Management: Knowledge Information Strategy:
Business Intelligence as Organizational Intelligence
By Professor Emeritus Akira Ishikawa
Former Dean, GSIPEB
Research Fellow, ICC Institute,
Doctoral Program Chair
1. Definition of Organizational Intelligence
In my previous books, I made a distinction between data, information, and intelligence (Ishikawa, 1986, 1988a, 1988b, 1993a, 1993b), based on ideas related to data processing, value addition, and decision making. In particular, I stated that if we classify decision-making into operational decision-making, managerial decision-making, and strategic decision-making, the corresponding types of information needed for each decision-making are, respectively, data, information, and intelligence. Therefore, according to this framework, strategic decision-making requires the highest quality of information, i.e., intelligence, especially business intelligence (BI) in the private sector.
The term “intelligence” thus connotes knowledge information, intelligently processed information, and, ideally, is equivalent to wisdom. Over the years, a new term has emerged: Organizational Intelligence. Matsuda (1994), an advocate of Organizational Intelligence, defines it as interaction, accumulation, and integration of human intelligence and machine intelligence that are inherent in any organization. He also divides organizational intelligence into processes and products. However, he does not explicitly state that organizational intelligence is derived from organizations, rather than individuals.
When we say organizational intelligence, we generally refer to intelligence generated by groups within an organization, rather than by organizations as a whole. Certainly, “group organization” cannot be ignored in the process of organizational intelligence production, and we should not disregard the fact that intelligence acquired by individuals has led to great inventions and discoveries in the past.
Another source of organizational intelligence is strategic partnerships or alliances. It should be noted that as people and companies become more closely linked through intricate networks, boundaries for organizational intelligence are increasingly blurred. In this current environment, knowledge is shared more frequently and to a greater extent than before. Also, it is possible that changes in organizational relationships (for example, from a loose alliance to M&A) would greatly influence the amount and quality of meta-organizational intelligence (intelligence generated by multiple organizations).
As will be discussed in this chapter, organizations have to design networks, make effective use of these networks, and continually improve them, in order to accumulate the necessary wisdom for generating organizational intelligence most effectively.
2. Production, Use, and Evaluation of Organizational Intelligence
Ishikawa, et al. (1991) compare management issues in collecting, analyzing, and evaluating knowledge information, i.e., intelligence, with those on information. In the collection phase, one of the key issues is how to speedily collect or produce intelligence.
It is the general assumption that the more processed or value-added information is, the closer it is to becoming useful intelligence. This is not always the case, however. Also, what is effective for individuals in creating intelligence does not always prove to be effective for organizations.
One good example is as follows; developing an expert system for financial analysis, which produced intelligence, led to a 40% reduction in a company’s personnel cost, while the rate of return decreased by 60%. Why? Because while one department gained in productivity by implementing the expert system, which helped to cut down on the number of employees, in another department, where the redundant staff had been relocated and retrained, personnel cost increased more than the increase in sales. Therefore, the overall profit decreased more than in the previous period.
An effective and overall analysis of intelligence may help to resolve this dilemma. However, it is difficult to determine an appropriate time span for assessing this case; the downturn in the profit might prove temporary or long-term. Another important factor is the versatility of the developed software. If the software can be used in other departments as well, the cost reduction will be greater.
Another example of
organizational intelligence involves a huge database of organic compounds for
R&D called SPHINCS. Fujifilm Company,
The original purpose of this
database was to develop sensitized materials. However, a certain laboratory at
There are two lessons to be drawn from these examples. Firstly, analysis and assessment are not independent of each other. Comprehensive analyses will inevitably include some form of evaluation. Cursory analyses, in contrast, are generally not of much use to management, unless the time span and know-how are clearly defined.
Secondly, the intelligence produced, used, and evaluated by an individual or a department is not always applicable in another department or throughout the entire organization. Therefore, organizational intelligence needs to be designed and developed on an organization-wide basis so that it can be utilized throughout the organization. If the expert system for financial analysis mentioned earlier could be used in all the departments of the organization, then they would all achieve the 40% reduction in personnel cost, which would make the total cost reduction a considerable sum. Companies with successful production, use, and evaluation of organizational intelligence include Microsoft, Intel, and Nintendo, among others.
3. Tools for Organizational Intelligence
The methods or techniques for producing organizational intelligence can be broadly divided into two kinds: the arts-oriented and the science-oriented. The former can also be viewed as a user-oriented approach, and the latter as a developer-oriented approach.
The former includes, for example, Value Chain Analysis and Critical Success Factor approach; its basic purpose is to find out what would help companies secure a competitive advantage against their competitors and end up victorious in a corporate war. The latter, on the other hand, includes tools such as Business Process Reengineering (BPR), Electronic Data Interchange (EDI), Groupware, RFID, and various BI suites and platforms. Its aim is to bring innovative concepts, solutions and standardization to organizations. To use a linguistic metaphor, the former is semantic, while and the latter is syntactic.
