Knowledge Management Processes
3.4 Taxonomy of KM
vulnerabilities in the PLF and supporting networks, specific locations of black propaganda creation and distribution, finances of certain funding organizations, and identification of specific operation cells within the Kryptanian government.
All of these refined issues challenge the team and begin a new spiral of explora- tion and creation by the team.
3.3.7 Summary
This example illustrates the emergent processes of knowledge creation over the several day ramp-up period of a distributed crisis intelligence team. The full spi- ral moved from team members socializing to exchange the tacit knowledge of the situation toward the development of explicit representations of their tacit knowledge. These explicit models allowed other supporting resources to be applied (analysts external to the group and on-line analytic tools) to link further evidence to the models and structure arguments for (or against) the models. As the models developed, team members discussed, challenged, and internalized their understanding of the abstractions, developing confidence and hands-on experience as they tested them against emerging reports and discussed them with team members and colleagues. The confidence and internalized understanding then led to a drive for further dialogue—initializing a second cycle of the spiral.
of tacit-explicit representation aids in the form of information visuali- zation and analysis tools, thinking aids, and decision support systems.
This area of KM focuses on the efficient networking of people and machine processes (such autonomous support processes are referred to asagents) to enable the shared reasoning between groups of people and their agents through computer networks. The barrier to achieving robustness in such KM processes is the difficulty of creating a shared contextof knowledge among humans and machines.
3. Processors. The third KM area is the technological development and implementation of computing networks and processes to enable explicit-explicit combination. Network infrastructures, components, and protocols for representing explicit knowledge are the subject of this fast-moving field. The focus of this technology area is networked computation, and the challenges to collaboration lie in the ability to sustain growth and interoperability of systems and protocols.
Table 3.8
Basic KM Taxonomy for the Intelligence Enterprise Intelligence Enterprise
KM: Acquiring, Creating, Maintaining, and Applying Knowledge to Achieve Organizational Objectives
Perspective of Knowledge Management
People
Operational View
Processes Human-Computer Interaction View
Processors Technical View Knowledge
Conversion
Socialization: tacit- to-tacit transactions
Externalization and internalization:
transactions between tacit and explicit
Combination: explicit- to-explicit transactions
Focus of the Enterprise
Operations, business processes, training
Tools, thinking aids, decision support, knowledge representa- tion and visualization
Infrastructure, knowledge, protocols
Basis of Collaboration
Networks of people (communities of practice): shared purpose, values, practice, knowledge
Networks of people and agents: shared reasoning and representation of tacit and explicit knowledge
Networked computation: shared configuration of con- tent in networks and nodes (computers) Barriers to
Collaboration and Interoperation
Culture (trust, values, vision)
Context Content and its
structure
Note that these three areas correspond to three basic descriptive views of the enterprise that will be subsequently introduced in Chapter 9.
The taxonomy can be further extended (Table 3.9) to consider the disci- plines and supporting tools and technologies in each of these three areas:
1. People. The objective of people-oriented disciplines is to create a knowledge-based organization that learns, shares, and creates knowledge collaboratively. The tools and technologies applied to this
Table 3.9
Taxonomy of Disciplines and Supporting Tools and Technologies Perspective
of Knowledge Management
People
Operational View
Processes Human-Computer Interaction View
Processors Technical View Objective Collaborative, learning
organization
Efficient HCI Effective human-computer networks Disciplines and
Areas of Research and Development
Collaboration for:
Knowledge sharing Problem solving eLearning Virtual teaming
HCI Human-agent collaboration
Knowledge presentation
Data capturing, representing, and warehousing Cognitive (reasoning) AI and machine learning
Networked computing Automation
Support Tools and Technologies
Virtual team establishment and support across time and space
Automatic experience capturing and linking (cases) to problems Auto training and eLearning
Data, information, and high-dimensionality knowledge presentation to humans, virtual, and artificial reality High-level abstract interaction between human and machine agents
Human-machine problem solving and workflow
Data representation, knowledge mapping to index, correlating, and linking (externalizing and internalizing) knowledge Search and retrieval
Data fusion and data mining
Decision support aids Analytic (thinking) tools Creativity and problem- solving support tools Multimedia Information retrieval, summarization, and abstraction
discipline range from collaborative services to create virtual (distrib- uted) teams and supporting services to eLearning tools to integrate learning into the work process.
