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Intelligence Systems: A Sociotechnical Systems Perspective James A. Sena, Ph.D.,

College of Business California Polytechnic State University

San Luis Obispo, CA 93407 (805) 756 6280

jsena 63 calpolv.edu

ABSTRACT To effectively compete a firm needs to take advantage of their intellectual capital. However, intellectual capital alone is not sufficient to capitalize on the intellectual assets of the firm. An intelligence system is also necessary. We propose an intelligence system, consisting of four evolving components: a corporate data base of transaction processing and management information systems, a decision-making environment of decision support and expert systems, a corporate-wide ability to examine the information resources, and a knowledge center to support individual and group decision making. We meld this view of an intelligence system into a sociotechnical system framework as means to explain and juxtaposition knowledge work.

Keywords Knowledge management, Intelligent Systems, Sociotechnical Systems, Data Warehouses, Transaction processing, communities of practice.

1. INTRODUCTION Businesses have become quite sophisticated in transforming data into information, but not nearly as good at turning information into knowledge. We regard knowledge to be the appropriate application of information. Managers are constantly faced with the challenge of managing the “meaning” of an overwhelming array of data on a daily, even moment-to-moment basis [9]. Businesses have neglected to create the kind of intelligence systems that explicitly turn information into knowledge [2].

1.1 Intelligence System An intelligence system covers the entire panorama of business transaction processing through decision making. Every decision maker must gather intelligence about the external and internal environment in order to make enlightened decisions. Intelligence gathering requires a series of mechanisms to capture data, information, knowledge and even corporate wisdom. Beyond the gathering of intelligence is the application and deployment of the data and information to enhance the competitiveness of the firm.

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SlGCPR ‘99 New Orleans LA USA Copyright ACM 1999 I-581 13-063-5/99/04...$5.00

A.B. (Rami) Shani, Ph.D., College of Business

California Polytechnic State University San Luis Obispo, CA 93407

(805) 756 1756 ashani @calpolv.edu

A first step to create an intelligence system, as we envision, is to recognize and appreciate the intellectual capital assets of the firm. These assets -- the knowledge of employees, customer and supplier relations, brand loyalty, market position, and knowledge -- need to be nurtured and leveraged [ 141. According to Peter Drucker [2], the chief source of competitive advantage is the knowledge of the organization’s members. The firm needs to recognize the mutual dependence between the company and its knowledge workers. And, in turn, the firm must serve and nurture it’s knowledge workers. The knowledge workers need the value creating processes and infrastructure of the organization, as well as conversations with other members of the firm to unleash and leverage their knowledge, leading to an intelligence system. This paper proposes an intelligence system framework couched within a sociotechnical system perspective.

1.2 Organizational Learning

According to Malan and Kriger [9] wisdom can be “learned”. By being aware of the forms of variation, managers can develop the necessary skills to detect and interpret differences in their areas of interest. With increased awareness managers can focus their attention and address a wide array of information to improve their decision making. They need to notice the granularity and variability of their surroundings by exposing themselves to opportunities to sense a wide variety of organizational data.

The effective utilization of a firm’s intellectual capital requires more than the storage and manipulation of data and information. Human assets need to be recognized and leveraged as organizational assets to be accessed and used, not just within the minds of individuals, but by a broad set of individuals on whose decisions the firm depends. According to Nonaka & Takeuchi [lo] knowledge management requires a commitment to “create new, task-related knowledge, disseminate it throughout the organization and embody it in products, services and systems.” Competitive advantage and product success are a result of collaborative, ongoing learning [ 131 [3]. Organizational and technical challenges require the integration of an effective human network [8]. Accordingly, new skills, mind-sets and models, organizational commitment, and ways of thinking are required to facilitate corporate learning.

1.3 Generating and Using Knowledge

At the organizational level knowledge is generated from internal operations and outside sources communicating with the corporate structure. Once created, knowledge is accessed when needed from sources inside and outside the firm. A key

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difference between information and knowledge is the use of wisdom - a knowing what to do. Knowledge is transferred in a formal manner through training or in a less formal way through work-related experiences. Information is represented and conveyed in printed or displayed forms, reports, graphs and charts - knowledge is using this information in an appropriate way. After validation, knowledge is internalized within the organization’s framework in its processes, systems, business rules and practices. With the need to maintain a sustainable competitive advantage critical knowledge cannot reside passively in the minds of employees. It has to be accessed, synthesized, augmented and deployed. The organization must learn to employ knowledge rapidly and uniformly.

