organizational memory and knowledge systems (omks): an integrated approach to building modern...

Organizational Memory and Knowledge Systems (OMKS): An Integrated Approach to Building Modern Decision Support Systems

Francis K. Andoh-BaidooState University of New York at BrockportJon BlueUniversity of Delaware

SIG-DSS Pre-ICIS 2006 Research WorkshopDecember 10, 2006Milwaukee, WI

December 10, 2006 Andoh-Baidoo and Blue 2

Agenda Problem Statement Theoretical Framework

Decision Making and Decision Support Systems (DSS) Data Warehouse Knowledge Management System Organizational Memory Information System (OMIS) Knowledge Spiral (Nonaka & Takeuchi, 1995)

Proposed Modern Decision Support System Approach - OMKS Knowledge Conversion in OMKS Implications for Research and Practice Conclusions


Problem Statement Researchers have recommended that

organizations eliminate their silo systems by consolidating their data, information, and knowledge repositories to enable effective and efficient decision making. Unfortunately, most organizations have not realized this end

The acquisition, storage, and utilization of tacit knowledge is difficult


Theoretical Framework Decision Making and Decision Support

Systems (DSS) Modern DSS are commissioned to support all

four phase of the decision making process: intelligence, design, choice, and implementation (Simon, 1955)

Data Warehouses, Knowledge Systems, and Organizational Memory Information Systems support decision making


Theoretical Framework (con’t.) Data Warehouse

Defined as “…a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process” (Inmon, p. 1)

Typically, On-Line Analytical Processing (OLAP), Data Mining, and Knowledge Discovery tools are used to support decision making processes


Theoretical Framework (con’t.) Knowledge Management System

Repository for explicit & tacit knowledge Explicit knowledge – systematic and can be expressed

formally as language, rules, objects, symbols, or equations

Tacit knowledge – includes beliefs, perspectives, and mental models ingrained in a person’s mind Tacit knowledge can be articulated, captured, and

represented (Nonaka, Takeuchi, & Umemoto, 1996; Polyshyn, 1981)


Theoretical Framework (con’t.) Organizational Memory Information Systems

(OMIS) Integrated knowledge based IS with culture,

history, business processes, and human memory attributes (Hackbarth, 1998)

Facilitate Organizational Learning: Individual learning, learning through direct communication, and learning using a knowledge repository (Heijst et al., 1997)


Theoretical Framework (con’t.)

Knowledge Spiral (Nonaka & Takeuchi, 1995)


Proposed Modern DSS Approach Scenarios Ontology Metadata Data / Knowledge Repositories Knowledge Conversion


A Scenario is a sequence of hypothetical (but mimicking real) situations encountered by a domain expert, together with the intermediate responses/actions (Yu-N & Abidi, 2000)

Ontology is a common and shared understanding of some domain that is capable of being communicated across people and systems (Benjamins et al., 1998)

Scenarios/Ontology/Metadata


Scenarios/Ontology/Metadata (con’t).

Ontology can be used with Scenarios to standardize the acquisition of tacit knowledge (Yu-N & Abidi, 2000)

Ontology-based metadata represents a common global metadata

Ontology-based metadata addresses the issues of data and semantic heterogeneity


Organization’sIndividuals

Admin ToolsDevelopment

Tools

Tools to Access DataEdit/Query Interface/Browser

Analysis Tools

ProposedOrganizational Memory and Knowledge System

Data

Warehouse

Knowledge

Repository

OntologicalMetadata

Marketing

Manufacturing

Sales

Human

Resources

Organization’s/External Databases

Knowledge

SummarizedData

AggregatedData

Scenarios/ Organizational Ontology

To Capture Tacit Knowledge

Knowledge Conversion

Knowledge Source

Supports Decision Making

ETL + OrganizationalOntology


Knowledge Conversion in OMKS

Externalization (tacit to explicit) Scenario based acquisition Facilitates tacit to explicit knowledge by using

mathematical models (Nemati et al., 2002) Stored as explicit mathematical inequalities Canonical model formulations with links to relational

tables in the DSS


Knowledge Conversion in OMKS (con’t).

Socialization (tacit to tacit) Ontology facilitates the common vocabulary for

knowledge worker communication Storage of digitized films of physical

demonstration for viewing by any organization members (with verbal explanations that explain the process)

Kinematics - individual sited with probes and a system records the movements of the person (Nemati et al., 2002)



Combination (explicit to explicit) Explicit knowledge is reconfigured

Valid knowledge can be used to modify existing knowledge

AI-based data mining on the output from brainstorming sessions



Internalization (explicit to tacit) Knowledge workers improve their work activities

through the shared knowledge (modification of the mental model)


Implications Research

More design science research needed on how to develop modern DSS using the proposed approach

Theory based behavioral research needed on the organizational impact of the proposed approach

Further research needs an integrated team of DSS, OMIS, and Data Warehousing scholars


Implications (con’t.) Practice

Organizations may benefit from the exploration of integrating existing Data Warehousing and Organizational Memory Information System

Organizations using the proposed framework can enhance decision making and organizational learning

Consultants may be called upon to study the problems with integrating systems in the proposed framework


Conclusion Researchers have suggested that integrating

knowledge management and decision support systems can enhance decision making

We have proposed a framework for developing modern DSS that combines functional features of data warehousing and organizational memory information systems

Framework uses scenarios to capture tacit knowledge and ontology for standardization Such an approach has the potential to enhance decision

making and organizational learning


References Benjamins, V.R., Fensel, D., & Perez, A.G. (1998). Knowledge Management through Ontologies.

In Proceedings of the Second International Conference of Practical Aspects of Knowledge Management (PAKM 98), October 29-30.

Hackbarth, G. (1998). The Impact of Organizational Memory on IT Systems, In Proceedings of the Fourth Americase Conference on Information Systems, E. Hoadley and I. Benbasat (eds)., pp. 588-590.

Heijst, G., Spek, R., & Kruizinga, E. (1997). Corporate memories as a tool for knowledge management. Expert Systems With Applications, 13(1), 41–54.

Inmon, W. (1995). What is a Data Warehouse? Prism Tech Topic, Vol.1, No. 1. Nemati, H.R., Steiger, D.M., Iyer, L.S., & Hershel, R.T. (2002). Knowledge warehouse: an

architectural integration of knowledge management, decision support, artificial intelligence and data warehousing, Decision Support Systems, Volume 33, Issue 2, June, 143-161.

Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company, How Japanese companies manage the dynamics of innovation, Oxford University Press, New York.

Nonaka, I., Takeuchi, H., & Umemoto K. (1996). A theory of organizational knowledge creation, International Journal of Technology Management, 11(7/8), 833 – 845.

Polanyi, M. (1966). The Tacit Dimension. Routledge and Kegan Paul, London, UK, 1966. Simon, H.A. (1955). A Behavioral Model of Rational choice. Quarterly Journal of Economics,

Vol. 69, pp. 99-118. Yu-N, C., Abidi, S.S.R. (2000). A Scenarios Mediated Approach for Tacit Knowledge Acquisition

and Crystallisation: Towards Higher Return-On-Knowledge and Experience, In Proceedings of the Third International Conference on Practical Aspects of Knowledge Management (PAKM2000) Basel, Switzerland, 30-31 Oct. 2000.

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