledge Worker
Shares
Accesses Originator/
Creator
Subject Matter Expert Practitioner Sources
Experiences with
References
to better organize explicit knowledge and to store it in corporate memory for long-term retention.
Another widely used tool for explicit knowledge coding is the Common- KADS methodology (Schreiber et al., 2000; Shadbolt, O’Hara, and Crow, 1999), which is a knowledge engineering methodology centered on six models of an organization:
1. Task model of the organization’s business processes.
2. Agent model of the executors’ use of knowledge, both human and arti- ficial, to carry out the various tasks in the organization.
3. Knowledge model that explains in detail the knowledge structures and types required for performing tasks.
4. Communication model that models the communicative transactions between agents.
5. Design model that specifies the architectures and technical requirements needed to implement a system that embodies the functions detailed by the knowledge and communication models.
In order to implement KADS, the organization is analyzed to identify knowledge-oriented problems, describe the organizational aspects that may affect knowledge solutions (e.g., culture, resources), and describe the business processes in terms of agents required, location, knowledge assets deployed, and measures of knowledge intensiveness and significance (e.g., mission criticality).
Next the knowledge used in the organization is described in terms of posses- sors and processes used, whether or not it is in the right form and location, of the right quality, and available at the right times. The feasibility of suggested solutions is then checked against the knowledge problems identified in the first step. This approach allows a systematic cost-benefit analysis to be carried out for the processes of knowledge capture.
Decision Trees
Decision trees are another widely used method to codify explicit knowledge.
This representation is both compact and efficient. The decision tree is typically in the form of a flowchart, with alternate paths indicating the impact of dif- ferent decisions being made at that juncture point. A decision tree can repre- sent many “rules,” and when you execute the logic by following a path down it, you are effectively bypassing rules that are not relevant to the case in hand.
You do not have to look at every rule to see if it “fires,” and you also take the shortest route to the correct outcome. Their graphical nature makes them very easy to understand, and they are obviously very well suited for the coding of process knowledge. An example would be a preventive maintenance process for factory equipment. The captured knowledge from maintenance workers could be coded in a decision tree to help future maintenance workers carry out parts replacement and other work on a schedule-based decision rather than reacting to parts becoming worn out. Another example, shown in Figure 4-7, helps guide the decision to consolidate or to develop a new product as a risk management decision tree.
Knowledge Taxonomies
Concepts can be viewed as the building blocks of knowledge and expertise.
We each use our own internal definitions of concepts to make sense of the world around us. Once key concepts have been identified and captured, they can be arranged in a hierarchy that is often referred to as a structural knowl- edge taxonomy. Knowledge taxonomies allow knowledge to be graphically represented in such a way that it reflects the organization of concepts within a particular field of expertise or for the organization at large. A knowledge dictionary is a good way to keep track of key concepts and terms that are used. This may be compiled as you acquire and code knowledge. It should clearly define and clarify the professional “jargon” of the subject matter domain.
Taxonomies are basic classification systems that enable us to describe con- cepts and their dependencies—typically in a hierarchical fashion. The higher up the concept is placed, the more general or generic the concept is. The lower the concept is placed, the more specific an instance it is of higher-level cate- gories. An example is shown in Figure 4-8.
An important concept that underlies taxonomies is the notion of inheritance.
Each node is a subgroup of the node above it, which means that all of the properties of the higher-level node are automatically transferred from “parent”
to “child.” As shown in Figure 4-8, if the higher-level node is a houseplant and the lower-level nodes are foliage and flowering plants, both of these two subgroups possess all the characteristics of houseplants. In fact, taxonomies originated as biological classification schemes.
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XAMPLE OF A DECISION TREENew Product Consolidation
Thorough Development Rapid
Development Repurpose
Product Strengthen
Product
Market Reaction
Good
Moderate Good Poor
Moderate Good Poor
Moderate
Poor Good
Moderate Poor
Taxonomies are most useful in the organization of declarative knowledge such as that embodied by diagnostic systems. The construction of a taxonomy involves identifying, defining, comparing, and grouping elements. Organiza- tional knowledge taxonomies, however, are driven not by basic first principles or “real” attributes but by consensus. All the organizational stakeholders need to agree on the classification scheme to be used to derive the taxonomy—it cannot be theoretical but empirical. This is how we code this type of knowl- edge in our work.
