Intelligence systems: a sociotechnical systems perspective

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Intelligence systems: A sociotechnical systems perspective CONFERENCE PAPER · JANUARY 1999 DOI: 10.1145/299513.299623 · Source: DBLP

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2 AUTHORS: James A. Sena

Abraham B. (Rami) Shani

California Polytechnic State University, Sa…

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IntelligenceSystems:A SociotechnicalSystemsPerspective James A. Sena, Ph.D.,

A.B. (Rami) Shani, Ph.D.,

College of Business California Polytechnic State University San Luis Obispo, CA 93407 (805) 756 6280

College of Business California Polytechnic State University San Luis Obispo, CA 93407 (805) 756 1756

jsena 63calpolv.edu

ashani @calpolv.edu A first stepto createan intelligence system,as we envision, is to recognize and appreciate the intellectual capital assetsof the firm. These assets-- the knowledge of employees,customerand supplier relations, brand loyalty, market position, and knowledge -- needto 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 dependencebetween 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, aswell asconversationswith other membersof the firm to unleash and leverage their knowledge, leading to an intelligence system.This paper proposesan intelligence system frameworkcouchedwithin a sociotechnicalsystemperspective.

ABSTRACT To effectively competea firm needsto take advantageof their intellectual capital. However, intellectual capital alone is not sufficient to capitalize on the intellectual assetsof the firm. An intelligence systemis also necessary.We proposean intelligence system, consisting of four evolving components: a corporate databaseof transactionprocessingand managementinformation systems,a decision-makingenvironment 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 meansto explain andjuxtaposition knowledge work.

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

1.2 Organizational

According to Malan and Kriger [9] wisdom can be “learned”. By being awareof the forms of variation, managerscan develop the necessaryskills to detect and interpret differences in their areas of interest. With increasedawarenessmanagerscan focus their attention and addressa 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 sensea wide variety of organizational data.

1. INTRODUCTION Businesseshave becomequite sophisticatedin transformingdata into information, but not nearly as good at turning information into knowledge. We regard knowledge to be the appropriate application of information. Managersare constantly faced with the challenge of managing the “meaning” of an overwhelming array of data on a daily, even moment-to-momentbasis [9]. Businesseshave neglected to create the kind of intelligence systemsthat explicitly turn information into knowledge [2].

The effective utilization of a firm’s intellectual capital requires more than the storageand manipulation of data and information. Human assets need to be recognized and leveraged as organizational assetsto be accessedand 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 managementrequires 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 corporatelearning.

1.1 Intelligence System

An intelligence systemcovers the entire panoramaof business transaction processingthrough decision making. Every decision maker must gather intelligence about the external and internal environment in order to make enlightened decisions. Intelligence gathering requires a seriesof mechanismsto 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 competitivenessof the firm. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee probided that copies arc not made or distrihutcd for protit or commercial advantage and that copies bear this notice and the full citation on the first pa@. IO copy otherwise. to republish. to post on servers or to redistribute to lists, requires prior specilic pwmission an&or a fee.

1.3 Generating and Using Knowledge

At the organizational level knowledge is generatedfrom internal operations and outside sources communicating with the corporate structure. Once created,knowledge is accessedwhen 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 transferredin a formal mannerthrough 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, augmentedand deployed. The organization must learn to employ knowledge rapidly and uniformly.

2. THE APPLICATION

intelligence system we observed what was happening in successfulfirms. We noted four overlapping, progressingkinds of systems. These systems constitute, what we term, an intelligence system, consisting of four sub systems: the information processingengine, the decision making subsystem, the data warehousesub 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

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 systemplays a key role in this managementprocess. This processneeds to be distributed and embodied through all parts of the value chain.

Figurel. The Intelligence System 3.1 The Information ProcessingEngine 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 maintenanceof transactions. OLTP systems are “increasingly responsiblefor the supply of accuratedata 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 commerceis 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.

