1. 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

2. December 10, 2006Andoh-Baidoo and Blue1 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

3. December 10, 2006Andoh-Baidoo and Blue2 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

4. December 10, 2006Andoh-Baidoo and Blue3 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

5. December 10, 2006Andoh-Baidoo and Blue4 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

6. December 10, 2006Andoh-Baidoo and Blue5 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)

7. December 10, 2006Andoh-Baidoo and Blue6 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)

8. December 10, 2006Andoh-Baidoo and Blue7 Theoretical Framework (con’t.)  Knowledge Spiral (Nonaka & Takeuchi, 1995)

9. December 10, 2006Andoh-Baidoo and Blue8 Proposed Modern DSS Approach  Scenarios  Ontology  Metadata  Data / Knowledge Repositories  Knowledge Conversion

10. December 10, 2006Andoh-Baidoo and Blue9  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

11. December 10, 2006Andoh-Baidoo and Blue10 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

12. December 10, 2006Andoh-Baidoo and Blue11 Organization’s Individuals Admin Tools Development Tools Tools to Access Data Edit/Query Interface/Browser Analysis Tools Proposed Organizational Memory and Knowledge System Data Warehouse Knowledge Repository Ontological Metadata Marketing Manufacturing Sales Human Resources Organization’s/External Databases Knowledge Summarized Data Aggregated Data Scenarios/ Organizational Ontology To Capture Tacit Knowledge Knowledge Conversion Knowledge Source Supports Decision Making ETL + Organizational Ontology

13. December 10, 2006Andoh-Baidoo and Blue12 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

14. December 10, 2006Andoh-Baidoo and Blue13 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)

15. December 10, 2006Andoh-Baidoo and Blue14 Knowledge Conversion in OMKS (con’t).  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

16. December 10, 2006Andoh-Baidoo and Blue15 Knowledge Conversion in OMKS (con’t).  Internalization (explicit to tacit) Knowledge workers improve their work activities through the shared knowledge (modification of the mental model)

17. December 10, 2006Andoh-Baidoo and Blue16 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

18. December 10, 2006Andoh-Baidoo and Blue17 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

19. December 10, 2006Andoh-Baidoo and Blue18 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

20. December 10, 2006Andoh-Baidoo and Blue19 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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