2. THE APPLICATION OF KNOWLEDGE Knowledge work, the appropriate application of information, involves the creation and/or transformation of knowledge from information and its application to new or improved technologies, products, and services. The entire process from front-end, market identification, product and service design, to the delivery of goods and services has to be managed. An intelligence system plays a key role in this management process. This process needs to be distributed and embodied through all parts of the value chain.

2.1 The Interplay between Tacit and Explicit Knowledge

Nonaka & Takeuchi, [lo], in their field research, noted that American and Japanese executives tended to hold fundamentally different attitudes about information and knowledge. Americans tended to put their faith in “explicit knowledge,” or knowledge that is formal, unambiguous, systematic, falsifiable and scientific. The Japanese were inclined to value “tacit knowledge,” or knowledge that is intuitive, bodily, interpretive, ambiguous, nonlinear and difficult to reduce to a scientific equation. The generation of tacit knowledge is a critical part of organizational knowledge in many firms. With its roots in the experience of individuals, tacit knowledge is difficult to process and hard to transfer. Extracting knowledge is another challenge for the intelligence system. Through the use of computer based training [CBT], simulations, the use of expert systems, and other model-based software tools tacit knowledge can be extracted, transferred, and placed into an explicit context that is usable by the intelligence system.

Explicit knowledge at the level of the individual is necessary but not sufficient. Generating organizational knowledge requires converting the tacit knowledge of the individual into explicit knowledge that is accessible to other organizational members. This often is a social process mechanism for generation whereby organizational members engage in a dialogues, thereby gaining new perspectives. In these dialogues, often termed community of practice, conflicts and interpretations can be resolved. The premises of existing knowledge are questioned and new knowledge can be generated.

3. THE INTELLIGENCE SYSTEM The intelligence system and application of knowledge throughout an organization requires a set of information enabling mechanisms. In order to derive our view of an

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intelligence system we observed what was happening in successful firms. We noted four overlapping, progressing kinds of systems. These systems constitute, what we term, an intelligence system, consisting of four sub systems: the information processing engine, the decision making subsystem, the data warehouse sub system, and the knowledge center. Each of the sub systems builds on the others to facilitate the generation and application of knowledge. Figure 1 provides an overview of the ingredients required for our intelligence system.

Information Pmcessing Engine Data Warehouse Subsystem

Figurel. The Intelligence System

3.1 The Information Processing Engine

The core of any business is its online transaction processing systems [OLTP]. Every organization has such a system. All of the basic operations of the firm, as well as the firm’s supply chain, depend on the accurate and timely processing and maintenance of transactions. OLTP systems are “increasingly responsible for the supply of accurate data for long-term storage in the firm’s data warehouse. Not all firms have data warehouses but many are moving in this direction. The emergence of business-to-business and business-to-consumer electronic commerce is drawing attention to well-designed OLTP systems capable of gracefully managing large volumes of transactions” [15]. Thus, the importance of a strong, well-managed transaction base is the foundation for the firm’s intelligence system.

From the knowledge worker’s perspective, transaction processing is dynamic. New records are continually being added, and existing records are updated or deleted. This constitutes an operational environment of non-stop change. Historical data is maintained to meet the requirements of operational reporting and management. The various transaction systems provide summary and exception data to management in formalized query and reporting systems, traditionally called Management Information Systems [MIS]. These MIS systems have been designed to support the operational and tactical decision making of mid-level managers and their staff. An MIS, typically, is targeted to a particular functional area or business unit (e.g. the Controller or the Sales Manager). There often exists a number of overlapping MIS systems -- the reason being the need for managers to have access to information that affects their sphere of operation as well as the general coordination of the firm’s activities. In many organizations there exists a corporate information systems [CIS] that provides operational and tactical data to senior management. The CIS is a formalized query and reporting system, similar to an Executive Information

System -- the difference being the CIS provides data based on the form’s OLTP activity -- a pulse on the firm’s internal operations. Figure 1.1 provides a diagram of the Information Processing Engine. The data, contained in the OLTP systems, the MIS’s and the CIS, comprises the Corporate database.

provides information to assist the decision maker in making a more enlightened decision. Using specialized software a set of rules are extracted from experts by working with a knowledge engineer. Rules are also obtained by examining the firm’s business rule structure for relevant data elements.