2.1 The Interplay between Tacit and Explicit Knowledge Nonaka & Takeuchi, [lo], in their field research,noted that American and Japaneseexecutivestendedto 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 experienceof individuals, tacit knowledge is difficult to process and hard to transfer. Extracting knowledge is another challenge for the intelligence system.Through the use of computerbased training [CBT], simulations, the use of expert systems,and other model-basedsoftware tools tacit knowledge can be extracted, transferred,and placed into an explicit context that is usable by the 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 systemsprovide summaryand exception data to managementin formalized query and reporting systems, traditionally called ManagementInformation Systems [MIS]. These MIS systems have been designed to support the operational and tactical decision making of mid-level managersand their staff. An MIS, typically, is targetedto 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 reasonbeing the need for managersto have accessto information that affects their sphereof 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

Explicit knowledge at the level of the individual is necessarybut not sufficient. Generating organizational knowledge requires converting the tacit knowledge of the individual into explicit knowledge that is accessibleto other organizational members. This often is a social processmechanismfor generationwhereby organizational membersengagein 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 knowledgecan be generated.

3. THE INTELLIGENCE

Data Warehouse Subsystem

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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System-- the difference being the CIS provides data basedon the form’s OLTP activity -- a pulse on the firm’s internal operations. Figure 1.1 provides a diagram of the Information ProcessingEngine. The data, contained in the OLTP systems, the MIS’s and the CIS, comprisesthe Corporatedatabase.

t

t--

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 businessrule structurefor relevant dataelements.

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THE CORPORATE DATABASE I lNFOWlATlON PROCESSING ENGINE

Figure 1.1 Information ProcessingEngine 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 heterogeneousdata 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 accessdata from a variety of sourcesand forms external from the corporatedatabase.Using a meta data base approach, data from the corporate data base, departmentalinformation systems,and individual systemscan be madeaccessibleto the knowledge worker in a homogeneous format. External information (e.g. supplier, customer,financial climate, marketresearch,competitive position, etc.) can also be integratedinto the metadatabasemanagementsystem.There are a variety of software-hardwareproducts 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 systemsare designedto satisfy the information needs of managers at any level in a distributed processing environment [22]. Typical systemsare designedto 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 Systemsare used by firm’s in narrow problem areasor focused decision making (e.g. credit analysis). A typical expert systemrecommendsa solution to a problem, whereasa DSSjust

Figure 1.2 Decision Making Subsystem

Both DSS and expert systemsdepend 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 describesor 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 employeesto interpret as they wish [24]. A company takes a big risk by allowing individual interpretation and implementation. People come from varied backgroundsand 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 supportmechanismsbecameinternalized 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 makebetter and faster decisions. Sometimesthought 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 schematicof the componentsof the Data Warehousesubsystem.

Data mining, a more passiveanalysis technique, is the processof automating information discovery. Data mining automatesthe processof discovering useful trendsand patternsfrom 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 salesfor our product in the midwest versesthe south this year and last year?” By asking this question the knowledge worker “knows” that there is an associationbetweenthe two geographic areas.Data mining takes a different approach to the question. Instead of assumingthe regional linkage, a data mining study might try to find the most significant factors involved in the salesvolumes.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).

Figure 1.3. Data WarehouseSubsystem Data warehouses are databasesdesigned to support decision making. They are specialized systemsthat 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 preprocessthe extracted data through the creation of indexes, partitions, aggregations, and summarizations to support high performance,complex queries.One of the critical aspectsof data warehousesis the time-based,snap-shotsof corporatedata. The knowledge worker can examine selected data elements over multiple time periods to view changesin various aspectsof the firm.

There are a number of different approachesto 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 managerwants to determinewhy certain customersremain 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. CustomerType). Then the data baseis analyzed to determineall data elementsthat differentiate various customers(e.g. number of yearsdoing businesswith 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 customerbase so they can create and understand different groupsto which they sell and market.

