KNOWLEDGE VALUATION. The starting block. Introduction

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1 The current issue and full text archive of this journal is available at wwwemeraldinsightcom/ htm KNOWLEDGE VALUATION The starting : enterprise (business) intelligence evolving towards knowledge valuation Annie Green George Washington University, Washington, DC, USA The starting 267 Abstract Purpose This paper proposes a logical starting point to valuing knowledge within the context of the business enterprise Design/methodology/approach The methodology or approach to knowledge valuation is derived from empirical research based on a framework of intangible valuation areas (FIVA) The key valuation components of FIVA are used as the basis for the evolution of an enterprise knowledge valuation system (KVS) Findings The findings of the paper are that, fundamental to implementing a KVS is the leveraging of the current enterprise environment, to uncover the intelligence that currently exists so as to make decisions and act on those decisions This paper presents the first layer of a conceptual model that supports the development of a KVS that leverages the current enterprise environment and integrates existing systems development methods and techniques in its approach to KVS creation and implementation Originality/value The conceptual model KVS Building Blocks integrates fundamental types of models with each other to provide a holistic approach to implementing knowledge valuation in an enterprise environment The first in the KVS Building Blocks establishes a journey to identifying the data and information enterprise stakeholders need represented in a language they understand, to build a foundation that supports the capability of enterprise stakeholders to value knowledge within their enterprise Keywords Knowledge management systems, Information management, Business enterprise, Intangible assets, Asset valuation Paper type Conceptual paper Introduction If there is any property that a theory of cognition must explain it is how intelligence is actually possible To be intelligent is to be able to do things, to wit, to exploit knowledge to attain whatever goals the organism has at the moment (Newell, 1990, p 158) To survive today s ongoing changes, we must be prepared to reconsider the very models on which our obsolete organizations are based (Toffler, 1980) Companies are faced with the dilemma of how to capture and institutionalize knowledge such that they maximize human potential, resulting in stronger competitive advantages and customer connections (Koulopoulos, 1997) Becoming a Smart Company means ferreting out the many ways in which simple changes can amount to quantum improvements (Koulopoulos, 1997) Fundamental is that knowledge management (KM), like other complex organizational activities, cannot deliver business results without a plan (Tiwana, 1999) VINE: The journal of information and knowledge management systems Vol 36 No 3, 2006 pp q Emerald Group Publishing Limited DOI /

2 VINE 36,3 268 Establishing a plan to implement a knowledge valuation system (KVS) is not unremarkable to the current approach to system construction and implementation Subsequently, a KVS must explicitly identify the piece of the integrated environment it satisfies, its role in the creation and distribution of knowledge, and its relationship to other enterprise elements An implementation plan for a KVS needs to consider cyclic or phased in deployment and implementation and address near term and far term solutions Implementation of a KVS is not an overnight epiphany, it must evolve over time Concepts are the basic building s on which a system operates A KVS concept (Figure 1) is constructed on the idea or understanding of valuing business knowledge Successful implementation of a KVS should provide an understanding of the various components that compose its makeup The key lies in understanding the role of a business existing enterprise infrastructure and accurately identifying and representing what will work and what will not work in the context of the business, as part of a KVS KVSs are considered a key element to drive successful business enterprises KVS has a strong potential to become foundational in regards to solving a business enterprise s problems, enhancing innovation, and providing a basis for integrating key business elements Knowledge valuation uses intelligence from the business environment and formulates a model of potential improvements that include levels of abstraction with the business environment and identifies the sources of value that align with business performance (Figure 2) The building s to a KVS, based on the conceptual model in Figure 1 are: (1) Block 1: enterprise (business) intelligence engineering (long-term memory) This is summarized information that is static to be used to determine patterns among data and information for use in decision making: Intelligence identification identify the intelligence components Intelligence representation model the intelligence components Intelligence capture evaluate and capture intelligent components based on contribution to business objectives (2) Block 2: knowledge engineering (short-term memory) This is a temporary buffer area that stores results of decisions until these results are encoded into the enterprise body of knowledge: Knowledge identification identify the knowledge components Knowledge representation model the knowledge components to represent expected results Knowledge capture evaluate and capture knowledge components based on contribution to business objectives (3) Block 3: organizational learning (motor productions) These are functions that decode and analyze information to determine its value to the business enterprise: Knowledge validation simulation of mental models that represent tacit or implicit knowledge gained to ensure correct knowledge is being captured Knowledge transfer dissemination and exchange of knowledge