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THE CORPORATE DATABASE I lNFOWlATlON PROCESSING ENGINE

Figure 1.1 Information Processing Engine

3.2 The Decision Making Subsystem

With the advent of local area networks [LANs], client-server processing, forms/events-driven software (e.g. Visual Basic), and accessibility to heterogeneous data bases, decision support and expert systems interest is being revitalized. LANs, in concert with Client-Server system and the Intranets, have enabled knowledge workers to access data from a variety of sources and forms external from the corporate data base. Using a meta data base approach, data from the corporate data base, departmental information systems, and individual systems can be made accessible to the knowledge worker in a homogeneous format. External information (e.g. supplier, customer, financial climate, market research, competitive position, etc.) can also be integrated into the meta database management system. There are a variety of software-hardware products that provide for such meta data management. Enterprise resource planning systems, such as SAP, have facilities to gather data from heterogeneous sources. Other products, such as. Microsoft’s Visual Studio 6, provide new technologies to make programming and databases “data savy” [6].

Decision support systems are designed to satisfy the information needs of managers at any level in a distributed processing environment [22]. Typical systems are designed to support the problem finding (future problems related to the present) and problem solving decisions that can not be derived directly form the OLTP, MIS or CIS corporate data reporting and query component. Using cases, based on past scenarios, new alternatives can be generated for evaluation by the decision maker. In effect, institutional memory -- collective experiences can be used to gather supporting information for decision making. Figure 1.2 provides an overview of the decision making subsystem Expert Systems are used by firm’s in narrow problem areas or focused decision making (e.g. credit analysis). A typical expert system recommends a solution to a problem, whereas a DSS just

Figure 1.2 Decision Making Subsystem

Both DSS and expert systems depend on the firm’s business rules. Generally business rules are thought to be an implementation of business policy in computer systems or modeling constructs used to represent business logic. A “business rule is an atomic, an explicit and well-formed expression that describes or constrains the business principles, guidelines and operations of a company using vocabulary and syntax that can easily be used and understood by the people within the company who are responsible for defining and carrying out the business.” [23]. The rules serve as bases, justifications, and guidance systems leading to intelligent insights for using information, making decisions, and direction for the completion of business tasks. A business rule explains why a decision is reached or why an action is taken, but it doesn’t describe how it is done. For many firms business rule formulation is currently left to individual employees to interpret as they wish [24]. A company takes a big risk by allowing individual interpretation and implementation. People come from varied backgrounds and training. A word may have one meaning for one person and a completely different meaning for someone else. For many firms, business rules are incorporated into decision support and expert systems in a flexible manner -- allowing for modification and updating as the organization changes.

3.3 The Data Warehouse Sub System

As decision support mechanisms became internalized and spread throughout a firm, the need exists for a common repository of data and information. A data warehouse is a collection of decision support technologies aimed at enabling knowledge workers -- executives, managers, and analysts -- to make better and faster decisions. Sometimes thought of as a corporate data decision bank, a data warehouse is a “subject-oriented, integrated, time-varying, non-volatile collection of data that is used primarily in organizational decision making ” [7]. Typically, the data warehouse is maintained apart from the

organization’s operational databases. Figure 1.3 provides a schematic of the components of the Data Warehouse subsystem.

Figure 1.3. Data Warehouse Subsystem

Data warehouses are databases designed to support decision making. They are specialized systems that extract operational data from online transaction processing systems (OLTP) and corporate information systems (detailed transaction and operational data used for managerial reporting and query) and preprocess the extracted data through the creation of indexes, partitions, aggregations, and summarizations to support high performance, complex queries. One of the critical aspects of data warehouses is the time-based, snap-shots of corporate data. The knowledge worker can examine selected data elements over multiple time periods to view changes in various aspects of the firm.

Data warehouse queries are commonly used to analyze past data by various factors and plan future strategies. Data is updated on a periodic basis. Data warehouses typically hold considerably more data than is stored in on-line transaction databases. As the extracted data gradually becomes dated, some form of data refresh is employed. Historical, summarized, and consolidated data is more important than detailed, individual records. Warehouses contain consolidated data from many operational databases over long periods of time, they tend to be orders of magnitude larger than operational databases.

Organizations typically have a number of important business dimensions that form a foundation for how their data is analyzed. Decision-makers require the ability to easily view their data along these dimensions. For example, a manager may wish to view sales of a product by month and by state. A general set of dimensions would reflect who, what, when, and where. Until recently, the tool that the decision-maker would utilize to access this data would typically be a query tool or a customized application program. Decision makers now can use a variety of measures to evaluate their operations and decisions from within a data warehouse. Foremost among these is Online Analytical Processing [OLAP] which provides the capability to deliver these measurements in a user-customizable format. Most OLAP systems use the multidimensional view of data for object analysis. The various measures are depicted as dimensions that, together, uniquely describe a business aspect’s information content and value. The attributes of dimensions are related via a hierarchy of relationships that facilitate measurement.