Data warehousequeries are commonly used to analyzepast data by various factors and plan future strategies.Data is updatedon a periodic basis. Data warehousestypically hold considerably more data than is storedin on-line transactiondatabases.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. Warehousescontain consolidated data from many operational databasesover long periods of time, they tend to be orders of magnitudelarger 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 managermay wish to view salesof 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-makerwould utilize to accessthis data would typically be a query tool or a customized application program.Decision makersnow can use a variety of measuresto evaluate their operations and decisions from within a data warehouse.Foremost among these is Online Analytical Processing [OLAP] which provides the capability to deliver thesemeasurementsin a user-customizableformat. Most OLAP systems use the multidimensional view of data for object analysis. The various measuresare depicted as dimensionsthat, together, uniquely describe a business aspect’s information content and value. The attributes of dimensionsare related via a hierarchy of relationships that facilitate measurement.

3.4 The Knowledge Center A knowledge worker in today’s changing businessenvironment 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,sometimescalled “virtual coffee rooms”, are emerging as a place where communities of practitioners who shareexpertise in areassuch 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 areasof knowledge, clients and employeeshave a way to find the appropriatecontact where they need information. Knowledge center associates-- subject matter experts -- are responsible for adding to the companies knowledge store through researchpapers,documentedcasestudies, sharedclient 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 describedin Figure 1.4. The objective is to build a collection of reusableassets,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 accessiblefrom within the corporate data framework. Through meansof a virtual “help desk” -- a targetedemail to knowledge center associatesand information technology [IlJ personnel answersand suggestionsfor problem solving are forthcoming. The knowledge center works because the associates are committed,as part of their scopeof 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 resourcesand to contact individuals that they know. Insteadthe knowledge resourcesof all of the associatesis available to the inquirer. Employeescan work more efficiently by reusing ideasinsteadof starting from scratch.

4. THE CHANGING ROLE OF INFORMATION TECHNOLOGY Given our suggestedintelligence system framework it may be worthwhile to reconsider the role of information technology. Today’s widespreaddependenceon information technology has precipitatedthe needfor more effective knowledgemanagement. This is an obvious conclusion. To be effective, knowledge workers need to be able to understandand act on information. Through proper knowledge management the organization’s resourcescan be leveraged to achieve their businessgoals. To insure that information technology becomespart of the firm’s businessplan it is essentialto alter the way in which information technology is viewed within the organization. This requires a change within the firm -- key managementpersonnel need to accept the IT managersas partners. IT needs to be seen as a mechanismfor growth [4]. When IT is placed in the role of potential profit-maker, managersbegin to view it in a more positive way. One way to do this is to identify technology as a businessproposition wherein the businessunits reap the profits asthey 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 somefirms, broad overviews of operations are describedon “balanced scorecards”where a few critical performance factors are updated daily. These factors

support managersand 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 businesscan be viewed in terms of thesecritical performancevariables. A firm’s intelligence systemreflects the enterprise’sknowledge base organized around the firm’s fundamental resources.These resourcesare called subject areas, about which the enterprise must know information. The systemmust 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 sourcesof revenueand expenses).Successfulcompaniesrapidly create,disseminateand embedknowledge in new technologies and organizations.This knowledge forms the foundation for an infrastructure for new knowledge, assumptions,and controls. By establishing controls new businessrules are defined that enable the consolidation and formalization of information gathering and dissemination to reveal knowledge. The deployment of groupwareproducts and the introduction of the intranet provide channelsto quickly provide information to all parts of the firm. Intelligent agentscan be embeddedon web pagespermitting the knowledge worker to examine information on a demandbasis. The agentsenable the rapid embeddingof complex models into software. Information managementis no longer just the responsibility of the information services.Information is a businessresourceused by businesspersonnel (knowledge workers -- data consumers), createdby businesspersonnel (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 producersto createaccurateand quality-based information is paramount.These sameproducers may be called on to capturefacts that may not be neededin their jobs or business units but could be required by knowledge workersin downstreamactivities. 4.1 The Role of Organizational

