Organizational Learning

The Knowledge-Based Intelligence Organization

4.2 Organizational Learning

Educators frequently cite futurist Alvin Toffler’s remarks on the preeminence of learning in his 1970 bestseller,Future Shock:“The illiterate of the 21st Century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” Toffler foresaw that rapid technological change and globalization would demand greater agility and flexibility in learning—the rapid creation and application of relevant knowledge that creates value. Such agility and flexibility requires learning to be a continuous and lifelong process—a fun- damental discipline of the knowledge-based organization.

Peter Senge’s 1990 classic,The Fifth Discipline, articulated and popular- ized the concept of the learning organization “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together” [19]. Senge asserted that the fundamental distinction between traditional controlling organizations and adaptive self-learning organizations are five key disciplines including both virtues (commitment to personal and team learning, vision shar- ing, and organizational trust) and skills (developing holistic thinking, team learning, and tacit mental model sharing). Senge’s core disciplines, moving from the individual to organizational disciplines, included:

• Personal mastery. Individuals must be committed to lifelong learning toward the end of personal and organization growth. The desire to learn must be to seek a clarification of one’s personal vision and role within the organization.

• Systems thinking.Senge emphasized holistic thinking, the approach for high-level study of life situations as complex systems. An element of learning is the ability to study interrelationships within complex

dynamic systems and explore and learn to recognize high-level patterns of emergent behavior. (We discuss this in greater detail in Section 4.4.)

• Mental models. Senge recognized the importance of tacit knowledge (mental, rather than explicit, models) and its communication through the process of socialization. The learning organization builds shared mental models by sharing tacit knowledge in the storytelling process and the planning process. Senge emphasized planning as a tacit- knowledge sharing process that causes individuals to envision, articu- late, and share solutions—creating a common understanding of goals, issues, alternatives, and solutions.

• Shared vision. The organization that shares a collective aspiration must learn to link together personal visions without conflicts or competition, creating a shared commitment to a common organizational goal set.

• Team learning. Finally, a learning organization acknowledges and understands the diversity of its makeup—and adapts its behaviors, pat- terns of interaction, and dialogue to enable growth in personal and organizational knowledge.

It is important, here, to distinguish the kind oftransformational learning that Senge was referring to (which brings cultural change across an entire organization), from the smaller scale group learning that takes place when an intelligence team or cell conducts a long-term study or must rapidly “get up to speed” on a new subject or crisis. Large-scale organizationwide transformational learning addresses the long-term culture changing efforts to move whole organi- zations toward collaborative, sharing cultures. Group learning and Senge’s per- sonal mastery, on the other hand, includes the profound and rapid growth of subject matter knowledge (intelligence) that can occur when a diverse intelli- gence team collaborates to study an intelligence target. In the next subsections, we address the primary learning methods that contribute to both.

4.2.1 Defining and Measuring Learning

The process of group learning and personal mastery requires the development of both reasoning and emotional skills. The level of learning achievement can be assessed by the degree to which those skills have been acquired. Researcher Ben- jamin Bloom and a team of educators have defined a widely used taxonomy of the domains of human learning: knowledge, attitude, and skills (KAS) [20].

These three areas represent the cognitive (or mental skills), affective (attitude or emotional skills), and psychomotor (manual or physical movement) domains of human learning.

The taxonomy of cognitive and affective skills can be related to explicit and tacit knowledge categories, respectively, to provide a helpful scale for meas- uring the level of knowledge achieved by an individual or group on a particular subject. The levels of learning can be applied to the states of knowledge devel- oped by an intelligence team on a particular problem. Table 4.5 compares the cognitive learning levels, ordered from simple to complex following the Bloom model, for a typical intelligence problem to illustrate the gradation of cognitive intelligence skills.

4.2.2 Organizational Knowledge Maturity Measurement

The goal of organizational learning is the development of maturity at the organ- izational level—a measure of the state of an organization’s knowledge about its domain of operations and its ability to continuously apply that knowledge to increase corporate value to achieve business goals.

Carnegie-Mellon University Software Engineering Institute has defined a five-level People Capability Maturity Model® (P-CMM ®) that distinguishes five levels of organizational maturity, which can be measured to assess and

Table 4.5

Cognitive, Explicit Learning Domain Skills Applied to Intelligence Cognitive

Explicit Knowing Mental Reasoning Skills

Intelligence Example:

Foreign Threat Analysis 1. Knowing—retaining and recalling data,

information, and knowledge

1. Knows the foreign nation-state authorities, government, organization, and military command structure.

2. Comprehending—interpreting problems, translating and relating data to information, assigning meaning

2. Comprehends the relationships and influences between government organizations and actors;

comprehends the relative roles of all players 3. Applying—applying concepts from one

situation to another, reasoning about cases and analogies

3. Applies experiences of similar and previous governments to reason about formation of policy and intentions.

4. Analyzing—decomposing concepts into components and relationships

4. Analyzes government policy statements and raw intelligence data; links all data to organizations and actions

5. Synthesizing—constructing concepts from components and assigning new meanings

5. Synthesizes models of national leadership in- tention formation, planning, and decision making 6. Evaluating—making judgments about the

values of concepts for decision making

6. Evaluates models and hypotheses, comparing and adapting models as time progresses to asses the utility of models and competing hypotheses

quantify the maturity of the workforce and its organizational KM performance.