3 The starting 269 Figure 1 Knowledge valuation system (KVS) concept

4 VINE 36,3 270 Figure 2 Building s to a knowledge valuation system (KVS) a performance-based enterprise knowledge life cycle

5 (4) Block 4: change management (perceptual productions) These are functions that encode information from the environment into knowledge for the business enterprise: Knowledge utilization institutionalization and implementation of knowledge Knowledge verification evaluation of mental models that represent tacit or implicit knowledge gained to confirm results obtained (5) Block 5: organizational performance management (sustainment and endurance) These are functions that evaluate, value and leverage knowledge to improve business performance: Knowledge accountability identification of sources of tacit or implicit knowledge gained Knowledge valuation estimation of the asset values of tacit or implicit knowledge gained The starting 271 Block 1: enterprise (business) intelligence engineering The emulation of intelligence in business is an abstract phenomenon whose value is based on the utilization of information as it relates to organizational performance Business intelligence (BI) is intrinsic to the business value chain and knowledge is aligned with BI To identify knowledge and subsequently value knowledge, a business needs to establish a performance-based enterprise knowledge life cycle (Figure 2) BI is the process of gathering information in the field of business It can be described as the process of enhancing data into information and then into knowledge BI is a valuable core competence and should be managed like traditional factors of labor, capital and raw materials (Von Krogh et al, 1998) Knowledge valuation is aligned with the value chain of the enterprise The value chain supports the business in identifying all the ways its knowledge assets could or should bring value to the business (Sullivan, 2000) The value chain logic enables businesses to shift their resources to capture potential value (McNair and Vangermeersch, 1998) The value chain is a combination of activities that together creates value-added products or services for a company ((Koulopoulos, 1997; McNurlin and Sprague, 1998; Von Krogh et al, 1998; Alter, 1999; Sullivan, 2000) The value chain is re-created daily by its value drivers and their interactions such as suppliers, customers, partners in strategic alliances and other stakeholders and the physical things of a business enterprise such as structures, procedures, products, services, etc (Alter, 1999; Porter, 1985) In the context of the business enterprise, the value chain provides a systematic way to divide a firm into its discrete activities (Porter, 1985; Alter, 1999) The value chain could be used to examine how firms activities are grouped and establish boundaries that are in alignment with its sources of value (Porter, 1985) The value chain provides the alignment of business value drivers to business capacity drivers Intelligence identification Structure or function must be identified; they are the mechanisms by which intelligence is achieved Figure 3 depicts the eight value drivers of the framework of intangible

6 VINE 36,3 272 valuation areas (FIVA) (Green, 2004) FIVA identifies the value chain components within the context of the business enterprise which provide the starting point to identifying the intelligent components that contribute to value creation in the business enterprise The value chain components of FIVA are defined as below: (1) Customer The economic value that results from the associations (eg loyalty, satisfaction, longevity) an enterprise has built with consumers of its goods and services (2) Competitor The economic value that results from the position (eg reputation, market share, name recognition, image) an enterprise has built in the business market place (3) Employee The economic value that results from the collective capabilities (eg knowledge, skill, competence, know-how) of an enterprise s employees (4) Information The economic value that results from an enterprise s ability to collect and disseminate its information and knowledge in the right form and content to the right people at the right time (5) Partner The economic value that results from associations (financial, strategic, authority, power) an enterprise has established with external individuals and organizations (eg consultants, customers, suppliers, allies, competitors) in pursuit of advantageous outcomes (6) Process The economic value that results from an enterprise s ability (eg policies, procedures, methodologies, techniques) to leverage the ways in which the enterprise operates and creates value for its employees and customers (7) Product/service The economic value that results from an enterprise s ability to develop and deliver its offerings (ie products and services) that reflects an understanding of market and customer(s) requirements, expectations and desires (8) Technology The economic value that results from the hardware and software an enterprise has invested in to support its operations, management and future renewal These business value drivers use a terminology of the business enterprise that is useful for internal representation of data and external communications It is important to state, the semantics of a representation language, so it is known exactly what expressions mean and which inferences are valid (Cawsey, 1998) Proper understanding requires intelligence of both language and context The semantics of language representation provides a way of mapping KVS expressions in a formal language to the business components in the real world Figure 3 Business sources of value (value chain)