Of course, this kind of dichotomy cannot cover every single technique and method. Since approaches for creating organizational intelligence are likely to be new and novel, and are related to creation and discovery, we have to look into these areas as well. Hence, if we assume that the ultimate goal of organizational intelligence is related to creativity, we have to examine as many creativity tools as possible.
Takahashi (1993) says that
there are more than 300 kinds of creativity techniques, and he introduces 91
techniques. He classifies the creativity techniques into four categories: the
divergent thinking methods such as Brainstorming and Checklist methods; the
convergent thinking methods such as the KJ and PERT methods; combinations of
these two methods such as Work Design Method; and the attitudinal methods which
encompass Transactional Analysis and Creative Dramatics (Takahashi, 1993, pp.
253–255). He further states that while Brainstorming is one of the most
well-known methods in the world, the KJ method and the NM method are most
In the next section, more details of idea-generating or idea-creating methods will be explored that should facilitate the development and enhancement of GDSS, NSS, and Electronic Meeting System (EMS) (Ishikawa, 1968; Ishikawa et al., 1981a, 1981b; Ishikawa and Mieno, 1992; Ishikawa and Hirota, 1994; Takahashi, 1993; and Nunamaker et al., 1991).
4. Idea Generation Methods
Idea generation methods, based on the four categories mentioned above, will be further examined here, starting with the divergent thinking methods.
4.1. Divergent idea generation methods
The major methods in this school include Free Association, Forced Association, and Analogous Idea Association. Free Association Methods are further classified into Brainstorming (Parnes and Harding, 1962), Card BS (Takahashi, 1987), Brainwriting (Geschka and Schlicksuppe, 1971), and Electronic Brainstorming (Nunamaker et al., 1991).
Card BS, Brainwriting, and Electronic Brainstorming are derivatives of Brainstorming, which is one of the most well-known creativity methods. Card BS uses cards in the process of Brainstorming to help participants to create as many ideas as possible, to think multilaterally, and to draw on each other’s ideas. Brainwriting, on the other hand, is called the 6-3-5 method because six participants offer three ideas each in five minutes. One of the characteristics of this method is the silent ambience; silent, individual thinking is blended with group thinking.
Electronic Brainstorming is a method of brainstorming made possible by computer technology; its originality lies not in the method itself but in the drastic new environment in which the method is used. With the new computer technologies, ideas can be generated in parallel within a given period of time, and reorganized online more effectively.
Forced Association Methods include Morphological Analysis (Allen, 1966), Checklist (R&D Guidebook Editorial Committee, 1973), Matrix (Takahashi, 1989a), Attributes Identification (Ueno, 1959), Desirable Point Identification (Takahashi, 1984), and the Gordon method (Whiting, 1958).
Fritz Zwicky, the creator of the Morphological Method, says he developed this method because people tend to be too hasty and caught up in preconceived notions when solving a problem. To avoid this, we must not give up on a problem until it is proved unsolvable.
The Checklist and the Matrix methods are well-known in their general structures, and both are logic-oriented, whereas Attributes Identification, Desirable Point Identification, and the Gordon Method are more semantic-oriented in that ideas are forced to be expressed as verbs in terms of grammar, subjectivity, and state-variables.
Another group of the Divergent Methods is the Analogous Idea Association Methods, which include Synectics (Alexander, 1965), the NM Method (Nakayama, 1965), and Bionics. Synectics, developed by William J. J. Gordon, aims to generate new ideas by combining two different attributes or by differentiating similar attributes. The NM Method, developed by Masakazu Nakayama, is characterized by a six-step process, namely: (1) setting the issue, (2) setting keywords, (3) exploring analogies, (4) exploring backgrounds of the analogies, (5) coming up with ideas, and (6) discovering solutions.
Bionics, on the other hand, starts with the study of biological systems, constructs models to mimic them, and then ends with the design of relevant equipment and machines. This approach is generally considered to be the reverse of Cybernetics, developed by Norbert Wiener.
4.2. Convergent idea generation methods
The convergent thinking methods may be divided into spatial approaches and temporal approaches. The former includes the KJ (Kawakita, 1975), Kozane (Umesao, 1989), Cross (Takahashi, 1989b) and ISOP (Ishikawa and Hirota, 1994) methods, whereas the latter includes PERT (Mori, 1964), Business Design (Nakamura, 1979), Cause-and-Effect Diagram (Ishihara, 1965), and Relevance Tree Methods (Ayres, 1970).
The KJ Method is quite
The temporal methods place a bigger emphasis on logics and relationships. PERT, developed by the US Navy, is one of the byproducts of the US-Soviet Space Race in the 1950s. It was prevalent in the areas of Operations Research and Management Sciences. On the other hand, Business Design, Cause-and-Effect Diagram, and Relevance Tree methods are less structured and more relation-oriented, avoiding rigid formality in a wide range of areas.
