Managing Knowledge in the Digital Firm

1. Chapter 11 Managing Knowledge inthe Digital Firm

2. Objectives • What is knowledge management? Why do businesses today need knowledge management programs and systems for knowledge management? • What types of systems are used for enterprise-wide knowledge management? How do they provide value for organizations? • How do knowledge work systems provide value for firms? What are the major types of knowledge work systems?

3. Objectives • What are the business benefits of using intelligent techniques for knowledge management? • What major management issues and problems are raised by knowledge management systems? How can firms obtain value from their investments in knowledge management systems?

4. Management Challenges • Designing knowledge systems that genuinely enhance organizational performance • Identifying and implementing appropriate organizational applications for artificial intelligence

5. The Knowledge Management Landscape Important Dimensions of Knowledge • Knowledge • Wisdom • Tacit knowledge • Explicit knowledge

6. The Knowledge Management Landscape U.S enterprise knowledge management software revenues, 2001-2006 Figure 11-1

7. The Knowledge Management Landscape Important Dimensions of Knowledge • Knowledge: • Is a firm asset • Has different forms • Has a location • Is situational

8. The Knowledge Management Landscape Organizational Learning and Knowledge Management • Organizational learning:Creation of new standard operating procedures and business processes reflecting experience • Knowledge management:Set of processes developed in an organization to create, gather, store, disseminate, and apply knowledge

9. The Knowledge Management Landscape The knowledge management value chain Figure 11-2

10. The Knowledge Management Landscape The Knowledge Management Value Chain • Knowledge acquisition • Knowledge storage • Knowledge dissemination • Knowledge application

11. The Knowledge Management Landscape The Knowledge Management Value Chain • Chief Knowledge Officer (CKO): Senior executive in charge of the organization's knowledge management program • Communities of Practice (COP): Informal groups who may live or work in different locations but share a common profession

12. Types of Knowledge Management Systems Types of Knowledge Management Systems • Enterprise Knowledge Management Systems: General purpose, integrated, and firm-wide systems to collect, store and disseminate digital content and knowledge • Knowledge Work Systems (KWS): Information systems that aid knowledge workers in the creation and integration of new knowledge in the organization • Intelligent Techniques: Datamining and artificial intelligence technologies used for discovering, codifying, storing, and extending knowledge

13. Types of Knowledge Management Systems Major types of knowledge management systems Figure 11-3

14. Enterprise-Wide Knowledge Management Systems Structured Knowledge Systems • Structured knowledge • Semistructured knowledge • Knowledge repository • Knowledge network

15. Enterprise-Wide Knowledge Management Systems Enterprise-wide knowledge management systems Figure 11-4

16. Enterprise-Wide Knowledge Management Systems KWorld’s knowledge domain Figure 11-5

17. Enterprise-Wide Knowledge Management Systems KPMG knowledge system processes Figure 11-6

18. Enterprise-Wide Knowledge Management Systems Window on Technology DaimlerChrysler Learns to Manage Its Digital Assets • What are the management benefits of using a digital asset management system? • How does ADAM provide value for DaimlerChrysler?

19. Enterprise-Wide Knowledge Management Systems Organizing Knowledge: Taxonomies and Tagging • Taxonomy: Method of classifying things according to a predetermined system • Tagging: Once a knowledge taxonomy is produced, documents are tagged with proper classification

20. Enterprise-Wide Knowledge Management Systems Hummingbird’s integrated knowledge management system Figure 11-7

21. Enterprise-Wide Knowledge Management Systems Knowledge Networks Key Functions of an Enterprise Knowledge Network • Knowledge exchange services • Community of practice support • Auto-Profiling Capabilities • Knowledge management services

22. Enterprise-Wide Knowledge Management Systems The problem of distributed knowledge Figure 11-8

23. Enterprise-Wide Knowledge Management Systems AskMe Enterprise knowledge network system Figure 11-9

24. Enterprise-Wide Knowledge Management Systems Portals, Collaboration Tools, and Learning Management Systems • Teamware: Group collaboration software running on intranets that is customized for teamwork