Data mining, a more passive analysis technique, is the process of automating information discovery. Data mining automates the process of discovering useful trends and patterns from within the data warehouse. Creating representative models based on existing data sets has proven useful for understanding trends, patterns, and correlations, as well as forming predictions based on historical outcomes. Using a query tool in a data warehouse, a knowledge worker can ask a question like: “What are the total sales for our product in the midwest verses the south this year and last year?” By asking this question the knowledge worker “knows” that there is an association between the two geographic areas. Data mining takes a different approach to the question. Instead of assuming the regional linkage, a data mining study might try to find the most significant factors involved in the sales volumes. The knowledge worker is asking the data mining tool to discover the most influential factors that affect the sales volumes - it tries to discover relationships and hidden patterns that may not be obvious (a passive knowledge generation approach).

There are a number of different approaches to data mining. Among these approaches are classification, clustering and visualization studies. The classification or supervised learning study is very common in businesses today. For example, a manager wants to determine why certain customers remain loyal while others leave. Obviously, the manager wants a way to predict which customers will be lost to competitors. Data mining’s approach to this situation is to not assume any correlations. Instead a “subject” of the study is specified (e.g. Customer Type). Then the data base is analyzed to determine all data elements that differentiate various customers (e.g. number of years doing business with the firm, number of times customer does business, and their satisfaction with the firm’s service). Clustering or unsupervised learning is a method of grouping instances (e.g. customer transactions) that share trends and patterns. There is no dependent variable. Instead implicit knowledge is assumed (e.g. which customers have remained loyal or lost.) The firm wants to understand what similarities exist in their customer base so they can create and understand different groups to which they sell and market.

3.4 The Knowledge Center A knowledge worker in today’s changing business environment cannot rely on informal methods for obtaining and sharing knowledge [S]. Just walking down the hall to get a cup of coffee and engaging other knowledge workers will not suffice. Knowledge centers, sometimes called “virtual coffee rooms”, are emerging as a place where communities of practitioners who share expertise in areas such as change management, advanced technologies, and project management can meet. In the knowledge center techniques for using data warehousing, data mining and other decision support tools are shared. By categorizing areas of knowledge, clients and employees have a way to find the appropriate contact where they need information. Knowledge center associates -- subject matter experts -- are responsible for adding to the companies knowledge store through research papers, documented case studies, shared client experiences, and more [13]. The knowledge store is a complex of groupware products, document managers, and intranet tools,

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similar to that described in Figure 1.4. The objective is to build a collection of reusable assets, tools and techniques.

Figurel.4. The Knowledge Center

The knowledge center enables knowledge workers to obtain information that doesn’t already reside in or is not readily accessible from within the corporate data framework. Through means of a virtual “help desk” -- a targeted email to knowledge center associates and information technology [IlJ personnel answers and suggestions for problem solving are forthcoming. The knowledge center works because the associates are committed, as part of their scope of work, to treat the knowledge inquiries as a high priority. Without this process, a person looking for information would only be able to access the knowledge center resources and to contact individuals that they know. Instead the knowledge resources of all of the associates is available to the inquirer. Employees can work more efficiently by reusing ideas instead of starting from scratch.

4. THE CHANGING ROLE OF INFORMATION TECHNOLOGY Given our suggested intelligence system framework it may be worthwhile to reconsider the role of information technology. Today’s widespread dependence on information technology has precipitated the need for more effective knowledge management. This is an obvious conclusion. To be effective, knowledge workers need to be able to understand and act on information. Through proper knowledge management the organization’s resources can be leveraged to achieve their business goals. To insure that information technology becomes part of the firm’s business plan it is essential to alter the way in which information technology is viewed within the organization. This requires a change within the firm -- key management personnel need to accept the IT managers as partners. IT needs to be seen as a mechanism for growth [4]. When IT is placed in the role of potential profit-maker, managers begin to view it in a more positive way. One way to do this is to identify technology as a business proposition wherein the business units reap the profits as they bear the cost of development.

