integrating data mining within a strategic knowledge management framework: a platform for...
TRANSCRIPT
Integrating Data Mining within a Strategic Knowledge Management Framework: A Platform for Sustainable Competitive Advantage within the
Australian Minerals and Metals Mining Sector
Sanaz Moayer
Fang Huang
Scott Gardner
School of Management and Governance,
Murdoch University, Perth, Australia
ICBIAKM 2015: The 17th International Conference on Business Intelligence, Analytics, and Knowledge Management
Paris, France
Contents
Introduction Objective of the Paper Definition of Knowledge Management and Its benefits Review of Existing Knowledge Management Models Strategic Application of Data Mining and Its benefits Competitive Advantage Data Mining, Business Intelligence, and Knowledge Management Conclusion
Introduction
-Knowledge is a valuable intangible asset.-Knowledge Management (KM) is a key part in creating Competitive Advantage.-Information Technology (IT) has a strategic support role in exploiting the composite knowledge capacity.-Data Mining (DM) tools add value throughout an organisation’s network within the broader KM framework
Objective of the Paper
To explore the linkages between strategic engagement of human knowledge and powerful technological platforms
Achieve competitive Advantage
Definition of Knowledge Management
KM definition by Jashapara (2011, p.14):
“The effective knowledge processes associated with exploration, exploitation and sharing of human knowledge (tacit and explicit)
that use appropriate technology and cultural environments to enhance an organisation’s intellectual capital and performance”.
Benefits of Knowledge Management
KM benefits (Duvall, 2002):
retention of expertisecapturing and sharing best practicesupporting customers and customer servicesaiding decision makingand increasing profit
Strategic Knowledge Management in Australian Mining Industry
Mining Technology Service (MTS)
A platform for finding, acquiring, developing, sharing, preserving, evaluating, and applying knowledge to support
the competitive advantage
Review of Existing Knowledge Management ModelsModel Features
The von Krogh and Roos Model of organisational Epistemology Individual knowledgeSocial knowledge
The Nonaka and Takeuchi Knowledge Spiral Model
Knowledge creation
Knowledge conversion (Socialisation, externalisation, combination, internalisation), ‘Ba’ safe space, Knowledge assets.
Hedlund and Nonaka’s Knowledge Management Model
Articulated knowledge- Individual
Tacit knowledge- Individual
Articulated knowledge- Group
Tacit knowledge- Group
Articulated knowledge- Organisation
Tacit knowledge- Organisation
Articulated knowledge- Inter- Organisational Domain
Tacit knowledge- Inter- Organisational Domain
The Choo Sense-making KM Model Sense makingKnowledge creationDecision making
The Wiig Model for Building and Using Knowledge Public KnowledgeShared experiencePersonal knowledge
The Boisot knowledge category Model
Propriety knowledgePersonal knowledgePublic knowledgeCommon sense
The Boisot I-Space KM Model Codified- UncodifiedAbstract- ConcreteDiffused- Undiffused
Review of Existing Knowledge Management Models cont.
Model Features
Skandia Intellectual Capital Model of Knowledge Management
EquityHuman Capital
Customer Capital(Customer Base, Relationsips, Potential)
Innovation CapitalProcess Capital
Demerest’s Knowledge Management Model
Knowledge constructionKnowledge embodiment
Knowledge dissemination
Use
Frid’s Knowledge Management Model
Knowledge ChaoticKnowledge AwareKnowledge FocusedKnowledge ManagedKnowledge Centric
Stankosky and Baldanza’s Knowledge Management Framework
LearningLeadership
Organisation, structure & culture
Technology
Kogut and Zander’s Knowledge Management Model
Knowledge CreationKnowledge Transfer
Process & Transformation Of Knowledge
Knowledge capabilities
Individual “Unsocial sociality”
Complex Adaptive System Model of KM
Creating new ideasSolving problemsMaking decisions
Taking actions to achieve desired results
Major Steps of Knowledge Management
Knowledge Creation Knowledge Storage Knowledge Transfer Knowledge Application
Holzner & Marks (1979) Consciousness Extension Transformation Implementation
Pentland (1995) Construction Organising - Storage Distribution Application
Nonaka & Takeuchi (1995) Construction Organising - Storage Distribution Application
Alavi & Leidner (2001) Knowledge Creation Knowledge Storage Knowledge Transfer Knowledge Application
Darroch (2003) Acquisition ----- Dissemination Use knowledge
Chen (2005) Knowledge Creation Knowledge Conversion Knowledge Circulation Knowledge Completion
Lee (2005) Creation Accumulation Sharing Utilisation & Internalisation
Adapted from Alavi & Leidner (2001)
Four Major steps of Knowledge Management
Four Knowledge Management Stages (Alavi & Leidner, 2001):
Knowledge Creation Knowledge Storage knowledge Transfer Knowledge Application
Strategic Application of Data Mining
Extract
Transform
Load
Store and manage
Provide data access Analyse Present
Benefit of Data Mining
Business Level (Bal, Bal and Demirhan, 2011): recognition of services and products which are important to customers profiling of appropriate offerings to meet particular customer needs highlighting areas of current and future customer interests, which in turn leads to new products and services.