The P-CMM® framework can be applied, for example, to an intelligence organization’s analytic unit (Table 4.6) to measure current maturity and develop strategy to increase to higher levels of performance [21]. The levels are successive plateausof practice, each building on the preceding foundation. The P-CMM®

provides a quantitative tool to measure and improve individual competencies, develop effective collaborative teams, and motivate improved organizational performance.

An organization may estimate its maturity, unit by unit, to contribute to intellectual capital estimation and to focus its learning investments (formal and informal). The highest level of optimized performance requires continual meas- urement of the effectiveness of intelligence processes. One of the benefits of

Table 4.6

Capability Maturity Levels of Intelligence Analysis Maturity

Level

Key Practices Characterizing the Maturity Level

Representative Practices Applied to the Discipline of Intelligence Analysis 1. Initial Inconsistent management of the

workforce

Ad hoc approach to problem solving across the organization

Ad hoc mentoring; lack of standard approaches, processes, or training across the analytic workforce

No collaboration in learning or analytic problem solving; different analytic standard applied across different units

2. Managed Focus on management of people Workforce performance management;

evaluation of labor per unit of intelligence product delivered

3. Defined Focus on management of competency of the workforce

Introduction of analytic processes, training, and evaluation of personnel competency, growth

Evaluation of analytic performance;

accuracy of intelligence 4. Predictable Focus on management of capabilities

Workforce is empowered, and practices are measured

Standard analytic processes in place, with training and capability measurement Evaluation of effectiveness: metrics used to evaluate analysis utility to customers 5. Optimized Focus on management of continuous

change and improvement

Practices are measured and improved to deliver higher value

Continuous characterization of intelligence problem environment and adaptation of mission

Continuous measurement and closed-loop adaptation of analytic processes against changing mission and customer values

formal e-learning systems to be discussed in the next section is the ability to measure, capture, and track the achieved skill levels of individuals within the organization to contribute to the measurement of organizational maturity. Simi- larly, the CRM systems introduced in the last chapter provide a tool to measure the intelligence consumer satisfaction with delivered intelligence.

4.2.3 Learning Modes

The organizational learning process can be formal (e.g., classroom education or training) or informal (e.g., hands-on, day-to-day experience). In the following paragraphs, we describe each of these processes and their roles in organizational learning in the intelligence organization.

4.2.3.1 Informal Learning

We gain experience by informal modes of learningon the job alone, with men- tors, team members, or while mentoring others. The methods of informal learn- ing are as broad as the methods of exchanging knowledge introduced in the last chapter. But the essence of the learning organization is the ability to translate what has been learned into changed organizational behavior. David Garvin has identified five fundamental organizational methodologies that are essential to implementing the feedback from learning to change; all have direct application in an intelligence organization [22].

1. Systematic problem solving. Organizations require a clearly defined methodology for describing and solving problems, and then for imple- menting the solutions across the organization. Methods for acquiring and analyzing data, synthesizing hypothesis, and testing new ideas must be understood by all to permit collaborative problem solving.

(These methods are described in Section 4.4 of this chapter.) The process must also allow for the communication of lessons learned and best practices developed (the intelligence tradecraft) across the organization.

2. Experimentation. As the external environment changes, the organiza- tion must be enabled to explore changes in the intelligence process.

This is done by conducting experiments that take excursions from the normal processes to attack new problems and evaluate alternative tools and methods, data sources, or technologies. A formal policy to encour- age experimentation, with the acknowledgment that some experiments will fail, allows new ideas to be tested, adapted, and adopted in the normal course of business, not as special exceptions. Experimentation can be performed within ongoing programs (e.g., use of new analytic tools by an intelligence cell) or in demonstration programs dedicated

to exploring entirely new ways of conducting analysis (e.g., the crea- tion of a dedicated Web-based pilot project independent of normal operations and dedicated to a particular intelligence subject domain).

3. Internal experience. As collaborating teams solve a diversity of intelli- gence problems, experimenting with new sources and methods, the lessons that are learned must be exchanged and applied across the organization. This process of explicitly codifying lessons learned and making them widely available for others to adopt seems trivial, but in practice requires significant organizational discipline. One of the great values of communities of common practice is their informal exchange of lessons learned; organizations need such communities and must support formal methods that reach beyond these communities. Learn- ing organizations take the time to elicit the lessons from project teams and explicitly record (index and store) them for access and application across the organization. Such databases allow users to locate teams with similar problems and lessons learned from experimentation, such as approaches that succeeded and failed, expected performance levels, and best data sources and methods.

