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Chapter 8

Chapter 8. The Knowledge Management Platform . Knowledge Processes and Technology Enablers. Objective Technology Enablers Find knowledge Employee skills yellow pages Create new knowledge DSS Tools Package and assembler Customized discussion groups knowledge

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Chapter 8

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  1. Chapter 8 The Knowledge Management Platform

  2. Knowledge Processes and Technology Enablers Objective Technology Enablers • Find knowledge Employee skills yellow pages • Create new knowledge DSS Tools • Package and assembler Customized discussion groups knowledge • Apply knowledge Search, retrieval, and storage tools to help classify both formal and informal knowledge • Reuse and revalidate Customer support knowledge knowledge bases, CoP, past project record database Knowledge Management

  3. Seven Layers of KM Architecture Knowledge Management

  4. KM Platform basic needs • Efficient Protocol : secure and fast sharing of content across locations, mobile clients • Portable operation : OS environment in different departments • Consistent and easy to use client interfaces • Scalability • Legacy integration from mainframe database • Security • Flexibility and customizability : in term of.... “What the user sees and needs to see” The lack of the end user’s ability to filter out the irrelevant content is the root cause of information overload. Knowledge Management

  5. Web or Proprietary Platforms? Knowledge Management

  6. Packaging Knowledge • Packaging :Filtering, editing, searching, and organizing pieces of knowledge. • Knowledge must be packaging in such a way that it’s insightful, relevant, and useful. • Allow the employee the time to Package Knowledge for future use to make content useful • Identification : general domain • Segmenting : target users • Mass Customization : suit each audience • Format : use indexes, groupings, and table of contents • Tests : refine the steps to seek feedback Knowledge Management

  7. Knowledge Delivery • PULL Approach • User Choice : Users want the knowledge • “Go and Get” what users need to know • PUSH Approach • You want the users to have the knowledge • Noticeability • Ease of use : workgroup Knowledge Management

  8. Knowledge Delivery • ALL • Suit for information managementnot KM • Data Slam : meaningless piece of data, difficult to navigate • Some • Useful, contextually applicable pieces • When to deliver the knowledge • JIT“JUST IN TIME” : Better • JIC“JUST IN CASE” : Information not knowledge, people ignore them, lack of action-ability that is not relevant to their immediate work Knowledge Management

  9. Source and Feeds for Marketing KM Knowledge Management

  10. Knowledge Management

  11. Data Warehouse in the KM Infrastructure Knowledge Management

  12. Data Warehouse in the KM Infrastructure Data representing, such as hypercube data model in multiple dimensions, help in supporting decision making with concrete data from the past. A data warehouse is of little use unless the data is converted to meaningful information and applied when needed. Knowledge Management

  13. GA Based Tools in the KM Technology Framework Knowledge Management

  14. GA Based Tools in the KM Technology Framework Genetic Algorithms are based on Charles Darwin’s theory of natural selection. “I don’t know how to build a good solution, but I will know it when I see it” Knowledge Management

  15. Neural Network in the KMS Architecture Knowledge Management

  16. Neural Network in the KMS Architecture A neural network is a networked computing architecture in which a number of processors are interconnected like the neurons in a human brain that can learn through trial and error. NN has their root of biology. Preprocess data, cleaning up data, training data Knowledge Management

  17. Neural Network Knowledge Management

  18. Rule-Based Systems in the KM Infrastructure Knowledge Management

  19. Rule-Based Systems in the KM Infrastructure If.......then........else......... Work well when The rules do not overlap You know the variables in your problem You can express them quantitatively Knowledge Management

  20. Case-Based Reasoning in KM Knowledge Management

  21. Case-Based Reasoning in KM Each attribute is assigned some weight, based on previous experience or existing knowledge. Cases that are closest matches to the case at hand are then retrieved. Knowledge Management

  22. Case-based reasoning Knowledge Management

  23. Applications of Various Intelligence Tools in a KM Platform Knowledge Management

  24. Knowledge Management

  25. A Comparison of Intelligence Tools Knowledge Management

  26. Level of Increasing Granularity in a KM System Representing the System’s Depth of Detail Knowledge Management

  27. Customer Support and Knowledge levels Knowledge Management

  28. Tagging Attributed for Knowledge Content in a KM system Knowledge Management

  29. Form Attributed paper electronic formal (file, word document, spreadsheet) informal (multimedia, sound, video tape) tacit or mental knowledge pointer (to person who has solved a problem of that nature before) Knowledge Management

  30. Type Attributed procedure guideline protocol manual reference worst practice report best practice report note memo failure report success report press report competitive intelligence report Knowledge Management

  31. Product and Service Attributed Strategic consulting Implementation consulting e-commerce consulting Knowledge Management

  32. Systems Commonly Confused with Customer KM Platform Knowledge Management

  33. Application Layer in the KM Platform Knowledge Management

  34. The Application Layer • Pointer to expertise • Electronic yellow pages • Document Management is not the focus of KM initiative. • Convert only those pieces of information that are needed. • Multimedia , Video Clip • A picture is worth a thousand words. • Microsoft NetMeeting Conference Software • 28000 desktop users at 250 sites around the world • No inconsistent documents • Virtual Meeting Knowledge Management

  35. Peer-to-peer networking • peer-to-peer networking support KM because it is closely mirrors face-to-face communication • peer-to-peer networking is defined as sharing resources by direct exchange between individual systems in a digital network • 1. it must rapidly get the right people involved • 2. it enable them collectively to apply their expertise Knowledge Management

  36. Peer-to-peer networkingNetwork of friends and their friends Automatically pass on question, Stop when run out of time one of the peer confirms having matching expertise Knowledge Management

  37. SECI Model and IT support Knowledge Management

  38. Discussion Group Chapter 8 • 1. What is Knowledge in your company? • 2. Tagging attributed for your knowledge Content. (Activity, Domain, Form, Type, Product/Service, Time, Location) p.28-31. • 3. Define what are pull technology and push technology in your KM system. p. 7 Knowledge Management

  39. The End

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