Focusing on unstructured problems
Deliver the right products/services to right customers through the right delivery channels
Competitive Advantage
Competitive Advantage approaches:
Positioning school (MBV):Porter’s Five Forces Model
Resource based school (RBV):VRIN Model
Data Mining, Business Intelligence, and Knowledge Management
Achieve Sustainable Competitive Advantage
Knowledge Management
Business Intelligence
Data Mining
Conclusion
Sustainable competitive Advantage
Knowledge management (exploring tacit & explicit knowledge; Organisational learning… )
Data management Business intelligence Data Mining …
References•Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly , 25 (1), 107-139.•Asad, M. (2012). Porter Five Forces vs Resource Based View-A Comparison. Available at SSRN 1986725 .•Bennet, A., & Bennet, D. (2004). Organizational survival in the new world: the inteligent complex adaptive system. A new theoary of the firm. Burlington: MA:Elsevier Butterworth-Heinemann.•Boisot, M. (1998). Knowledge assets: Securing competitive advantage in the information economy. Oxford University Press, USA.•Boisot, M. (1987). Information and Organizations. The Manager as Anthropologist, , Fontana/Collins, London .•Carpenter, M. A., Sanders, W. G., Rice, J. L., & Martin, N. J. (2010). Strategic Management: A dynamic Perspective Concepts and Cases. Pearson.•Chase, R. (1997). The Knowledge based Organization: An International Survey. Journal of Knowledge Management , 1 (1).•Chen, M.-Y., & Chen, A.-P. (2005). Integrating option model and knowledge management performance measures: an empirical study. Journal of Information Science , 31 (5), 381-393.•Choo, C. (1998). The knowing organization. New York: Oxford University Press.•Darroch, J. (2003). Developing a measure of knowledge management behaviors and practices. Journal of Knowledge Managemen , 7 (5), 41-54.•Demerest, M. (1997). Understanding knowledge management. Journal of long range Planing , 30 (3), 374-84.•Duvall, M. (2002). Knowledge Management Vendors Go Vertical. Intranet journal .•Frid, R. (2003). A Common KM Framework For The Government Of Canada: Frid Framework For Enterprise Knowledge management. Canadian Institute of Knowledge Management, Ontario. •Hedlund, G., & Nonaka, I. (1993). Models of Knowledge Management in the West and Japan. Implementing Strategic Process, Change, Learning and , 117-44.•Holzner, B., & Marx, J. (1979). The Knowledge Application: The Knowledge System in Society,Allyn-Bacon, Boston,. •Jambhekar, N. D. (2011). Knowledge Discovery through Data Mining in the improvement of Business and its Operating. Online International Interdisciplinary Research Journal , I (II).•Jashapara, A. (2011). Knowledge management an integrated approach (second ed.). Sydney: Prentice Hall.•Jindal, D., & Bhambri, V. (2011). EMERGING TRENDS IN KNOWLEDGE MANAGEMENT IN BANKING SECTOR. CHIEF PATRON CHIEF PATRON , 1 (10), 93-96.
References cont.
•Kogut, B., & Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3(3), 383-97. , 3 (3), 383-397.•Lee, C., Lee, K. S., & e. a. (2005). KMPI: measuring knowledge management performance. Information & Management , 42 (3), 469-482.•Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press.•Palace, B. (1996). Data Mining: What Is Data Mining? Retrieved from http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm•Pentland, B. T. (1995). Information Systems and Organizational Learning: The Social Epistemology of Organizational Knowledge Systems. Accounting, Management and Information Technologies , 5 (1), 1-21.•Porter, M. E. (2004). Competitive advantage : Creating and Sustaning Superior Performance. Free Press.•Porter, M. E. (1980). Competitve Strategy: thechniques for Analyzing Industries and Competitors. New York: Free Press.•Roos, G., & Roos, J. (1997). Measuring your Company’s Intellectual Performance. Journal of Long Range Planning, , 30 (3), 413-26.•Seddawy, A. B., Khedr, A., & Sultan, T. (2012). Adapted Framework for Data Mining Technique to Improve Decision Support System in an Uncertain Situation. International Journal of Data Mining & Knowledge Management Process (IJDKP) , 2 (3).•Shetty, S., & Achary, K. K. (2008). Audio Data Mining Using Multi-perceptron Artificial Neural Network. IJCSNS International Journal of Computer Science and Network Security , 8 (10).•Stankosky, M., & Baldanza, C. (2001). A systems approach to engineering a KM system. Unpublished manuscript .•Tyagi, S., & Sharma, B. (2011). Data Mining Tools and Techniques to Manage the Textile Quality Control Data for Strategic Decision Making. International Journal of Computer Applications , 13 (4), 26-29.•Von Krogh, G., & Roos, J. (1995). Organizational Epistemology. New York:St.Martin press.•Wiig, K. M. (1993). Knowledge management foundations: thinking about thinking: how people and organizations create, represent, and use knowledge. Arlington, TX: Schema Press.•Yang, T., Gong, Y., & Bai, P. (2008). Spatial Data Mining Features between General Data Mining. 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing. Zhu, X., & Li, J. (2006). An ant colony system-based optimization scheme of data mining. Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) IEEE.
Thank You & Comments
ICBIAKM 2015: 17th International Conference on Business Intelligence, Analytics, and Knowledge Management
Paris, France
July 2015