The choice of information to represent in the intelligence system, given the vast array of stored information can be complex and overwhelming. In some firms, broad overviews of operations are described on “balanced scorecards” where a few critical performance factors are updated daily. These factors

support managers and their staff in making decisions -- leading to the formation of an intelligence system. They include customer, internal business, financial, strategic, and innovation and learning measures. They all can be incorporated into the intelligence system. Summarized information generated throughout the business can be viewed in terms of these critical performance variables.

A firm’s intelligence system reflects the enterprise’s knowledge base organized around the firm’s fundamental resources. These resources are called subject areas, about which the enterprise must know information. The system must be as understandable to the business executive as the organization chart (which models the organization’s human resources), and the chart of accounts (which models financial resources in the form of sources of revenue and expenses). Successful companies rapidly create, disseminate and embed knowledge in new technologies and organizations. This knowledge forms the foundation for an infrastructure for new knowledge, assumptions, and controls. By establishing controls new business rules are defined that enable the consolidation and formalization of information gathering and dissemination to reveal knowledge. The deployment of groupware products and the introduction of the intranet provide channels to quickly provide information to all parts of the firm. Intelligent agents can be embedded on web pages permitting the knowledge worker to examine information on a demand basis. The agents enable the rapid embedding of complex models into software.

Information management is no longer just the responsibility of the information services. Information is a business resource used by business personnel (knowledge workers -- data consumers), created by business personnel (data producers), and defined and guided by business personnel (data definers). Knowledge workers use information as raw material in their work. Therefore, the reliance on data producers to create accurate and quality-based information is paramount. These same producers may be called on to capture facts that may not be needed in their jobs or business units but could be required by knowledge workers in downstream activities.

4.1 The Role of Organizational Learning Processes

As organizations are faced with tougher competition, the pressures to better utilize “human capital” has increased the interest in the phenomena of “organizational learning” -- knowing how and when to use information appropriately. Organizational learning is a system of principles, activities, processes and structures that enable an organization to realize the potential inherent in its human capital’s knowledge (information application) and experience (corporate/individual memories of what worked and in what situation). According to Senge [ 183, organizational learning incorporates all activities and processes taking place on the individual, team and organizational levels. Schein [16] notes that there are at least three distinctly different types of learning: Knowledge acquisition and insights (cognitive learning), habits and skill learning, and emotional conditioning and learning anxiety. Two different kinds of organizational learning processes, learning

how (organizational members engaging in processes to transfer the system-wide implications of new information technologies. and improve existing skills or routines and learning) and Ensuring compatibility between the technical and environmental learning why (organizational members diagnosing causality), subsystems requires that new information technologies are can also be identified. All of these different modes of learning effective in meeting the needs of customers and are capable of could be incorporated into the knowledge center. Organizations, enhancing the competitive position of the firm leading to a by their very nature as social systems, are the environments in redefinition of the relationship between the technical and which learning takes place [l]. As such, organization design environmental subsystems. Compatibility between technical and plays a critical role in creating an environment that fosters social subsystems implies that a delicate balance must be transforming information into knowledge and the development established between selecting the new information technologies, of human capital to perform the transformation. Stating the compatible with the existing social subsystem and changing key obvious, the format acceptance and recognition of the managerial processes -- e.g., managerial accounting systems and intelligence system as part of the design to facilitate this human resources selection and training, to accommodate the transformation is necessary. requirements of the new information technology.

5. THE SOCIOTECHNICAL SYSTEM PERSPECTIVES Sociotechnical system [STS] theory provides a broad conceptual foundation and insights into the way that organizations transform information into knowledge. By describing the components of STS we intend to fuse this perspective with our concept of intelligence systems. Many organizations that utilize formalized information transformation mechanisms are viewed as non-routine organizations. These organizations are composed of a social sub-system (the nature of the human assets - the people with knowledge, competencies, skills, attitudes), a technical sub-system (the inputs and the technology which converts inputs into outputs - or product-in-becoming) and an environment sub-system (including customers, competitors and a host of other outside forces). Sociotechnical system design pulls the three sub-systems to utilize the firm’s resources through knowledge management configurations and processes -- leading to the development of an intelligence system. The sociotechnical supporting structure for the intelligence system can be viewed as an engine that leads to information transformation and knowledge creation, utilization of intellectual capital and bottom line business performance. These concepts are presented in Figure 2.

The business environment (the environmental subsystem) is composed of elements in the marketplace in which the organization competes. As competition intensifies and customers become more sophisticated, the external environment becomes less stable and more complex. Different information technologies offer distinct benefits with regard to flexibility, productivity, quality improvement, efficiency, and integration of change. The primary requirements are that the information technology chosen is consistent with and supports the strategic goals of the firm and its human capacity to fully utilize the information technology [20].