Learning Processes

As organizations are faced with tougher competition, the pressuresto 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, processesand structures that enable an organization to realize the potential inherent in its human capital’s knowledge (information application) and experience (corporate/individual memoriesof 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 membersengaging in processesto transfer and improve existing skills or routines and learning) and learning why (organizational members diagnosing causality), can also be identified. All of these different modesof learning could be incorporatedinto the knowledge center.Organizations, by their very nature as social systems,are the environments in which learning takes place [l]. As such, organization design plays a critical role in creating an environment that fosters transforming information into knowledge and the development of human capital to perform the transformation. Stating the obvious, the format acceptance and recognition of the intelligence system as part of the design to facilitate this transformationis necessary.

5. THE SOCIOTECHNICAL PERSPECTIVES

the system-wideimplications of new information technologies. Ensuring compatibility betweenthe technical and environmental subsystems requires that new information technologies are effective in meeting the needsof customersand are capable of enhancing the competitive position of the firm leading to a redefinition of the relationship between the technical and environmental subsystems.Compatibility between technical and social subsystems implies that a delicate balance must be establishedbetweenselecting the new information technologies, compatible with the existing social subsystemand changing key managerialprocesses-- e.g., managerialaccounting systemsand human resources selection and training, to accommodatethe requirementsof the new information technology.

SYSTEM

The business environment (the environmental subsystem) is composed of elements in the marketplace in which the organization competes. As competition intensifies and customersbecomemore sophisticated,the external environment becomesless 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 chosenis consistent with and supports the strategic goals of the firm and its human capacity to fully utilize the information technology [20].

Sociotechnicalsystem[STS] theory provides a broad conceptual foundation and insights into the way that organizations transform information into knowledge. By describing the componentsof STS we intend to fuse this perspectivewith our concept of intelligence systems.Many organizations that utilize formalized information transformation mechanismsare 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 managementconfigurations 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 businessperformance.These conceptsare presentedin Figure 2.

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 encompassesindividuals’ 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 acrosssupervisory and subordinatelines of authority. They include relationships between the formal and informal systems and the componentsrelated to the culture and tradition of the organization, such as work habits and practices, assumptions, values,rites, rituals, and emergentrole network The technical subsystemof an organization encompassesthe technological resources, physical and financial assets, tools, techniques, devices, artifacts, methods, configurations, procedures, intellectual capital, and knowledge used by the organizational membersto 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 subsystemslargely intact. Fully integrated local area networks involve transformations of both the technical and social subsystems. The organization of the intelligence system assists in the confirmration and mocesses. Inrovidinz the firm with tools and L 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 systemelements.Top management’sinvestment in new information technology requires an adjustment of organizational structure to accommodate the needs of the

Figure 2. The Intelligence System:STS Framework Under guidelines, decisions madeabout 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 91

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 organizationsthat are organizedas communities-of-practicewith 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 measureits success,such as an increase in the firm’s capabilities and its intellectual capital and the ability to transform and apply information appropriately.

must be captured and used as a corporate information resource within the technological subsystem. A decision-enabling framework of decision support and expert systemsmust be in place to support the social subsystem.A corporate wide ability to examineand apply knowledge, through data warehousing and data mining supports the environmental subsystem,Social and technical systemdynamics of the firm set the stagefor 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’sconfigurations and processes,and arebased on the design choices and understanding of the intelligence system,itself. An optimally designedfirm - one that utilizes the proposed design framework - will take advantage of the knowledge center to better utilize it’s intellectual capital to achievea higher level of businessproficiency.

