Knowledge Management and Data Mining
Managing Knowledge in organizations
G.O. KAYODE-ADEDEJI
SCHOOL OF ENGINEERING, DESIGN and TECHNOLOGY
UNIVERSITY OF BRADFORD
G.O.Kayode-Adedeji@bradford.ac.uk
2011
[Type the company name]
1/1/2011
Contents Introduction 2 KNOWLEDGE MANAGEMENT VS INFORMATION MANAGEMENT 5 KNOWLEDGE MANAGEMENT CONTROVERSIES 5 POSSIBLE CONSTRAINTS IN THE IMPLEMENTATION OF A KNOWLEDGE MANAGEMENT PROGRAM 6 CASE STUDY ON THE SUCCESSFUL IMPLEMENTATION OF KM: 6 THE EVOLUTION OF KM AT BUCKMAN LABORATORIES. 6 CASE STUDY ON THE FAILURE OF KNOWLEDGE MANAGEMENT 8 ANATOMY OF A FAILED KNOWLEDGE MANAGEMENT INITIATIVE: LESSONS FROM PHARMACORP’S EXPERIENCES 8 BENEFITS OF KNOWLEDGE MANAGEMENT 9 DATA MINING 10 FACTORS INFLUENCING THE GROWING INTEREST IN DATA MINING 10 LIMITATIONS OF DATA MINING 11 HOW DATA MINING WORKS 12 DATA MINING TECHNIQUES 13 ADVANTAGES OF DATA MINING 14 DATA MINING ISSUES 14 CONCLUSION 15 REFERENCES 15
SECTION 1
Introduction
We are in the information age and as the demand for information and knowledge increases so did the need to access, process and disseminate knowledge and information effectively increases. This has led to the development of a process called Knowledge management. It is a high trending topic in industries and academic establishments globally similar to the buzz created by cloud computing in the information technology world. There is an array of definitions for knowledge management which is because of its dynamic nature. According to (Charles & Chauvel, 1999) knowledge management is clearly on a slippery slope of being intuitively important yet intellectually elusive: * Important because, “With rare exceptions, the productivity of a modern corporation or nation lies more in its intellectual and system capabilities than in its hard assets...” (Quinn et al.,1996) as cited by (Charles & Chauvel, 1999) * Elusive because, “To define knowledge in a non-abstract and non-sweeping way seems to be very
G.O. KAYODE-ADEDEJI
SCHOOL OF ENGINEERING, DESIGN and TECHNOLOGY
UNIVERSITY OF BRADFORD
G.O.Kayode-Adedeji@bradford.ac.uk
2011
[Type the company name]
1/1/2011
Contents Introduction 2 KNOWLEDGE MANAGEMENT VS INFORMATION MANAGEMENT 5 KNOWLEDGE MANAGEMENT CONTROVERSIES 5 POSSIBLE CONSTRAINTS IN THE IMPLEMENTATION OF A KNOWLEDGE MANAGEMENT PROGRAM 6 CASE STUDY ON THE SUCCESSFUL IMPLEMENTATION OF KM: 6 THE EVOLUTION OF KM AT BUCKMAN LABORATORIES. 6 CASE STUDY ON THE FAILURE OF KNOWLEDGE MANAGEMENT 8 ANATOMY OF A FAILED KNOWLEDGE MANAGEMENT INITIATIVE: LESSONS FROM PHARMACORP’S EXPERIENCES 8 BENEFITS OF KNOWLEDGE MANAGEMENT 9 DATA MINING 10 FACTORS INFLUENCING THE GROWING INTEREST IN DATA MINING 10 LIMITATIONS OF DATA MINING 11 HOW DATA MINING WORKS 12 DATA MINING TECHNIQUES 13 ADVANTAGES OF DATA MINING 14 DATA MINING ISSUES 14 CONCLUSION 15 REFERENCES 15
SECTION 1
Introduction
We are in the information age and as the demand for information and knowledge increases so did the need to access, process and disseminate knowledge and information effectively increases. This has led to the development of a process called Knowledge management. It is a high trending topic in industries and academic establishments globally similar to the buzz created by cloud computing in the information technology world. There is an array of definitions for knowledge management which is because of its dynamic nature. According to (Charles & Chauvel, 1999) knowledge management is clearly on a slippery slope of being intuitively important yet intellectually elusive: * Important because, “With rare exceptions, the productivity of a modern corporation or nation lies more in its intellectual and system capabilities than in its hard assets...” (Quinn et al.,1996) as cited by (Charles & Chauvel, 1999) * Elusive because, “To define knowledge in a non-abstract and non-sweeping way seems to be very
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