25. Enterprise-Wide Knowledge Management Systems Portals, Collaboration Tools, and Learning Management Systems • Learning Management Systems (LMS): Tools for the management, delivery, tracking, and assessment of various types of employee learning

26. Enterprise-Wide Knowledge Management Systems Window on Management Managing Employee Learning: New Tools, New Benefits • What are the management benefits of using learning management systems? • How do they provide value to Alyeska and APL

27. Knowledge Work Systems Knowledge Workers and Knowledge Work Knowledge workers perform 3 key roles: • Keeping the organization current in knowledge as it develops in the external world • Serving as integral consultants regarding the areas of their knowledge, the changes taking place, and opportunities • Acting as change agents

28. Knowledge Work Systems Requirements of knowledge work systems Figure 11-10

29. Knowledge Work Systems Examples of Knowledge Work Systems • Computer-aided design (CAD) • Virtual reality systems • Virtual Reality Modeling Language (VRML) • Investment workstations

30. Intelligent Techniques Capturing Knowledge: Expert Systems • Knowledge Base: Model of human knowledge • Rule-based Expert System: Collection in an AI system represented in the the form of IF-THEN

31. Intelligent Techniques Capturing Knowledge: Expert Systems • AI shell: programming environment • Inference Engine: strategy used to search through the rule base • Forward Chaining: strategy for searching the rules base that begins with the information entered by user and searches the rule base to arrive at a conclusion

32. Intelligent Techniques Rules in an AI program Figure 11-11

33. Intelligent Techniques Inference engines in expert systems Figure 11-12

34. Intelligent Techniques Capturing Knowledge: Expert Systems • Backward Chaining:Strategy for searching the rule base in an expert system that acts as a problem solver • Knowledge Engineer:Specialist who elicits information and expertise from other professionals and translates it into set of rules for an expert system

35. Intelligent Techniques Examples of Successful Expert Systems • Galeria Kaufhof • Countrywide Funding Corp.

36. Intelligent Techniques Organizational Intelligence: Case-Based Reasoning • Case-based Reasoning (CBR): Artificial intelligence technology that represents knowledge as a database of cases and solutions

37. Intelligent Techniques How case-based reasoning works Figure 11-13

38. Fuzzy Logic Systems Fuzzy Logic Systems • Rule-based AI • Tolerates imprecision • Uses nonspecific terms called membership functions to solve problems

39. Fuzzy Logic Systems Implementing fuzzy logic rules in hardware Figure 11-14

40. Neural Networks Neural Networks • Hardware or software emulating processing patterns of biological brain • Put intelligence into hardware in form of a generalized capability to learn

41. Neural Networks How a neural network works Figure 11-15

42. Genetic Algorithms Genetic Algorithms • Problem-solving methods • Promote evolution of solutions to specified problems • Use a model of living organisms adapting to their environment

43. Genetic Algorithms The components of a genetic algorithm Figure 11-16

44. Genetic Algorithms Hybrid AI Systems • Integration of multiple AI technologies into a single application • Takes advantage of best features of technologies

45. Intelligent Agents Intelligent Agents • Software program that uses built-in or learned knowledge base to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application

46. Intelligent Agents Intelligent agent technology at work Figure 11-17

47. Management Issues for Knowledge Management Systems Implementation Challenges • Insufficient resources available to structure and update the content in repositories • Poor quality and high variability of content quality because of insufficient mechanisms • Content in repositories lacks context, making documents difficult to understand

48. Management Issues for Knowledge Management Systems Implementation Challenges • Individual employees not rewarded for contributing content, and many fear sharing knowledge with others on the job • Search engines return too much information, reflecting lack of knowledge structure or taxonomy

49. Management Issues for Knowledge Management Systems Implementing knowledge management projects in stages Figure 11-18

50. Obtaining Value from Knowledge Management Systems Obtaining Value from Knowledge Management Systems • Develop in stages • Choose a high-value business process • Choose the right audience • Measure ROI during initial implementation • Use the preliminary ROI to project enterprise-wide values