7 Intelligence representation The FIVA value drivers are further defined to identify capacity drivers (Figure 4) that align them with performance of the business The capacity drivers provide the structure for identifying and determining the relationship between business value drivers, data, information and knowledge The initial layer of the KVS exhibits the levels of abstraction of the business goals, which is represented by the decomposition of the business goals into their subsequent capacity drivers and primitive elements that directly identify the value chain components and their sub-elements (Newell, 1990) The KVS design represents the levels of abstraction of the business by including capacity drivers that support: Strategic top management s planning responsibilities The starting 273 Operational day-to-day working activities of the organization Tactical middle management s allocating and controlling the organization resources Effective capacity utilization is a primary goal of operational, tactical, and strategic decision-making process: Capacity is the value-creating ability of an organization, an ability that takes in a wide variety of resources (McNair and Vangermeersch, 1998, p 1) The basis of the capacity model is the elimination of waste, which is idle or excess capacity The essence of capacity is the need to use resources to their fullest capacity When resources are not used to their fullest capacity, this produces waste Waste erodes profits and degrades organizational performance Capacity is defined for every resource and each resource has a driver These drivers represent the measures of capacity for the resource thus establishing the resource as an asset This approach to capacity provides: a path to enterprise intelligence; to what extent enterprise intelligence is a unique attribute of the enterprise; to how enterprise intelligence is measured or evaluated; and to what the nature of mechanisms capable of intelligence are Capacity cost management reaches a consensus within an organization on what capacity is and the baseline measures used to capture this capability Having agreed on the basis capacity of the enterprise, the enterprise can establish: estimates of the cost of a unit of capacity; how to track and report the utilization of existing capacity; and how to improve company performance in this key functional area Unutilized capacity that cannot be stored is waste Minimizing wasted capacity, whether that waste is stored in unnecessary inventory, reflected in pure idleness, hidden by rework, or buried in standards, is the ultimate goal of capacity cost management (McNair and Vangermeersch, 1998) Capacity is tied to the decision-making process of the organization Capacity utilization is a primary goal of the operational, tactical, and strategic decision-making

8 VINE 36,3 274 Figure 4 A symbolic model that represents the primitive level of the business enterprise

9 process This relationship provides a basis for taking action to improve performance, which creates a strong foundation for the construction of enterprise intelligence The common language for discussing and measuring capacity utilization is incorporated in the KVS model This provides a comprehensive approach to intelligence and allows a KVS to measure the achievements of the enterprise by using the capacity of an organization as its measurement of performance The KVS model establishes a common language of intelligence in the context of the enterprise s decision-making processes based on performance indicators This common language is based on (McNair and Vangermeersch, 1998): Resource capability: the amount and type of work a resource can support resources are what an organization buys and use to support its activities and outputs Baseline capacity measures: the capability to do work the amount of work the resources can support Capacity deployment: measurements of the deployment of capacity deemed to be available for use Capacity utilization measures: tracking and reporting actual performance against plans and the profit and cost implication of this performance Time frame of analysis: provide operational, tactical and strategic views of capacity views reflect the changing time frame and context of decisions that affect available capacity and it deployment Organizational focus and capacity cost management: the unit of analysis, the focus of the organization The starting 275 The capacity of an organization is the ultimate driver to its success Profit has two views: the increase of revenue or the decrease of expenses When viewing the bottom line, anything achievable, must be measurable and the final unit of measure within the business enterprise is the dollar A business must be aware of the cost of its activities, in order to understand where the waste is Intelligence capture Within the context of the business enterprise, the data repositories seek to provide a body of knowledge that addresses the problems that face the enterprise in achieving its goals The data and information are captured via the business functions of an enterprise and encoded for retention and recall for use in making decisions with respect to the business value chain A KVS supports interfaces to external and internal data of the business enterprise and establishes the need for agents to facilitate the search and retrieval of data Figure 5 Aligning capacity drivers with business operations