2. Processes. HCI and related disciplines have the objective of achieving efficient human-machine interaction, enabling humans-agent teams to smoothly exchange tacit and explicit knowledge. Tools that support this process include virtual- and artificial-reality visualizations (and multisensory presentations), human-machine conversation, and autonomous agent services to search and explore large data volumes.
3. Processors.Effective computer networks are the objective of the diverse computing disciplines that support KM: enterprise architecting, net- worked computing infrastructure, data warehousing, services for information management, collaboration, cognitive (reasoning) sup- port, and knowledge distribution.
Because the KM field can also be described by the many domains of exper- tise (or disciplines of study and practice), we can also distinguish five distinct areas of focus (Table 3.10) that help describe the field. The first two disciplines view KM as a competence of people and emphasize making people knowledgeable:
1. Knowledge strategists. Enterprise leaders, such as the chief knowledge officer (CKO), focus on the enterprise mission and values, defining value propositions that assign contributions of knowledge to value (i.e., financial or operational). These leaders develop business models to grow and sustain intellectual capital and to translate that capital into organizational values (e.g., financial growth or organizational perform- ance). KM strategists develop, measure, and reengineer business processes to adapt to the external (business or world) environment.
2. Knowledge culture developers. Knowledge culture development and sustainment is promoted by those who map organizational knowledge and then create training, learning, and sharing programs to enhance the socialization performance of the organization. This includes the cadre of people who make up the core competencies of the organiza- tion (e.g., intelligence analysis, intelligence operations, and collection management). In some organizations a chief learning officer (CLO) is designated this role to oversee enterprise human capital, just as the chief financial officer (CFO) manages (tangible) financial capital.
The next three disciplines view KM as an enterprise capability and empha- size building the infrastructure to make knowledge manageable:
3. KM applications.Those who apply KM principles and processes to spe- cific business applications create both processes and products (e.g., software application packages) to provide component or end-end serv- ices in a wide variety of areas listed in Table 3.10. Some commercial KM applications have been sufficiently modularized to allow them to be outsourced to application service providers (ASPs) [20] that
Table 3.10 The Disciplines of KM
Knowledge Perspective Discipline The KM Disciplines: Key Areas of Focus Making People
Knowledgeable (KM as a Competence)
1. Knowledge strategy
Chief information officer (CIO)/CKO mission, values, value propositions
Intellectual capital, knowledge metrics Knowledge capital management: human capital (know-how) and structural capital (business process, know-what)
eBusiness process engineering and reengineering Business modeling business process rules 2. Knowledge
(learning) culture developers
Chief learning officer (CLO)
Knowledge sharing, exchange, and collaboration Virtual teams, communities of practice Best practices, training, e-learning Problem solving, storytelling Making Knowledge
Manageable (KM as a Capability)
3. KM applications
Program management (PM), intellectual capital management (ICM)
Supply chain management (SCM) Customer relationship management (CRM) Content/document management (CM/DM) Business and competitive intelligence (BI/CI) 4. Enterprise
architecture
Data storage, warehousing
KM services, tools (e.g., collaboration, cognition) KM architectures
5. Technology and tools
Knowledge capture, search, mapping Knowledge storage and dissemination Content management
Collaboration, personalization
Problem solving, decision aiding, decision making Fusion and mining, analysis
“package” and provide KM services on a per-operation (transaction) basis. This allows some enterprises to focus internal KM resources on organizational tacit knowledge while outsourcing architecture, infra- structure, tools, and technology.
4. Enterprise architecture. Architects of the enterprise integrate people, processes, and IT to implement the KM business model. The archi- tecting process defines business use cases and process models to develop requirements for data warehouses, KM services, network infrastructures, and computation.
5. KM technology and tools. Technologists and commercial vendors develop the hardware and software components that physically imple- ment the enterprise. Table 3.10 provides only a brief summary of the key categories of technologies that make up this broad area that encompasses virtually all ITs.
Within the community of intelligence disciplines, each of these five areas can be identified in the conventional organizational structure, but all must be coordinated to achieve an enterprisewide focus on knowledge creation and shar- ing. In subsequent chapters, we detail each of these discipline areas as applied to the intelligence enterprise.