The firm’s social subsystem refers to human resources and human capital assets, which work in the organization, and the totality of their individual and social attributes. The social subsystem encompasses individuals’ aptitudes, competencies and skills, know-how (knowledge-base), attitudes and beliefs, and relationships within groups and among groups. These relationships include lateral and vertical relationships among and across supervisory and subordinate lines of authority. They include relationships between the formal and informal systems and the components related to the culture and tradition of the organization, such as work habits and practices, assumptions, values, rites, rituals, and emergent role network

The technical subsystem of an organization encompasses the technological resources, physical and financial assets, tools, techniques, devices, artifacts, methods, configurations, procedures, intellectual capital, and knowledge used by the organizational members to acquire inputs and transfer inputs into outputs [12]. Important differences exist among different information technologies in terms of their impact on the firm’s technical subsystem. The introduction of e-mail software has a limited and local impact; it leaves both the social and technical subsystems largely intact. Fully integrated local area networks involve transformations of both the technical and social subsystems.

Figure 2. The Intelligence System: STS Framework The organization of the intelligence system assists in the confirmration and mocesses. nrovidinz the firm with tools and

Under guidelines, decisions made about or within any one of the organizational subsystems, should meet the demands of the other subsystems. The scope of STS extends beyond work design to broader dimensions of organizational strategy, structure, and key managerial processes. STS provides a particular useful framework for the examination and analysis of

L I an organizational enabling framework to achieve its strategy. This cluster includes multiple elements, such as structural design of the firm, reward system, and learning systems, as well as the intelligence system elements. Top management’s investment in new information technology requires an adjustment of organizational structure to accommodate the needs of the

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information technology being adopted. Bureaucratic structures with levels of functional specialization and numerous levels of hierarchy are suited to efficient operation of highly mechanized operations under static conditions. Conversely, flexible organizations that are organized as communities-of-practice with high level of inter-functional collaboration and decentralized decision making are more suited for the knowledge intensive firms. A critical component of the intelligence system configuration and process is the establishment of multiple methods and criteria to be used by the firm to measure its success, such as an increase in the firm’s capabilities and its intellectual capital and the ability to transform and apply information appropriately.

6. CONCLUSIONS For intelligence systems to exist, certain organizational conditions and arrangements need to be in place. The starting point is a set of external and internal conditions that call for adaptation. Competitive pressures require improved processes and organizational configurations that facilitate improved operational efficiency and the creation of knowledge that enhances the success of the firm.

The design requirements are related to the classic process criteria of work design[21,11,12]: want to do, can do, and know what to do. In order to make the intelligence system “work” management needs to be responsible for completing such design requirements as:

Determining the required levels of application-specific and managerial knowledge;

Enabling the centralized collection of that knowledge from sources internal and external;

Representing current knowledge in documents, databases, and other clear and widely accessible formats;

Embedding knowledge in business rules, procedures, policies and control mechanisms;

Refining and testing knowledge - for instance, stress testing the firm’s existing models with worst case scenarios;

Overseeing the transfer of knowledge to application-related decision making;

Overseeing the transfer of knowledge and information to senior management monitoring the current state of the firm; and

Creating an infrastructure to support all of these activities.

What we did, in proposing our view of an intelligence system, was to spell out the obvious. These are the ingredients for success in the competitive business world. As such, we see no real rationale for research to legitimize our observations. We used the sociotechnical system to describe and associate the business system with our view of an intelligence system. Some form of ongoing integration from OLTP to data warehousing to knowledge centers needs to be internalized and championed. The framework centers on the need to identify the design requirements and the design dimensions in accordance with the four evolving stages of the intelligence system, Transactions

must be captured and used as a corporate information resource within the technological subsystem. A decision-enabling framework of decision support and expert systems must be in place to support the social subsystem. A corporate wide ability to examine and apply knowledge, through data warehousing and data mining supports the environmental subsystem, Social and technical system dynamics of the firm set the stage for the firm’s ability to analyze it’s way of organizing and act on it’s findings. The analysis mechanisms, used by the firm, are rooted in the intelligence system’s configurations and processes, and are based on the design choices and understanding of the intelligence system, itself. An optimally designed firm - one that utilizes the proposed design framework - will take advantage of the knowledge center to better utilize it’s intellectual capital to achieve a higher level of business proficiency.

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