10 VINE 36,3 276 and/or intelligence within its business domains Data are the foundation of intelligence It is data that are formed into information that is transformed into intelligence that is transformed into knowledge The challenge is to be able to retrieve these data within a specific domain and in a useful form for its intended use The capacity drivers are aligned with elements and values (financial (tangible) and non-financial (intangible)) that reside in existing operational databases (Figure 5) Conclusion There is nothing more difficult to take in hand, more perilous to conduct or more uncertain in its success than to take the lead in the introduction of a new order to things (Macchiavelli, ) The KVS Building Blocks is based on a cognitive approach, which places concentration on understanding how people understand and construct mental models of complex systems We live in a world of systems and to understand systems people need to understand the underlying patterns System thinking is a conceptual framework for making complete patterns clearer Using and understanding systems thinking can help people see how to change the patterns effectively (McNurlin and Sprague, 1998) A view of cognition as it relates to the business enterprise is to provide a unified model of mechanisms that facilitate the appropriate behavior within the enterprise structure A cognitive approach to modeling enterprise knowledge presents the KVS as a system within the context of the business enterprise The method used is that of system development life cycle (SDLC) Its purpose to dissect the system a group of interacting, interrelated or interdependent elements forming a complex whole (The American Heritage Dictionary, 1984) into its component pieces and study how the component pieces interact and work together This article has presented the external foundation for one of a five approach to establishing an enterprise KVS With the foundation established, the next task is to engineer the internal intelligence structure the actual capacity drivers to facilitate the alignment of intelligence with knowledge There is a lot more to do before the first is in place References Alter, S (1999), Information Systems, The Foundation of E-business, 4th ed, Prentice-Hall, Englewood Cliffs, NJ (The) American Heritage Dictionary (1984), 2nd college ed,, Houghton Mifflin, Boston, MA Cawsey, A (1998), The Essence of Artificial Intelligence, Prentice-Hall, Englewood Cliffs, NJ Green, A (2004), Prioritization of sources of intangible assets for use in enterprise balance scorecard valuation models of information technology (IT) firms, unpublished doctoral dissertation, George Washington University, Washington, DC Koulopoulos, TM (1997), Smart Companies Smart Tools, Transforming Business Processes into Business Assets, International Thomson Publishing, London McNair, CJ and Vangermeersch, R (1998), Total Capacity Management, Optimizing at the Operational, Tactical, and Strategic Levels, St Lucie Press, Boca Raton, FL

11 McNurlin, BC and Sprague, RH Jr (1998), Information Systems Management in Practice, 4th ed, Prentice-Hall, Englewood Cliffs, NJ Newell, A (1990), Unified Theories of Cognition, Harvard University Press, Cambridge, MA Porter, ME (1985), Competitive Advantage, Creating and Sustaining Superior Performance, Free Press, New York, NY Sullivan, PH (2000), Value-driven Intellectual Capital, How to Convert Intangible Corporate Assets into Market Value, John Wiley & Sons, New York, NY Tiwana, A (1999), The Knowledge Management Toolkit, Prentice-Hall PTR, New York, NY Toffler, A (1980), The Third Wave, Pan Books, London Von Krogh, G, Roos, J and Kleine, D (1998), Knowing in Firms: Understanding, Managing and Measuring Knowledge, Sage Publications, London The starting 277 About the author Annie Green currently works as a Chief Knowledge Strategist/Architect for Keane Federal Systems She has more that 20 years of systems engineering, business process engineering, requirements engineering, project management and methodology development experience She is adjunct faculty at George Washington University (GWU) and is the Lead Professor of the KnowledgeWare Technologies course She has a BS Degree from Virginia Commonwealth University, MSIS from George Mason University and a DSc from GWU, which has a major concentration in Knowledge Management, with her dissertation focused on Intangible Asset Valuation Her work focuses on building an infrastructure that supports a knowledge architecture that facilitates measuring the value of intangible assets (knowledge) with respect to organizational performance Key to her Knowledge Management Infrastructure Methodology is Communities of Practice (CoPs) Annie Green can be contacted at anegreen@gwuedu To purchase reprints of this article please reprints@emeraldinsightcom Or visit our web site for further details: wwwemeraldinsightcom/reprints