Transcript Organizational Memory and Knowledge Systems (OMKS): An

Organizational Memory and
Knowledge Systems (OMKS): An
Integrated Approach to Building
Modern Decision Support Systems
Francis K. Andoh-Baidoo
State University of New York at Brockport
Jon Blue
University of Delaware
SIG-DSS Pre-ICIS 2006 Research Workshop
December 10, 2006
Milwaukee, WI
Agenda
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Problem Statement
Theoretical Framework
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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
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Problem Statement
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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
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Theoretical Framework
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Decision Making and Decision Support
Systems (DSS)
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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
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Theoretical Framework (con’t.)
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Data Warehouse
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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
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Theoretical Framework (con’t.)
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Knowledge Management System
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Repository for explicit & tacit knowledge
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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
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Tacit knowledge can be articulated, captured, and
represented (Nonaka, Takeuchi, & Umemoto, 1996;
Polyshyn, 1981)
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Theoretical Framework (con’t.)
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Organizational Memory Information Systems
(OMIS)
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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)
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Theoretical Framework (con’t.)
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Knowledge Spiral (Nonaka & Takeuchi, 1995)
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Proposed Modern DSS Approach
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Scenarios
Ontology
Metadata
Data / Knowledge Repositories
Knowledge Conversion
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Scenarios/Ontology/Metadata
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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)
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Scenarios/Ontology/Metadata (con’t).
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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
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Proposed
Organizational
Memory and
Knowledge
System
Marketing
Sales
Manufacturing
Organization’s
Individuals
ETL + Organizational
Ontology
Human
Resources
Organization’s/External
Databases
Summarized
Data
Knowledge
Repository
Data
Warehouse
Scenarios/ Organizational
Ontology
To Capture
Tacit Knowledge
Knowledge
Ontological
Metadata
Analysis Tools
Admin Tools
Development
Tools
Aggregated
Data
Tools to Access Data
Edit/Query Interface/Browser
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Knowledge Conversion in OMKS
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Externalization (tacit to explicit)
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Scenario based acquisition
Facilitates tacit to explicit knowledge by using
mathematical models (Nemati et al., 2002)
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Stored as explicit mathematical inequalities
Canonical model formulations with links to relational
tables in the DSS
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Knowledge Conversion in OMKS (con’t).
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Socialization (tacit to tacit)
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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)
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Knowledge Conversion in OMKS (con’t).
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Combination (explicit to explicit)
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Explicit knowledge is reconfigured
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Valid knowledge can be used to modify existing
knowledge
AI-based data mining on the output from
brainstorming sessions
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Knowledge Conversion in OMKS (con’t).
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Internalization (explicit to tacit)
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Knowledge workers improve their work activities
through the shared knowledge (modification of
the mental model)
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Implications
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Research
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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
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Implications (con’t.)
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Practice
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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
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Conclusion
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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
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Such an approach has the potential to enhance decision
making and organizational learning
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References
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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. 588590.
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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