A number of concept sorting techniques may be used in coding organiza- tional knowledge, ranging from manual to completely automated processes.
An example of a manual process would be to have participants sort cards into groupings. An automated example would be something like the RepGrid technique developed by Shaw (1981) based on Kelly’s (1955) personal con- struct theory. Most automated systems use a form of cluster analysis to iden- tify groupings in a set of data (e.g., hierarchical cluster analysis; see Johnson, 1967), multidimensional scaling (e.g., Kruskal, 1977), or network scaling (e.g., Schvaneveldt, Durso, and Dearholt, 1985). Cluster analysis is a method of pro- ducing classifications from data that is initially unclassified. In hierarchical cluster analysis, the groupings are arranged in the form of a hierarchical tree.
Repertory grid analysis is a technique based on a theory that states each person functions as a scientist who classifies or organizes his or her world. Based on these classifications, the individual is able to construct theories and act based on these theories. A repertory grid depicts this theoretical framework for a
F
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XAMPLE OF A KNOWLEDGE TAXONOMYPlants
Houseplants Landscaping plants Native/Wild plants
Foliage Flowering
Cacti
Trees Ground
cover
Deciduous Evergreen
given individual. The different taxonomic approaches to the codification of explicit knowledge are summarized in Table 4-2.
When creating a knowledge taxonomy of the organization, it is vitally important to identify content owners. This helps ensure that content will always be kept up to date. The organization will also have a clear idea of which staff members are holders of specialized knowledge. This knowledge taxon- omy (sometimes called a knowledge map) should also make use of metadata, tagging on “information about information”—for example, tagging content with content owners, “best before” dates, classification information such as key words, business-specific information such as intended audience, and verti- cal industry addressed. An illustration follows.
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AJORT
AXONOMICA
PPROACHES TOK
NOWLEDGEC
ODIFICATIONTaxonomic Approach Key Features
Cognitive or concept map ■ Key content represented as a node in a graph, and the relationships between these key concepts are explicitly defined.
■ Can show multiple perspectives or views on the same content.
■ Fairly easy to produce and intuitively simple to understand but difficult to use for knowledge related to procedures.
Decision tree ■ Hierarchical or flowchart type of representation of a decision process.
■ Very well suited to procedural knowledge—less able to capture conceptual interrelationships.
■ Easy to produce and easy to understand.
Manual knowledge taxonomy ■ Object-oriented approach that allows lower or more specific knowledge to automatically incorporate all attributes of higher-level or parent content they are related to.
■ Very flexible—can be viewed as a concept map or as a hieararchy.
■ More complex; will therefore require more time to develop as it must reflect user consensus.
Automated knowledge taxonomy ■ A number of tools are now commercially available for taxonomy construction.
■ Most are based on statistical techniques such as cluster analysis to determine which types of content are more similar to each other and can constitute subgroups or thematic sets.
■ Good solution if there is a large amount of legacy content to sort through.
■ More expensive and still not completely accurate—will need to validate and refine for maximum usefulness.
Information professionals, as well as journalists and professional writers, are the ideal candidates to capture knowledge and develop knowledge taxonomies, as it is within the realm of library and information science skill sets. Captur- ing organizational knowledge is almost always a process of adding value to the original content. Restructuring and rewriting, for example, are ways of directly increasing the value of organizational knowledge assets. By using pro- fessional writers, key information can be distilled into a more effective form.
This process will also identify knowledge gaps and provide a mechanism for filling them. The act of analyzing and reworking the information will help clarify what the organization knows and what it needs to know. It is neither necessarily cheap nor easy, but it will capture key knowledge, improve consis- tency, and enhance generalizability throughout the organization. Writing good content is the best way of creating knowledge assets within an organization.