4. External sources of comparison.While the lessons learned just described applied to self learning, intelligence organizations must look to exter- nal sources (in the commercial world, academia, and other cooperating intelligence organizations) to gain different perspectives and experi- ences not possible within their own organizations. A wide variety of methods can be employed to secure the knowledge from external per- spectives, such as making acquisitions (in the business world), estab- lishing strategic relationships, the use of consultants, establishing consortia. The process of sharing, then critically comparing qualitative and quantitative data about processes and performance across organi- zations (or units within a large organization), enables leaders and process owners to objectively review the relative effectiveness of alter- native approaches.Benchmarkingis the process of improving perform- ance by continuously identifying, understanding, and adapting outstanding practices and processes found inside and outside the organization [23]. The benchmarking process is an analytic process that requires compared processes to be modeled, quantitatively meas- ured, deeply understood, and objectively evaluated. The insight gained is an understanding of how best performance is achieved; the knowl- edge is then leveraged to predict the impact of improvements on over- all organizational performance.

5. Transferring knowledge. Finally, an intelligence organization must develop the means to transfer people (tacit transfer of skills,

experience, and passion by rotation, mentoring, and integrating process teams) and processes (explicit transfer of data, information, business processes on networks) within the organization. In Working Knowledge [24], Davenport and Prusak point out that spontaneous, unstructured knowledge exchange (e.g., discussions at the water cooler, exchanges among informal communities of interest, and dis- cussions at periodic knowledge fairs) is vital to an organization’s suc- cess, and the organization must adopt strategies to encourage such sharing.

Notice that each of these activities contribute to moving individuals and teams around the learning spiral of Noinaka and Tageuchi (introduced in the last chapter) by encouraging discussion (socialization), explicit description (externalization), analysis and evaluation (combination), and dissemination of results (internalization).

4.2.3.2 Formal Learning

In addition to informal learning, formal modes provide the classical introduc- tion to subject-matter knowledge. For centuries, formal learning has focused on a traditional classroom model that formalizes the roles of instructor and student and formalizes the learning process in terms of courses of study defined by a syl- labus and learning completion defined by testing criteria. The advent of elec- tronic storage and communication has introduced additional formal learning processes that allow the process to transcend space-time limitations of the tradi- tional classroom. Throughout the 1980s and 1990s, video, communication, and networking technologies have enabled the capture, enhancement, and distribu- tion ofcannedandinteractiveinstructional material. These permit wider distri- bution of instructional material while enriching the instruction with student interaction (rather than passive listening to lectures). Information technologies have enabled four distinct learning modes that are defined by distinguishing both the time and space of interaction between the learner and the instructor (Figure 4.4) [25]:

1. Residential learning (RL). Traditional residential learning places the students and instructor in the physical classroom at the same time and place. This proximity allows direct interaction between the student and instructor and allows the instructor to tailor the material to the students.

2. Distance learning remote (DL-remote). Remote distance learning pro- vides live transmission of the instruction to multiple, distributed loca- tions. The mode effectively extends the classroom across space to reach

a wider student audience. Two-way audio and video can permit lim- ited interaction between extended classrooms and the instructor.

While RL and DL-remote synchronize instruction and learning at the same time, the next two modes are asynchronous, allowing learning to occur at a time and place separate from the instructor’s presentation.

3. Distance learning canned (DL-canned). This mode simply packages (or cans) the instruction in some media for later presentation at the stu- dent’s convenience (e.g., traditional hardcopy texts, recorded audio or video, or softcopy materials on compact discs) DL-canned materials include computer-based training courseware that has built-in features to interact with the student to test comprehension, adaptively present material to meet a student’s learning style, and link to supplementary materials to the Internet.

4. Distance learning collaborative (DL-collaborative). The collaborative mode of learning (often described as e-learning) integrates canned material while allowing on-line asynchronous interaction between the student and the instructor (e.g., via e-mail, chat, or videoconference).

Collaboration may also occur between the student and software agents (personal coaches) that monitor progress, offer feedback, and recommend effective paths to on-line knowledge.

Different Same

Time of instruction-learning

1. Residential learning

2. Distance learning (DL)—remote classroom

3. DL—canned

4. DL—

collaboration

Asynchronous learning Synchronous

learning

SameDifferent

Instructor-studentplace

Figure 4.4 The major formal learning modes.

Of course, the DL modes may be combined in a course package to allow periodic synchronous instruction orlive labevents interspersed between periods of asynchronous learning. The asynchronous mode may also include interactive simulations (e.g., analytic problem games) to develop and evaluate student skills (and measure performance). The advantages of traditional RL include the direct socialization between student and instructor to exchange tacit knowledge, as the instructor adapts to the learning style of the student. The advantage of inte- grated DL modes, of course, is the ability to deliver cost-effective training to a widely distributed student body that gives students the flexibility to learn at their own time, place, and pace. DL collaborative learning systems can perform preassessments of students, then personalize the lesson plan to a student’s skills and styles, then perform postassessments to verify the skills mastered. This data may also be automatically registered in the corporate knowledge map of employee skills.

Intelligence organizations, as premier knowledge institutions, must apply each of these modes to provide the analytic workforce with the tools to rapidly gain the skills necessary to maintain competency in the changing world environment.