6. CONCLUSIONS For intelligence systems to exist, certain organizational conditions and arrangementsneed to be in place. The starting point is a set of external and internal conditions that call for adaptation. Competitive pressuresrequire improved processes and organizational configurations that facilitate improved operational efficiency and the creation of knowledge that enhancesthe successof 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” managementneedsto be responsiblefor completing such design requirementsas:

7. REFERENCES [l] Argyris, C., & Schon, D.A., Organizational Learning: A Theory of Action Perspective,Reading, MA: Addison-Wesley, (1978). [2] Dash, J. Turning Technology into Techknowlgey. Software Magazine, (February, 1998),64-73. [3] Drucker, P., The Age of Social Transformation. The Atlantic Monthly, 274(5), .(1994), 54-80. [4] Griffith, V. Making Information Technology Strategic. Strategy and Business, Booz-Allen & Hamilton, (41hQtr, 1997). [S] Hanley, S. The Growth of Knowledge ManagementCenters. SoftwareMagazine. (January,1998). [6] Hodfield, J., Visual Studio 6: What is in it for you?, Technical Guide to Visual Studio 6, (March, 1999). [7] Inmon, W.H., Building the Data Warehouse.Wiley, (1996) [8] Lloyd, B., Knowledge Management:The Key to Long-term Organizational Success.Long Range Planning, Vol. 29, No. 4. (1996) 576-580. [9] Malan, L. and Kriger, M., Making Sense of Managerial Wisdom. Journal of Management Inquiry, Vol. 7 No. 3, (September,1998). [lo] Nonaka, I. And Takeuchi, H., The Knowledge-Creating Company: How JapaneseCompanies Create the Dynamics of Innovation. Oxford Press,(1995). [ll] Pasmore,W. A., Designing Work Systemsfor Knowledge Workers. Journal for Quality and Participation, (July-August, 1993) [12] Pasmore,W.A. Creating Strategic Change, John Wiley, New York (1994). 1131 Purser, R.E., Pasmore, W.A., & Tenkasi, R.V., The Influence of Deliberations on Learning in New Product Development Teams. Journal of Engineering and Technology Management,9, (1992) l-28. [14] Roos, G, and Roos, J., Measuring your Company’s Intellectual Performance.Long RangePlanning, Vol. 30, No. 3, (1997) 413-426. [15] Ruden, K., Transaction Processing Today. DBMS, (January,1998)

Determining the required levels of application-specific and managerialknowledge; Enabling the centralized collection of that knowledge from sourcesinternal and external; Representingcurrent knowledge in documents,databases, and other clear and widely accessibleformats; 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; Overseeingthe transfer of knowledge to application-related decision making; Overseeing the transfer of knowledge and information to senior managementmonitoring the current stateof the firm; and Creating an infrastructure to support all of theseactivities. What we did, in proposing our view of an intelligence system, was to spell out the obvious. These are the ingredients for successin the competitive business world. As such, we see no real rationale for researchto legitimize our observations.We used the sociotechnical system to describe and associatethe businesssystemwith our view of an intelligence system.Some form of ongoing integration from OLTP to data warehousingto knowledge centers needs to be internalized and championed. The framework centers on the need to identify the design requirementsand the design dimensions in accordancewith the four evolving stages of the intelligence system, Transactions

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[21] Stebbins, M. and Sham, A.B., Organizational Design and the Knowledge Worker. Leadership and Organizational DevelopmentJournal, Vol. 16, No. 1, (1995), 23-30. [22] Thierauf, R.J, Online Analytical Processing Systems for Business,Quorum Books, (1997) [23] Thorpe, Margaret, “Understanding Business Rules.” BusinessRules ALERT!, (June/July, 1997). [24] Von Halle, Barbara and Conkey, Jane, “Explaining the Business Connection,” Database Programming and Design, (December,1996),.9-12

[16] Schein, E.H., How can organizations learn faster: The problem of entering the green room, Sloan Management Review, 34,2, (1993), 85-92. [17] Schon, D.A., Educating the Reflective Practitioner, San Francisco,CA: JosseyBass,(1987) [ 181Senge,P.M., The Fifth Discipline: The Art and Practice of the Learning Organization, New York, NY: Doubleday, (1991) [20] Sham, A.B. (Rami) and Sena,J., Information Technology and the Integration of Change: Sociotechnical System Approach. The Journal of Applied Behavioral Science, 30, (1994), 247-270.

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