4.3. Combination methods
The ZK (Katagata and Tajima, 1978), Input-Output (Whiting, 1963) and Work Design (Nadler, 1969) methods use combinations of the divergent and convergent thinking. The characteristics of these techniques are that divergent thinking and convergent thinking are repeatedly used, and they encompass a variety of domains.
For example, the ZK Method, developed by Zenji Katagata, encompasses three levels of thinking: the cognitive world, the imaginary world, and the reality. In creativity development, the biggest emphasis is placed on the cognitive world in idea generation phase, while the imaginary world and the reality are more important in the solution phase. This approach therefore requires both divergent and convergent thinking to ensure a smooth process. It is well-known that the first Input-Output Method was developed by GE for the purpose of exploring new design ideas for an automatic system. At that time, this approach consisted of a formulaic repetition of divergent and convergent thinking with certain restrictions, with emphasis on the step-by-step process and evaluation.
Now, it has become diversified, and more open and less formulaic. The Work Design Method, on the other hand, was limited to the area of systems design. However, as the ideal or normative approach and the realistic or specific approach became more valued within this method, it started to use combinations of divergent and convergent thinking more than it did before.
4.4. Attitudinal approaches
Interactive Methods and
Dramatic Methods fall into this genre. The former is further classified into
Transactional Analysis (Berne, 1958), Encounter Group (
The main distinction between the Interactive Methods and the Dramatic Methods lies in the setting in which a player conceives ideas. In the former, the setting is of a clinical nature and does not always involve scenarios and stories, whereas in the latter, scenarios and stories are prerequisites.
Transactional Analysis was developed on the basis of a traditional psychoanalytic theory. While Freud categorized ego into super-ego, ego and id, Berne, who developed this Transactional Analysis method, conceived the structure of ego as P (Parents), A (Adults), and C (Children). Upon identifying psychoanalytic attributes of each character, interaction between these three characters is structurally analyzed, so as to help foster the personal growth and self-realization which form the basis for idea generation.
Encounter Group, on the other hand, is not a training method but a fully open, informal meeting where two facilitators play an important role in encouraging coordination and self-expression among participants without taking leadership.
Counseling Methods, from diagnostic approaches to psychoanalytic ones, have been developed since the l930s. Both directional counseling and non-directional counseling have been conducted in order to solve problems of individuals as well as groups.
In Psychodrama Analysis, participants are required to act spontaneously so that they can realize their undiscovered self, which in turn is related to generating new ideas. The main feature of Role Playing is that it aims to bring participants from the realm of reality into that of imagination, so that interaction between them may generate new thoughts and ideas.
Finally, Creative Dramatics, as the name implies, is a technique where children are led to realize the importance of the creative process and the joy of creation, by making them create their own stories without borrowing from stories they know. This can also be applied to managers and employees.
Table 4 shows a summary of major idea generation methods and approaches, from the viewpoints of techniques, objects, entities, application stages, and scenes of application.
Table 4: A Summary of Idea-creation (Generation) Approaches (Representative)
Source: Adapted from Takahashi (1993, pp. 258–259).
5. Future of Organizational Intelligence
Organizations should continually introduce and improve methods and techniques for organizational intelligence, so that they can create organizational intelligence and strategic intelligence. What is important here is how to create new ideas quickly by using these methods.
It is also vital to keep searching for the correct direction in which improvements should be made. For that, it may be necessary to stock inventories of creativity techniques on a larger scale. Furthermore, we would need to explore combinations of these techniques for each issue, to discover the best combination for generating organizational intelligence. To this end, we must conduct various experiments constantly and systematically.
There are many paths that organizational intelligence can take. One direction is, as mentioned earlier, to discover the know-how of producing organizational intelligence speedily by one or a few techniques, and the best combination of techniques for a given realm of organizational intelligence.
Another direction is to explore questions such as what kind of external system environment may be best suited to generating organizational intelligence, and what sort of internal network environment would be the most desirable. Of particular importance is the question of the key environmental factors — those that trigger the generation of organizational intelligence, and those that ignite and inspire each individual so that organizational intelligence may be continually produced.
Yet another direction is to develop a system that can maintain the balance between competition and cooperation among individual members, or among sectors, for the most desirable synergy effects that would produce organizational intelligence. This requires psychological and motivational studies on the interaction of individuals, groups and organizations.
As shown in many successful cases, the importance of the illogical, the irrational, and the accidental cannot be ignored either, so as to best produce quality-oriented, sustainable organizational intelligence.
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This paper was excerpted from Dr. Ishikawa’s upcoming new book, “An Introduction to Knowledge Information Strategy,” published by World Scientific Publishing Company. Copyright 2012 Akira Ishikawa and WSPC. The paper featured above comprises Chapter 10; additional selected chapters will be featured in upcoming issues of this Journal.
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